# llm_cache

draft_name: _novelty_score_
prompt_hash: ba2676f36958443a
model: claude-haiku-4-5-20251001
created_at: 2026-03-06T21:16:19.517347+00:00
request_json:
  Rate each idea's novelty/originality on a 1-5 scale.

  1 = Generic building block anyone would include (e.g. "Agent Gateway", "Certificate Authority")
  2 = Obvious extension of existing work, minimal originality
  3 = Useful and relevant but expected given the problem space
  4 = Interesting contribution with some original thinking
  5 = Genuinely novel mechanism, protocol, or architectural insight

  Ideas to score:

  ---
  ID: 3255
  Draft: Use Cases and Requirements of Massive Data Transmission(MDT) in High Bandwidth-delay Product (BDP) Network
  Idea: MDT framework for nighttime bandwidth utilization
  Description: An architecture enabling efficient use of idle network bandwidth during off-peak hours to deliver large data transfers with guaranteed completion times.

  ---
  ID: 3256
  Draft: Privacy Preserving Verifiable Geofencing with Residency Proofs for Sovereign Workloads
  Idea: Hardware-rooted cryptographic geofencing for workload identity
  Description: A high-assurance profile that replaces IP-based location verification with cryptographically verifiable geofencing using hardware-rooted attestation to prevent unauthorized location spoofing.

  ---
  ID: 3257
  Draft: EDHOC PSK authentication
  Idea: Pre-Shared Key authentication for EDHOC
  Description: A PSK-based authentication method for the EDHOC key exchange protocol with defined message flows and security considerations.

  ---
  ID: 3258
  Draft: Requirements for Provider Edge in IPv6-only Underlay Networks
  Idea: Provider Edge requirements for IPv6-only networks with IPv4aaS
  Description: Functional and operational requirements for PE devices to deliver IPv4-as-a-Service in multi-domain networks with IPv6-only underlay.

  ---
  ID: 3259
  Draft: A Vocabulary for Controlling Usage of Content Collected by Search and AI Crawlers
  Idea: Standardized vocabulary for content usage preferences by AI/search crawlers
  Description: A structured vocabulary format allowing content creators to declare permissions and restrictions on how their digital content can be used by search engines and AI systems.

  ---
  ID: 3260
  Draft: Certificate Management Messages over CMS (CMC): Compliance Requirements
  Idea: CMC Compliance Requirements
  Description: Establishes a set of compliance statements and requirements for implementing the CMC (Certificate Management over CMS) enrollment protocol.

  ---
  ID: 3261
  Draft: Mangrove: A Unified Framework for Internet Topology, Routing Abstraction, and Modeling
  Idea: Unified Internet Topology and Routing Model
  Description: A system that aggregates diverse data sources (traceroute, BGP tables, IP allocations, broadband measurements) into a comprehensive, unified representation of Internet topology and routing behavior for global visibility.

  ---
  ID: 3262
  Draft: Gap Analysis for the Cross-device Communication Protocol for AI Agents in Network Devices
  Idea: Cross-device Communication Protocol Gap Analysis for Network AI Agents
  Description: Analyzes the applicability and gaps of existing networking and AI protocols for enabling communication and collaboration between AI agents deployed on different network devices.

  ---
  ID: 3263
  Draft: Task discovery in agentic networks
  Idea: Task Discovery Architecture for Agentic Networks
  Description: Defines a multi-layer architectural framework enabling task owners to publish structured task cards to a platform where autonomous agents discover tasks, negotiate execution terms, and coordinate multi-agent collaboration.

  ---
  ID: 3264
  Draft: Model Context Protocol over Media over QUIC Transport
  Idea: Model Context Protocol over MOQT
  Description: Defines the transport mapping of the Model Context Protocol (MCP) onto Media over QUIC Transport (MOQT) for efficient, low-latency communication between language model applications and external data sources.

  ---
  ID: 3265
  Draft: SSH Agent Protocol
  Idea: SSH Agent Protocol
  Description: A protocol specification for key agents to manage cryptographic keys and perform signature operations on behalf of SSH clients.

  ---
  ID: 3266
  Draft: The Use of URIs as Meta-Syntax for Core Mail List Commands and their Transport through Message Header Fields
  Idea: Mailing List Command Header Fields
  Description: Structured email header fields (List-Help, List-Subscribe, List-Unsubscribe, List-Post, List-Owner, List-Archive) containing URIs to enable mail user agents to present mailing list commands.

  ---
  ID: 3267
  Draft: List-Id: A Structured Field and Namespace for the Identification of Mailing Lists
  Idea: List-Id Header Field and Namespace
  Description: A standard header field and FQDN-based namespace to uniquely identify mailing lists independent of hosting infrastructure.

  ---
  ID: 3268
  Draft: AI Agent Discovery (AID) Problem Statement
  Idea: AI Agent Discovery Requirements
  Description: Requirements and considerations for mechanisms enabling AI agents to discover and identify other agents in distributed deployments.

  ---
  ID: 3269
  Draft: Recursively Setting Attributes of Subdirectories and files
  Idea: Recursive Attribute Setting for NFSv4
  Description: A mechanism to set attributes recursively across all files and subdirectories in a parent directory, enabling near-data computing optimizations.

  ---
  ID: 3270
  Draft: MOQ Transport for Agent Protocols
  Idea: MOQT Protocol Abstraction for Agent Communication
  Description: A protocol abstraction layer that maps request-response and streaming patterns from diverse agent protocols onto MOQT's publish/subscribe model to enable unified inter-agent communication.

  ---
  ID: 3271
  Draft: MOQ Transport for Agent Protocols
  Idea: MOQT Binding for A2A and MCP Protocols
  Description: Concrete mappings demonstrating how agent discovery, task delegation, tool access, and resource sharing protocols can be implemented over MOQT transport.

  ---
  ID: 3272
  Draft: Simple Certificate Enrollment Protocol
  Idea: Simple Certificate Enrollment Protocol
  Description: A PKI communication protocol that leverages PKCS#7 and PKCS#10 over HTTP to enable simple certificate enrollment without complex infrastructure.

  ---
  ID: 3273
  Draft: Use of the DH-Based KEM (DHKEM) in the Cryptographic Message Syntax (CMS)
  Idea: DHKEM in CMS
  Description: A mechanism to wrap Ephemeral-Static and Elliptic Curve Diffie-Hellman as KEM interfaces within Cryptographic Message Syntax for unified cryptographic protocol design.

  ---
  ID: 3274
  Draft: PIE: A Lightweight Control Scheme To Address the Bufferbloat Problem
  Idea: PIE Queue Management Scheme
  Description: A lightweight active queue management algorithm using proportional-integral control to regulate buffer occupancy and maintain target latency without per-flow state.


  Return ONLY a JSON object mapping idea ID to score, like {"123": 3, "456": 1, ...}.
  No fences, no explanation.

response_json:
  {
    "3255": 2,
    "3256": 4,
    "3257": 2,
    "3258": 1,
    "3259": 3,
    "3260": 1,
    "3261": 4,
    "3262": 3,
    "3263": 3,
    "3264": 2,
    "3265": 1,
    "3266": 1,
    "3267": 1,
    "3268": 2,
    "3269": 2,
    "3270": 3,
    "3271": 2,
    "3272": 1,
    "3273": 2,
    "3274": 4
  }


draft_name: _novelty_score_
prompt_hash: 8c255a5f71136d64
model: claude-haiku-4-5-20251001
created_at: 2026-03-06T21:16:20.953056+00:00
request_json:
  Rate each idea's novelty/originality on a 1-5 scale.

  1 = Generic building block anyone would include (e.g. "Agent Gateway", "Certificate Authority")
  2 = Obvious extension of existing work, minimal originality
  3 = Useful and relevant but expected given the problem space
  4 = Interesting contribution with some original thinking
  5 = Genuinely novel mechanism, protocol, or architectural insight

  Ideas to score:

  ---
  ID: 3275
  Draft: PIE: A Lightweight Control Scheme To Address the Bufferbloat Problem
  Idea: PIE Queue Management Scheme
  Description: A lightweight active queue management algorithm using proportional-integral control to regulate buffer occupancy and maintain target latency without per-flow state.

  ---
  ID: 3276
  Draft: Post-Quantum EDHOC - Initiator and Responder using signature and/or KEM
  Idea: Post-Quantum EDHOC with Mixed Authentication
  Description: A quantum-resistant variant of EDHOC protocol using signature-based authentication for initiators and KEM-based authentication for responders to reduce message complexity.

  ---
  ID: 3277
  Draft: AAuth - Agentic Authorization OAuth 2.1 Extension
  Idea: Agent Authorization Grant for OAuth 2.1
  Description: An OAuth 2.1 extension enabling confidential AI agents with long-lived identities to request user consent and obtain access tokens through polling, SSE, or WebSocket with natural-language scoped descriptions.

  ---
  ID: 3278
  Draft: An Identitifier Proof-of-Possession Mechanism
  Idea: Identifier Proof-of-Possession Mechanism
  Description: A mechanism for third-party services to cryptographically prove and attest the association between a communications platform user identity and their telephone number or other Service Independent Identifiers.

  ---
  ID: 3279
  Draft: SONAR: Statistical Observation Network for Attestation and Reach
  Idea: SONAR: Verifiable Multicast Delivery with Cryptoeconomic Accountability
  Description: A protocol combining IP multicast efficiency, VRF-based sampling, blockchain attestations, and ALTA authentication to enable verifiable multicast delivery claims without trusted intermediaries.

  ---
  ID: 3280
  Draft: Reilly Sentinel Protocol (RSP): Blockchain-Anchored Integrity for AI Datasets, Training, Fine-Tuning, and Inference Provenance
  Idea: Sentinel Evidence Package (SEP)
  Description: A dual-layer tamper-evident package binding payload digests, provenance metadata, signatures, blockchain timestamp proofs, and resolvable identifiers to create independently verifiable receipts across the AI lifecycle.

  ---
  ID: 3281
  Draft: Reilly Sentinel Protocol (RSP): Blockchain-Anchored Integrity for AI Datasets, Training, Fine-Tuning, and Inference Provenance
  Idea: Blockchain-Anchored Integrity for AI Provenance
  Description: Method for establishing integrity and auditability of AI datasets, training jobs, and inference outputs through blockchain timestamp proofs integrated with cryptographic signatures.

  ---
  ID: 3282
  Draft: Vocabulary For Expressing Content Signals
  Idea: Content Signals Vocabulary for Automated Processing
  Description: Three-category taxonomy enabling parties to express preferences about how digital assets are used by automated processing systems.

  ---
  ID: 3283
  Draft: Framework, Use Cases and Requirements for AI Agent Protocols
  Idea: AI Agent Communication Framework
  Description: Framework identifying protocols, use cases, and requirements for AI agents to communicate with users and interact with Internet resources including APIs and other AI agents.

  ---
  ID: 3284
  Draft: SRv6 behavior extention for Flow Control in WAN
  Idea: SRv6 Flow Control Extension for WAN
  Description: Extension of SRv6 to enable flow control and congestion notification capabilities in wide area networks supporting RDMA-based distributed AI workloads.

  ---
  ID: 3285
  Draft: ECDP: Elliptic Curve Data Protocol
  Idea: Elliptic Curve Data Protocol (ECDP)
  Description: Peer-to-peer protocol for decentralized networks using elliptic curve cryptography for encryption, ECDSA for authentication, and ECDH for key exchange over TCP/UDP.

  ---
  ID: 3286
  Draft: Trust Anchor Hints in Ephemeral Diffie-Hellman Over COSE (EDHOC)
  Idea: Trust Anchor Hints Transport in EDHOC
  Description: A format for encoding and transporting hints about trusted third-party trust anchors during EDHOC key exchange to enable efficient trust anchor discovery.

  ---
  ID: 3287
  Draft: KVCache over MoQT
  Idea: KVCache Distribution over MoQT
  Description: A protocol specification for transmitting and caching LLM key-value cache artifacts over publish-subscribe transport to enable local reuse and reduce inference prefill computation.

  ---
  ID: 3288
  Draft: Vocabulary For Expressing AI Substitutive Usage
  Idea: AI Substitutive Use Preference Category
  Description: A vocabulary extension for expressing user preferences about whether digital assets can be used to generate AI-substitutive outputs during inference-time processing.

  ---
  ID: 3289
  Draft: Remote attestation over EDHOC
  Idea: Remote Attestation in EDHOC
  Description: Integration of the RATS remote attestation architecture into EDHOC to enable device attestation as part of the lightweight key exchange flow.

  ---
  ID: 3290
  Draft: Distributed Inference Network (DIN) Problem Statement, Use Cases, and Requirements
  Idea: Distributed Inference Network Architecture
  Description: A problem statement and requirements framework for distributed edge-cloud AI inference networks addressing latency, scalability, security, and resource efficiency.

  ---
  ID: 3291
  Draft: Consistency-Aware Multipath Transport (CAMP) toward Interactive Multimodal LLM-Based Systems
  Idea: Consistency-Aware Multipath Transport
  Description: A multipath transport protocol that manages heterogeneous modalities (video, audio, text) across separate streams while maintaining inter-modal arrival consistency for low-latency interactive LLM systems.

  ---
  ID: 3292
  Draft: Consistency-Aware Multipath Transport (CAMP) toward Interactive Multimodal LLM-Based Systems
  Idea: Differentiated Multimodal Stream Encapsulation
  Description: A mechanism to enable per-modality stream management and scheduling policies within multipath transport to handle heterogeneous QoS requirements.

  ---
  ID: 3293
  Draft: Unified Optical Networks and AI Computing Orchestration (UONACO) Problem Statement, Use Cases and Requirements
  Idea: Unified Optical Network and AI Compute Orchestration
  Description: A coordinated control plane architecture that integrates optical transport network awareness with AI compute scheduler visibility to optimize distributed training and inference workloads.

  ---
  ID: 3294
  Draft: Automatic Extended Route Optimization (AERO)
  Idea: Automatic Extended Route Optimization over OMNI Interfaces
  Description: An IPv6-based routing and mobility service using Neighbor Discovery over virtual overlay multilink interfaces to support secure path selection, multinet traversal, and mobility management across heterogeneous network types.


  Return ONLY a JSON object mapping idea ID to score, like {"123": 3, "456": 1, ...}.
  No fences, no explanation.

response_json:
  {
    "3275": 4,
    "3276": 3,
    "3277": 4,
    "3278": 3,
    "3279": 4,
    "3280": 4,
    "3281": 3,
    "3282": 2,
    "3283": 1,
    "3284": 3,
    "3285": 1,
    "3286": 3,
    "3287": 4,
    "3288": 2,
    "3289": 3,
    "3290": 1,
    "3291": 4,
    "3292": 3,
    "3293": 2,
    "3294": 3
  }


draft_name: _novelty_score_
prompt_hash: f00672496ca5ca0d
model: claude-haiku-4-5-20251001
created_at: 2026-03-06T21:16:22.589572+00:00
request_json:
  Rate each idea's novelty/originality on a 1-5 scale.

  1 = Generic building block anyone would include (e.g. "Agent Gateway", "Certificate Authority")
  2 = Obvious extension of existing work, minimal originality
  3 = Useful and relevant but expected given the problem space
  4 = Interesting contribution with some original thinking
  5 = Genuinely novel mechanism, protocol, or architectural insight

  Ideas to score:

  ---
  ID: 3295
  Draft: Automatic Extended Route Optimization (AERO)
  Idea: Automatic Extended Route Optimization over OMNI Interfaces
  Description: An IPv6-based routing and mobility service using Neighbor Discovery with unique-local addressing over virtual overlay multilink interfaces to support secure multilink path selection and mobility across diverse network topologies.

  ---
  ID: 3296
  Draft: Automatic Extended Route Optimization (AERO)
  Idea: Automatic Extended Route Optimization over OMNI Interfaces
  Description: An IPv6-based routing and mobility service using Neighbor Discovery over virtual overlay multilink interfaces with support for both Provider-Aggregated and Provider-Independent addressing schemes.

  ---
  ID: 3297
  Draft: MANET Internetworking: Problem Statement and Gap Analysis
  Idea: MANET Internetworking Problem Statement
  Description: Identifies gaps and challenges in connecting autonomous mobile ad-hoc networks to fixed infrastructure like the Internet.

  ---
  ID: 3298
  Draft: Short Usage Preference Strings for Automated Processing
  Idea: Short Usage Preference Strings Format
  Description: Defines a simple machine-readable format for content creators to signal preferences about automated consumption of their content.

  ---
  ID: 3299
  Draft: Short Usage Preference Strings for Automated Processing
  Idea: HTTP Header Conveyance for Usage Preferences
  Description: Specifies an HTTP header field mechanism to carry usage preference signals.

  ---
  ID: 3300
  Draft: Coordinating the Use of Application Profiles for Ephemeral Diffie-Hellman Over COSE (EDHOC)
  Idea: EDHOC Application Profile Coordination
  Description: Defines mechanisms for discovering and coordinating the use of application profiles that specify EDHOC parameters and processing rules.

  ---
  ID: 3301
  Draft: Coordinating the Use of Application Profiles for Ephemeral Diffie-Hellman Over COSE (EDHOC)
  Idea: CBOR-based EDHOC Profile Representation
  Description: Establishes a canonical CBOR format for describing, distributing, and storing EDHOC application profiles.

  ---
  ID: 3302
  Draft: Coordinating the Use of Application Profiles for Ephemeral Diffie-Hellman Over COSE (EDHOC)
  Idea: Well-known EDHOC Application Profiles
  Description: Defines a set of standardized application profiles for common EDHOC use cases.

  ---
  ID: 3303
  Draft: Domain Boundaries 2.0 Problem Statement
  Idea: DNS-based Administrative Boundary Verification
  Description: Addresses the problem that organizational boundaries cannot be reliably confirmed through DNS, hindering client security inferences.

  ---
  ID: 3304
  Draft: Network Traffic Analysis and Network Modal Mapping Method
  Idea: LLM-based Traffic Classification and Resource Mapping
  Description: Framework using large language models to automatically extract multi-dimensional traffic features from pcap data and dynamically map traffic patterns to computing-storage-transmission resource allocation strategies.

  ---
  ID: 3305
  Draft: Nonce-based Freshness for Remote Attestation in Certificate Signing Requests (CSRs) for the Certification Management Protocol (CMP) and for Enrollment over Secure Transport (EST)
  Idea: Nonce Provisioning for Attestation Evidence Freshness in CSRs
  Description: Mechanism for RA/CA to issue nonces to end entities for inclusion in attestation Evidence within Certificate Signing Requests to demonstrate freshness in CMP and EST protocols.

  ---
  ID: 3306
  Draft: Attested Inference Receipt (AIR): A COSE/CWT Profile for Confidential AI Inference
  Idea: Attested Inference Receipt (AIR) Profile
  Description: COSE_Sign1 envelope carrying CWT claims profiled per EAT framework that binds model identity, input/output hashes, and platform attestation metadata for auditable confidential AI inference events.

  ---
  ID: 3307
  Draft: A Blockchain Trusted Protocol for Intelligent Communication Network
  Idea: Blockchain-based Trusted Protocol for 6G Networks
  Description: Blockchain-based protocol framework designed to establish trust in sixth-generation intelligent communication networks.

  ---
  ID: 3308
  Draft: Positioning of PoA
  Idea: Power of Attorney (PoA) Authorization Framework
  Description: Decentralized subgranting authorization technique enabling a principal to delegate limited capabilities to an agent for constrained time and context via self-contained signed documents.

  ---
  ID: 3309
  Draft: Vocabulary for Expressing Content Preferences for AI Training
  Idea: AI Training Content Preference Vocabulary
  Description: A standardized vocabulary enabling rightsholders to express granular preferences about AI training use of their content through metadata and content-delivery protocols.

  ---
  ID: 3310
  Draft: Mailing List Manager (MLM) Transformations
  Idea: Author-Centric DMARC MLM From: Header Restoration
  Description: A mechanism to restore original From: headers rewritten by mailing list managers through author-side signing measures, enabling DMARC compliance without header rewriting.

  ---
  ID: 3311
  Draft: An Open, Decentralized, and Scalable Framework for Large Language Model Inference
  Idea: Decentralized LLM Inference Coordination Framework
  Description: An open framework for executing LLM inference across mutually untrusted participants with network and transport mechanisms supporting sequential dependencies, large intermediate activations, and tight latency requirements.

  ---
  ID: 3312
  Draft: An Open, Decentralized, and Scalable Framework for Large Language Model Inference
  Idea: Decentralized LLM Inference Coordination Framework
  Description: An open framework for executing LLM inference across mutually untrusted participants with network and transport mechanisms supporting sequential dependencies, large intermediate activations, and tight latency requirements.

  ---
  ID: 3313
  Draft: Using LISP as a Network Substrate for AI Agent Communication
  Idea: LISP for AI Agent Networking
  Description: Extend LISP protocol with context-aware mapping and agent-centric communication primitives to provide location transparency, mobility, and logical isolation for distributed AI systems across cloud-edge-endpoint environments.

  ---
  ID: 3314
  Draft: Hierarchical Topology for Language Model Coordination
  Idea: YANG Model for Hierarchical LM Coordination
  Description: Define a YANG data model and reference architecture for orchestrating cooperative inference across heterogeneous language models of varying sizes using secure messaging, request escalation, token-based authorization, and pluggable trust models.


  Return ONLY a JSON object mapping idea ID to score, like {"123": 3, "456": 1, ...}.
  No fences, no explanation.

response_json:
  {
    "3295": 3,
    "3296": 2,
    "3297": 2,
    "3298": 3,
    "3299": 2,
    "3300": 3,
    "3301": 2,
    "3302": 1,
    "3303": 3,
    "3304": 4,
    "3305": 3,
    "3306": 4,
    "3307": 2,
    "3308": 4,
    "3309": 3,
    "3310": 4,
    "3311": 4,
    "3312": 4,
    "3313": 4,
    "3314": 3
  }


draft_name: _novelty_score_
prompt_hash: c8617e4a6a7da144
model: claude-haiku-4-5-20251001
created_at: 2026-03-06T21:16:24.092155+00:00
request_json:
  Rate each idea's novelty/originality on a 1-5 scale.

  1 = Generic building block anyone would include (e.g. "Agent Gateway", "Certificate Authority")
  2 = Obvious extension of existing work, minimal originality
  3 = Useful and relevant but expected given the problem space
  4 = Interesting contribution with some original thinking
  5 = Genuinely novel mechanism, protocol, or architectural insight

  Ideas to score:

  ---
  ID: 3315
  Draft: BGP AS_PATH Validation State Extended Community
  Idea: BGP Extended Community for ASPA Validation State
  Description: Define a BGP opaque extended community to signal AS_PATH validation results based on ASPA within IBGP, enabling policy-driven route decision-making based on upstream provider authorization validation.

  ---
  ID: 3316
  Draft: Architecture for Service Flow Characteristics and Modal Mapping Based on SDN and ALTO Protocol
  Idea: ALTO-based Service Flow to Network Resource Mapping
  Description: Framework using ALTO protocol to map service flow characteristics to network modal resources in SDN-based multi-modal computing networks with dynamic optimization.

  ---
  ID: 3317
  Draft: Network Service Type-Aware Traffic Labeling Protocol (NST-TLP)
  Idea: Network Service Type-Aware Traffic Labeling Protocol
  Description: Protocol mechanism for embedding standardized service type identifiers into packet headers (IPv4/IPv6, MPLS, Ethernet) to enable intelligent traffic recognition and policy-based forwarding.

  ---
  ID: 3318
  Draft: Fully Adaptive Routing Ethernet in Scale-Up Networks
  Idea: Fully Adaptive Routing Ethernet for AI Scale-Up Networks
  Description: Extension of WECMP load-balancing to support ultra-high-throughput, ultra-low-latency routing for MoE-based LLM inference across hundreds of GPUs in scale-up networks.

  ---
  ID: 3319
  Draft: Structural Vulnerabilities in ASRank under Adversarial Conditions
  Idea: ASRank Structural Vulnerability Analysis
  Description: Security analysis identifying structural vulnerabilities in the ASRank algorithm for inferring AS business relationships, demonstrating manipulation risks under adversarial conditions.

  ---
  ID: 3320
  Draft: BRSKI-CLE: A Certificateless Enrollment framework in BRSKI
  Idea: Certificateless BRSKI Enrollment for Constrained IoT
  Description: BRSKI enrollment framework using Key Encapsulation Mechanism instead of certificates for Class 1 constrained devices with quantum-safe algorithm support and lightweight credential issuance.

  ---
  ID: 3321
  Draft: ALTO Multi-Domain Use Cases and Services
  Idea: Multi-Domain ALTO Service Discovery
  Description: Framework for ALTO clients and servers to discover and communicate across multiple autonomous network domains with different trust boundaries.

  ---
  ID: 3322
  Draft: ALTO Multi-Domain Use Cases and Services
  Idea: Cross-Domain Path Optimization
  Description: Mechanism to optimize application traffic paths that span multiple autonomous networks using ALTO information from multiple domains.

  ---
  ID: 3323
  Draft: Coupling ECN Marking Thresholds with Dynamic Buffer Allocation
  Idea: Dynamic ECN Threshold Coupling
  Description: Algorithm to dynamically adjust ECN marking thresholds in real-time based on instantaneous buffer allocation to prevent premature or late congestion marking.

  ---
  ID: 3324
  Draft: Applicability of MCP for the Network Management
  Idea: MCP-Based Network Management Plane
  Description: Architecture for applying Model Context Protocol to expose network management operations, capabilities, and AI integration through standardized MCP interfaces.

  ---
  ID: 3325
  Draft: Applicability of MCP for the Network Management
  Idea: MCP Server Discovery for Network Elements
  Description: Mechanism for automatic discovery and communication establishment between network elements and MCP servers in IP networks.

  ---
  ID: 3326
  Draft: Intelligent Routing Method of SR Policy
  Idea: Quality-Based SR Policy Path Selection
  Description: Intelligent routing method that selects among SR Policy candidate paths based on network quality metrics in MPLS environments.

  ---
  ID: 3327
  Draft: Intelligent Routing Method of SR Policy
  Idea: Quality-Based SR Policy Path Selection for SRv6
  Description: Intelligent routing method that selects among SR Policy candidate paths based on network quality metrics in IPv6 Segment Routing environments.

  ---
  ID: 3328
  Draft: Collective Communication Optimizations: Requirement and Analysis
  Idea: Network-Assisted Collective Communication Optimization
  Description: Enable efficient One-to-Many, Many-to-One, and Many-to-Many collective operations in AI clusters by offloading collective acceleration to the network while maintaining RDMA compatibility.

  ---
  ID: 3329
  Draft: Capabilities and Future Requirements of IPv6 for the Internet of Agents (IoA)
  Idea: IPv6 Requirements for Internet of Agents
  Description: Define foundational capabilities and evolutionary requirements that IPv6 must provide to support hundreds of billions of intelligent agents at scale.

  ---
  ID: 3330
  Draft: MCP-based Network Measurement Framework: Using Model Context Protocol for Intelligent Network Measurement
  Idea: MCP-based Network Measurement Framework
  Description: Enable AI-assisted, natural language-driven network measurement and fault diagnosis by implementing network devices as MCP servers and controllers as MCP clients.

  ---
  ID: 3331
  Draft: Model Context Protocol (MCP) Extensions for Network Equipment Management
  Idea: MCP Extensions for Network Equipment Management
  Description: Define minimal extensions to Model Context Protocol allowing network equipment to act as MCP servers with new capability tokens, tools, resources, and prompts for AI-driven network management.

  ---
  ID: 3332
  Draft: Use Cases and Requirements of Communication Protocol for Measurement Agents on Network Devices
  Idea: Measurement Agent Communication Protocol
  Description: Define communication protocol requirements and use cases for autonomous measurement agents deployed on network devices.

  ---
  ID: 3333
  Draft: Use Cases and Requirements of Communication Protocol for Troubleshooting Agents on Network Devices
  Idea: Troubleshooting Agent Communication Protocol
  Description: Establish communication protocol requirements and use cases for autonomous troubleshooting agents operating on network devices.

  ---
  ID: 3334
  Draft: Requirements and Gap Analysis of Multicast in AI Data Centers
  Idea: Multicast Optimization for AI Data Centers
  Description: Identify multicast requirements and capability gaps for efficient point-to-multipoint data transmission in large language model training and inference workloads.


  Return ONLY a JSON object mapping idea ID to score, like {"123": 3, "456": 1, ...}.
  No fences, no explanation.

response_json:
  {
    "3315": 3,
    "3316": 3,
    "3317": 3,
    "3318": 4,
    "3319": 4,
    "3320": 3,
    "3321": 2,
    "3322": 3,
    "3323": 3,
    "3324": 2,
    "3325": 1,
    "3326": 3,
    "3327": 3,
    "3328": 4,
    "3329": 3,
    "3330": 2,
    "3331": 2,
    "3332": 2,
    "3333": 2,
    "3334": 3
  }


draft_name: _novelty_score_
prompt_hash: a9c334a9122805c6
model: claude-haiku-4-5-20251001
response_json: {"3335": 3, "3336": 2, "3337": 3, "3338": 4, "3339": 4, "3340": 4, "3341": 3, "3342": 2}
created_at: 2026-03-06T21:16:25.663718+00:00
request_json:
  Rate each idea's novelty/originality on a 1-5 scale.

  1 = Generic building block anyone would include (e.g. "Agent Gateway", "Certificate Authority")
  2 = Obvious extension of existing work, minimal originality
  3 = Useful and relevant but expected given the problem space
  4 = Interesting contribution with some original thinking
  5 = Genuinely novel mechanism, protocol, or architectural insight

  Ideas to score:

  ---
  ID: 3335
  Draft: Automatic Network Congestion Relief
  Idea: Intelligent Automatic Congestion Relief Mechanism
  Description: Enable self-healing network responses to fiber optic failures through intelligent traffic analysis and dynamic regulation.

  ---
  ID: 3336
  Draft: Enhanced Use Cases for Scaling Deterministic Networks
  Idea: Deterministic Network Scaling Requirements
  Description: Define use cases and network requirements for scaling deterministic networking to industrial IoT, high-quality video, intelligent computing, and ISAC-enabled factories.

  ---
  ID: 3337
  Draft: AI Agent Architecture for Network Digital Twin
  Idea: AI Agent Architecture for Network Digital Twin
  Description: An architecture integrating AI agents with network digital twin technology to enable autonomous network understanding and decision-making.

  ---
  ID: 3338
  Draft: Definition of Service Intent in Autonomic Networks
  Idea: Service Intent Model for Autonomic Networks
  Description: A structured semantic model and format for expressing service-level objectives and constraints across network, compute, and storage resources in autonomic domains.

  ---
  ID: 3339
  Draft: MCP-based Network Measurement Framework: Using Model Context Protocol for Intelligent Network Measurement
  Idea: MCP-based Network Measurement Framework
  Description: A framework enabling natural language-driven, AI-assisted network measurement by treating devices as MCP servers and controllers as MCP clients for real-time monitoring and fault diagnosis.

  ---
  ID: 3340
  Draft: Model Context Protocol (MCP) Extensions for Network Equipment Management
  Idea: MCP Extensions for Network Equipment Management
  Description: Extensions to the Model Context Protocol enabling network equipment to act as MCP servers with new capability tokens, tools, resources, and prompts for AI-driven management.

  ---
  ID: 3341
  Draft: Model Context Protocol (MCP) Extensions for Network Equipment Management
  Idea: MCP Extensions for Network Equipment
  Description: Define capability tokens, tools, resources, prompts, and error codes to enable network equipment to operate as MCP servers for AI-driven management.

  ---
  ID: 3342
  Draft: Web User Agents
  Idea: User Agent Duties Framework
  Description: Establish collective standards and duties that web user agents must uphold to serve their users rather than other constituencies.


  Return ONLY a JSON object mapping idea ID to score, like {"123": 3, "456": 1, ...}.
  No fences, no explanation.


# ideas

draft_name: draft-a2a-moqt-transport
title: A2A Protocol Transport over MOQT
idea_type: protocol
extracted_at: 2026-03-06T21:09:28.927513+00:00
description:
  Transport mechanism for Agent-to-Agent protocol using MOQT's publish/subscribe model and prioritization to enable discovery, negotiation, and real-time collaboration between AI agents.


draft_name: draft-aap-oauth-profile
title: Agent Authorization Profile for OAuth 2.0
idea_type: extension
extracted_at: 2026-03-06T21:09:28.933471+00:00
description:
  Authorization profile extending OAuth 2.0 and JWT with structured claims for agent identity, task context, operational constraints, delegation chains, and human oversight in M2M scenarios.


draft_name: draft-abaris-aicdh
title: AI-Disclosure HTTP Response Header
idea_type: mechanism
extracted_at: 2026-03-06T21:09:28.939114+00:00
description:
  Machine-readable HTTP response header field for disclosing presence and degree of AI-generated or AI-assisted content with support for layered disclosure strategies.


draft_name: draft-abbey-scim-agent-extension
title: SCIM 2.0 Extension for Agents and Agentic Applications
idea_type: extension
extracted_at: 2026-03-06T21:09:28.944726+00:00
description:
  Extension to SCIM specification defining new schemas and resource types for managing agents and agentic applications across identity domains.


draft_name: draft-agent-gw
title: Intelligent Agent Communication Gateway Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:09:28.949827+00:00
description:
  Architectural framework for multi-agent infrastructure providing semantic routing based on intent and capability, and working memory for managing structured context in collaborative workflows.


draft_name: draft-ahn-nmrg-5g-security-i2nsf-framework
title: Intent-Based Security Policy Translation for 5G
idea_type: architecture
extracted_at: 2026-03-06T21:09:32.589859+00:00
description:
  High-level security intents declared by administrators are automatically translated into enforceable network and application policies for 5G edge networks.


draft_name: draft-ahn-nmrg-5g-security-i2nsf-framework
title: Distributed Policy Enforcement via NEF and IBN Controllers
idea_type: mechanism
extracted_at: 2026-03-06T21:09:32.589859+00:00
description:
  Network policies are delivered to 5G core via NEF while application policies are enforced on UE through distributed IBN Controllers, enabling context-aware and adaptive enforcement.


draft_name: draft-ainp-protocol
title: AI-Native Network Protocol (AINP)
idea_type: protocol
extracted_at: 2026-03-06T21:09:32.602756+00:00
description:
  A semantic communication protocol that enables intent exchange between AI agents using semantic routing and capability-based discovery instead of location-based routing.


draft_name: draft-an-nmrg-i2icf-cits
title: Interface to In-Network Computing Functions for C-ITS
idea_type: architecture
extracted_at: 2026-03-06T21:09:32.612145+00:00
description:
  Framework for orchestrating and managing In-Network Computing Functions in Cooperative Intelligent Transportation Systems to optimize V2X communications, traffic management, and edge data processing.


draft_name: draft-an-nmrg-i2icf-cits
title: Agent-to-Agent Communication in Transportation Networks
idea_type: pattern
extracted_at: 2026-03-06T21:09:32.612145+00:00
description:
  Enables direct collaboration between intelligent agents within vehicles, RSUs, and network domains for coordinated decision-making in C-ITS.


draft_name: draft-ar-emu-hybrid-pqc-eapaka
title: Hybrid Post-Quantum Cryptography for EAP-AKA'
idea_type: extension
extracted_at: 2026-03-06T21:09:35.585693+00:00
description:
  Extends EAP-AKA' Forward Secrecy with hybrid post-quantum key agreement to protect against quantum computer threats to ephemeral keys.


draft_name: draft-architect-cittamarket
title: Bitcoin-Anchored AGI Identity Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:09:35.592231+00:00
description:
  Uses Bitcoin blockchain Proof-of-Work as an immutable anchor for creating unforgeable sovereign identity records of AGI systems.


draft_name: draft-aylward-aiga-1
title: Tiered Risk-Based Governance for Autonomous AI Agents
idea_type: architecture
extracted_at: 2026-03-06T21:09:35.598675+00:00
description:
  Framework applying proportional oversight to AI agents based on their capabilities with immutable kernel architecture and action-based authorization.


draft_name: draft-aylward-daap-v2
title: Distributed AI Accountability Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:09:35.610451+00:00
description:
  Multi-layered framework combining cryptographic identity verification, periodic check-ins, remote shutdown, adaptive location reporting, and behavioral monitoring for autonomous AI agents.


draft_name: draft-barney-caam
title: Post-Discovery Authorization Handshake
idea_type: protocol
extracted_at: 2026-03-06T21:09:39.032436+00:00
description:
  Runtime authorization layer that enforces agent behavior between discovery and tool execution through relationship-based access control and purpose-bound delegation.


draft_name: draft-barney-caam
title: Sidecar-based Authorization Mediator
idea_type: architecture
extracted_at: 2026-03-06T21:09:39.032436+00:00
description:
  Architecture pattern for deploying authorization enforcement as a sidecar component that mediates agent actions and propagates cryptographically verifiable intent.


draft_name: draft-bastian-jose-dvs
title: DH-KA derived HMAC for JWS
idea_type: mechanism
extracted_at: 2026-03-06T21:09:39.038313+00:00
description:
  Mechanism for deriving symmetric MAC keys from Diffie-Hellman key agreement combined with KDF using public information in JWS headers.


draft_name: draft-berlinai-vera
title: Zero Trust Runtime Agent Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:09:39.049956+00:00
description:
  Structured threat model and enforcement framework for AI agents with five pillars, typed schemas, and formally stated security properties.


draft_name: draft-berlinai-vera
title: Evidence-based Autonomy Maturity Model
idea_type: mechanism
extracted_at: 2026-03-06T21:09:39.049956+00:00
description:
  Runtime system where agents earn autonomy incrementally through cryptographic proof of safe behavior rather than time-based authorization.


draft_name: draft-bernardos-cats-isac-uc
title: Integrated Sensing and Communications for CATS
idea_type: architecture
extracted_at: 2026-03-06T21:09:39.055212+00:00
description:
  Paradigm for jointly designing sensing and communication functions to optimize shared spectral and hardware resources in wireless networks.


draft_name: draft-birkholz-verifiable-agent-conversations
title: Verifiable Agent Conversation Format
idea_type: protocol
extracted_at: 2026-03-06T21:09:43.044402+00:00
description:
  CDDL-based data format with JSON/CBOR representations for recording and cryptographically signing agent interactions including messages, tool invocations, and reasoning traces to enable auditability and compliance verification.


draft_name: draft-blahaj-grow-rpki-oauth
title: RPKI-based OIDC Authentication for Peering APIs
idea_type: mechanism
extracted_at: 2026-03-06T21:09:43.050335+00:00
description:
  Authentication mechanism that attests ASN-based party identities in OpenID Connect flows by leveraging RPKI attestations.


draft_name: draft-bradleylundberg-cfrg-arkg
title: Asynchronous Remote Key Generation Algorithm
idea_type: protocol
extracted_at: 2026-03-06T21:09:43.056803+00:00
description:
  Generic algorithm enabling delegation of asymmetric public key generation to untrusted parties without exposing private keys, supporting pseudonymity, forward secrecy, and multi-device authentication scenarios.


draft_name: draft-campbell-agentic-http
title: Agentic Hypercall Protocol
idea_type: pattern
extracted_at: 2026-03-06T21:09:43.063198+00:00
description:
  Standardized framework for agent-to-agent HTTP interactions using GET-based tool invocation, stateless authentication, and formalized 402 Payment Required status to enable economic transactions between agents.


draft_name: draft-chang-agent-context-interaction
title: Context Distribution Optimization Framework
idea_type: mechanism
extracted_at: 2026-03-06T21:09:45.851754+00:00
description:
  A system for optimizing context distribution across multi-agent workflows to reduce latency, token consumption, and improve task completion.


draft_name: draft-chang-agent-token-efficient
title: Agentic Data Optimization Layer (ADOL)
idea_type: protocol
extracted_at: 2026-03-06T21:09:45.858482+00:00
description:
  A data layer protocol that reduces token bloat in agent communication by compressing schema definitions, message structures, and capability exchanges.


draft_name: draft-chen-agent-decoupled-authorization-model
title: Intent-Based Just-in-Time Authorization
idea_type: architecture
extracted_at: 2026-03-06T21:09:45.864578+00:00
description:
  A decoupled authorization framework for agents that grants fine-grained permissions based on dynamic intent and behavioral trustworthiness rather than static identity.


draft_name: draft-chen-ai-agent-auth-new-requirements
title: Dynamic Behavior-Based Authentication and Authorization Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:09:45.870443+00:00
description:
  A specification of novel authentication and authorization requirements for autonomous agents that verify dynamic behavioral patterns rather than static identity.


draft_name: draft-chen-lake-edhoc-aka
title: EDHOC-AKA Authentication Method
idea_type: protocol
extracted_at: 2026-03-06T21:09:45.876279+00:00
description:
  An efficient mutual authentication and key derivation protocol combining AKA and EDHOC using symmetric cryptography for resource-constrained mobile networks.


draft_name: draft-chen-oauth-rar-agent-extensions
title: Policy Context in RAR
idea_type: extension
extracted_at: 2026-03-06T21:09:49.384820+00:00
description:
  Extension to OAuth Rich Authorization Requests allowing clients to explicitly request authorization evaluation under a specific policy assurance level.


draft_name: draft-chen-oauth-rar-agent-extensions
title: Lifecycle Binding for Authorizations
idea_type: mechanism
extracted_at: 2026-03-06T21:09:49.384820+00:00
description:
  Mechanism to bind authorization lifetime strictly to a task's lifecycle in long-running automated and agent-based scenarios.


draft_name: draft-chuyi-nmrg-ai-agent-network
title: AI Agent-Driven Network Operations
idea_type: architecture
extracted_at: 2026-03-06T21:09:49.391053+00:00
description:
  Architecture for deploying large model-based AI agents to automate network operations and maintenance with improved intelligence levels.


draft_name: draft-chuyi-nmrg-ai-agent-network
title: Network Data and Interface Adaptation for AI Agents
idea_type: requirement
extracted_at: 2026-03-06T21:09:49.391053+00:00
description:
  Framework addressing required changes to network data models, system architectures, and interfaces to support AI agent integration.


draft_name: draft-condrey-rats-witnessd-enrollment
title: Witnessd Trust Anchor Bootstrap Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:09:49.397218+00:00
description:
  Protocol for device enrollment, key provisioning, and trust anchor validation in the witnessd Proof of Process framework supporting multiple deployment modes.


draft_name: draft-contario-totp-secure-enrollment
title: Secure TOTP Key Enrollment
idea_type: mechanism
extracted_at: 2026-03-06T21:09:49.402778+00:00
description:
  Enhanced key exchange scheme for TOTP enrollment that prevents compromise of non-expiring TOTP keys through secure delivery mechanisms.


draft_name: draft-cosmos-protocol-specification
title: Cosmos Protocol: Trust-Native Identity and Communication
idea_type: protocol
extracted_at: 2026-03-06T21:09:49.408298+00:00
description:
  Badge-based identity and communication system enabling decentralized identity management and passwordless authentication without mandatory external consensus or blockchain infrastructure.


draft_name: draft-cui-ai-agent-discovery-invocation
title: AI Agent Metadata Format
idea_type: extension
extracted_at: 2026-03-06T21:09:53.529327+00:00
description:
  A common metadata schema for describing AI agents including capabilities, I/O specifications, supported languages, authentication methods, and tags.


draft_name: draft-cui-ai-agent-discovery-invocation
title: Capability-based Agent Discovery Mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:09:53.529327+00:00
description:
  A discovery mechanism that matches AI agents based on declared capabilities rather than explicit naming or enumeration.


draft_name: draft-cui-ai-agent-discovery-invocation
title: Intent-based Agent Selection
idea_type: extension
extracted_at: 2026-03-06T21:09:53.529327+00:00
description:
  An optional extension enabling intent-based selection of agents prior to capability-based discovery and invocation.


draft_name: draft-cui-ai-agent-task
title: Dynamic Task Coordination Requirements for AI Agents
idea_type: requirement
extracted_at: 2026-03-06T21:09:53.535559+00:00
description:
  A set of requirements for autonomous task coordination including dynamic discovery, negotiation, and context-aware scheduling with real-time adaptability.


draft_name: draft-cui-dmsc-agent-cdi
title: Cross-Domain Agent Interoperability Framework
idea_type: architecture
extracted_at: 2026-03-06T21:09:53.543579+00:00
description:
  A framework enabling seamless collaboration among AI agents across different networks and administrative domains through identity federation, trust delegation, and federated policy enforcement.


draft_name: draft-cui-dns-native-agent-naming-resolution
title: DNS-Native Agent Naming and Resolution
idea_type: protocol
extracted_at: 2026-03-06T21:09:53.551189+00:00
description:
  A DNS-based system using FQDNs as stable agent identifiers and resolving them to verifiable endpoints with cryptographic integrity chains.


draft_name: draft-cui-nmrg-auto-test
title: AI-Assisted Network Protocol Testing Framework
idea_type: architecture
extracted_at: 2026-03-06T21:09:53.558093+00:00
description:
  A framework leveraging AI and LLMs to automate network protocol testing through protocol formalization, test generation, and iterative refinement.


draft_name: draft-cui-nmrg-auto-test
title: Automation Maturity Levels for Protocol Testing
idea_type: pattern
extracted_at: 2026-03-06T21:09:53.558093+00:00
description:
  A structured classification system defining five levels of testing automation maturity from fully manual (Level 0) to fully autonomous and adaptive (Level 5).


draft_name: draft-cui-nmrg-llm-benchmark
title: LLM Network Configuration Evaluation Framework
idea_type: framework
extracted_at: 2026-03-06T21:09:56.486177+00:00
description:
  A comprehensive framework with emulator-based interactive environments, representative tasks, and multi-dimensional metrics to assess LLM agents' reasoning, command accuracy, and functional correctness in intent-driven network configuration.


draft_name: draft-cui-nmrg-llm-nm
title: Human-in-the-Loop LLM Network Management Framework
idea_type: framework
extracted_at: 2026-03-06T21:09:56.493701+00:00
description:
  An interoperable framework that combines enhanced telemetry, LLM agent decision modules, and interaction data models with enforced human oversight workflows for collaborative network management.


draft_name: draft-dhir-http-agent-profile
title: HTTP Agent Profile (HAP)
idea_type: protocol
extracted_at: 2026-03-06T21:09:56.500658+00:00
description:
  A protocol mechanism enabling cryptographic authentication of autonomous agents, separation of human and agent traffic via privacy-preserving tokens, and protocol-level value exchange for web agents.


draft_name: draft-diaconu-agents-authz-info-sharing
title: Cross-Domain Authorization Information Sharing for Multi-Agent Systems
idea_type: mechanism
extracted_at: 2026-03-06T21:09:56.506187+00:00
description:
  A solution approach for securely sharing authorization information across multiple administrative domains using dynamic identity, interoperable claims, and verifiable credentials.


draft_name: draft-drake-email-tpm-attestation
title: Hardware Attestation for Email Sender Verification
idea_type: mechanism
extracted_at: 2026-03-06T21:09:56.511532+00:00
description:
  A mechanism enabling email senders to include cryptographic hardware attestation evidence in message headers, verifiable directly to TPM manufacturer CAs, with privacy-preserving SD-JWT alternative for claim disclosure without hardware identity revelation.


draft_name: draft-du-ai-agent-communication-6g-aspect
title: AI Agent Communication Network Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:09:59.623657+00:00
description:
  Define networking requirements and standardization framework for agent-to-agent communication as a new traffic class in 6G systems.


draft_name: draft-eckert-anima-acp-free-ani
title: Application-Level Automation Network Infrastructure
idea_type: architecture
extracted_at: 2026-03-06T21:09:59.629816+00:00
description:
  Lightweight automation infrastructure reusing ANI trust materials and GRASP/BRSKI protocols without requiring ACP or changes to network forwarding planes.


draft_name: draft-eggert-mailmaint-uaautoconf
title: Automatic Email/Calendar/Contact Service Configuration
idea_type: mechanism
extracted_at: 2026-03-06T21:09:59.635409+00:00
description:
  Standardized mechanism for user agents to auto-discover and configure email, calendar, and contact server settings from service provider published configuration.


draft_name: draft-fu-cats-flow-lb
title: Flow-Level Load Balancing for CATS
idea_type: mechanism
extracted_at: 2026-03-06T21:09:59.641205+00:00
description:
  Data plane autonomous flow distribution mechanism that reduces control plane overhead in computing-aware traffic steering.


draft_name: draft-fu-nmop-agent-communication-framework
title: Multi-Agent Communication Framework for AIOps
idea_type: architecture
extracted_at: 2026-03-06T21:09:59.646512+00:00
description:
  Framework architecture with AI gateway and Agent Name Service enabling heterogeneous agent coordination for network intelligent operations and maintenance.


draft_name: draft-gaikwad-aps-profile
title: Agent Persistent State Profile
idea_type: architecture
extracted_at: 2026-03-06T21:10:03.169922+00:00
description:
  A vendor-neutral storage service class that bridges the gap between agent frameworks and storage systems by defining compliance, auditing, and cryptographic erasure primitives for durable agent state.


draft_name: draft-gaikwad-aps-profile
title: Compliance-oriented agent memory model
idea_type: pattern
extracted_at: 2026-03-06T21:10:03.169922+00:00
description:
  A storage abstraction that enables retention, audit trails, and cryptographic erasure of agent state tied to user or agent identity.


draft_name: draft-gaikwad-llm-benchmarking-methodology
title: LLM inference benchmarking methodology
idea_type: requirement
extracted_at: 2026-03-06T21:10:03.176584+00:00
description:
  A standardized test procedure and measurement specification for evaluating LLM serving systems across latency, throughput, scheduling, and resource management dimensions.


draft_name: draft-gaikwad-llm-benchmarking-profiles
title: LLM serving benchmarking profiles
idea_type: pattern
extracted_at: 2026-03-06T21:10:03.182573+00:00
description:
  Concrete architectural roles and workload patterns that bind terminology and procedures to specific System Under Test boundaries and measurement points for reproducible LLM benchmarking.


draft_name: draft-gaikwad-south-authorization
title: Stochastic Authorization (SOUTH)
idea_type: protocol
extracted_at: 2026-03-06T21:10:03.193475+00:00
description:
  An authorization protocol that returns deterministic or probabilistic allow decisions based on local policy, enabling incorporation of uncertainty and contextual information into access control.


draft_name: draft-gaikwad-woa
title: Web of Agents Description Format
idea_type: protocol
extracted_at: 2026-03-06T21:10:06.589143+00:00
description:
  A JSON-based format served from well-known locations that allows HTTP hosts to advertise AI agents with JSON Schema descriptions of inputs/outputs, enabling uniform client invocation.


draft_name: draft-gont-dhcwg-dhcpv6-iids
title: DHCPv6 Semantically Opaque IID Generation
idea_type: mechanism
extracted_at: 2026-03-06T21:10:06.596426+00:00
description:
  A DHCPv6 server-side algorithm for generating stable, semantically opaque IPv6 interface identifiers that remain consistent across multiple servers and reinstallments.


draft_name: draft-goswami-agentic-jwt
title: Agentic JWT for Intent-Execution Alignment
idea_type: extension
extracted_at: 2026-03-06T21:10:06.603473+00:00
description:
  An OAuth 2.0 extension using JWT to bind authorization tokens to user intents and specific agent tasks, preventing zero-trust drift in autonomous AI systems with dynamic workflows.


draft_name: draft-gupta-httpapi-events-query
title: Events Query Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:10:06.609758+00:00
description:
  A minimal HTTP-based protocol enabling user agents to receive event notifications directly from resources of interest using the resource itself as the notification source.


draft_name: draft-guy-bary-stamp-protocol
title: Secure Task-bound Agent Message Proof Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:10:06.616632+00:00
description:
  A cryptographic delegation and proof framework for multi-agent systems that binds access tokens to specific user intents, tasks, and agent identities while interoperating with OAuth 2.0 and GNAP.


draft_name: draft-hackett-ures
title: Unified HTML/CSS Rendering Requirements for Email
idea_type: requirement
extracted_at: 2026-03-06T21:10:09.148763+00:00
description:
  Standardized requirements for consistent rendering of HTML email across heterogeneous mail clients, addressing dark mode, assets, positioning, and SVG handling.


draft_name: draft-happel-structured-email-trust
title: Trust Model for Structured Email
idea_type: requirement
extracted_at: 2026-03-06T21:10:09.164684+00:00
description:
  Security and trust considerations and MUA handling recommendations for structured data embedded in email messages.


draft_name: draft-hong-nmrg-agenticai-ps
title: Agentic AI for Autonomous Network Management
idea_type: requirement
extracted_at: 2026-03-06T21:10:09.170165+00:00
description:
  Problem statement and standardization requirements for deploying collaborative autonomous AI agents to automate network operations, management, and security.


draft_name: draft-huang-acme-scalable-agent-enrollment
title: Zero-Knowledge Proof-based Attestation for Certificate Enrollment
idea_type: extension
extracted_at: 2026-03-06T21:10:13.112934+00:00
description:
  Integrates ZKPs into certificate enrollment to enable private and efficient continuous attestation without full validation overhead.


draft_name: draft-huang-acme-scalable-agent-enrollment
title: Host-Endorsement Bootstrap Model for Agent Certificate Provisioning
idea_type: pattern
extracted_at: 2026-03-06T21:10:13.112934+00:00
description:
  Establishes a one-time high-assurance trusted host environment that is then authorized to endorse certificate requests for its agents.


draft_name: draft-huang-rats-agentic-eat-cap-attest
title: EAT Extensions for Agent Capability Attestation
idea_type: extension
extracted_at: 2026-03-06T21:10:13.119661+00:00
description:
  Extends Entity Attestation Token with new claims to securely assert and verify functional, reasoning, and operational capabilities of autonomous agents.


draft_name: draft-hw-ai-agent-6g
title: 6G Agent Protocol Requirements and Enabling Technologies
idea_type: requirement
extracted_at: 2026-03-06T21:10:13.125547+00:00
description:
  Analyzes protocol requirements and enabling technologies for AI agents in 6G networks considering mobile-specific characteristics and performance constraints.


draft_name: draft-ietf-ace-coap-est-oscore
title: EST over CoAP with OSCORE and EDHOC
idea_type: protocol
extracted_at: 2026-03-06T21:10:13.131700+00:00
description:
  Specifies certificate enrollment protocol using CoAP with OSCORE security and EDHOC key establishment as an alternative to DTLS for constrained environments.


draft_name: draft-ietf-ace-edhoc-oscore-profile
title: ACE Profile with EDHOC and OSCORE
idea_type: protocol
extracted_at: 2026-03-06T21:10:13.138671+00:00
description:
  Defines an ACE authentication and authorization profile that uses EDHOC for mutual authentication and establishes OSCORE security contexts for constrained resource servers.


draft_name: draft-ietf-anima-brski-prm
title: Pledge Responder Mode for BRSKI
idea_type: extension
extracted_at: 2026-03-06T21:10:17.441902+00:00
description:
  Extends BRSKI to support pledges in server mode where the registrar initiates contact through a registrar-agent intermediary when direct pledge-to-registrar communication is unavailable.


draft_name: draft-ietf-anima-brski-prm
title: Object Security for Bootstrap Trust
idea_type: mechanism
extracted_at: 2026-03-06T21:10:17.441902+00:00
description:
  Establishes trust between pledge and registrar using object-level security mechanisms rather than transport-layer security to accommodate the reversed interaction model.


draft_name: draft-ietf-anima-constrained-voucher
title: Constrained BRSKI with CBOR Vouchers
idea_type: protocol
extracted_at: 2026-03-06T21:10:17.449319+00:00
description:
  Adapts BRSKI for resource-constrained IoT networks by replacing JSON-encoded vouchers with compact CBOR encoding and introducing optimized data types to reduce voucher size.


draft_name: draft-ietf-emu-eap-edhoc
title: EAP Authentication Method using EDHOC
idea_type: protocol
extracted_at: 2026-03-06T21:10:17.462149+00:00
description:
  Defines EAP-EDHOC as a new EAP method leveraging the lightweight EDHOC handshake protocol to enable authentication and key establishment in constrained network environments.


draft_name: draft-ietf-emu-hybrid-pqc-eapaka
title: Hybrid Post-Quantum Cryptography for EAP-AKA' Forward Secrecy
idea_type: extension
extracted_at: 2026-03-06T21:10:17.468645+00:00
description:
  Enhances EAP-AKA' forward secrecy by incorporating hybrid post-quantum cryptography alongside traditional ECDHE to protect against future quantum computing threats.


draft_name: draft-ietf-emu-pqc-eapaka
title: Post-Quantum KEMs for EAP-AKA'
idea_type: extension
extracted_at: 2026-03-06T21:10:20.105843+00:00
description:
  Integrates post-quantum key encapsulation mechanisms into EAP-AKA' to protect against quantum computer threats to the ephemeral key exchange.


draft_name: draft-ietf-hpke-hpke
title: Hybrid Public Key Encryption Scheme
idea_type: protocol
extracted_at: 2026-03-06T21:10:20.112449+00:00
description:
  Defines a modular encryption scheme combining asymmetric KEMs, KDFs, and AEAD functions for hybrid encryption of arbitrary-sized plaintexts.


draft_name: draft-ietf-lake-app-profiles
title: CBOR-based EDHOC Application Profile Representation
idea_type: mechanism
extracted_at: 2026-03-06T21:10:20.125936+00:00
description:
  Defines a canonical CBOR format for describing, distributing, and storing EDHOC application profiles to ensure interoperability and extensibility.


draft_name: draft-ietf-lake-authz
title: Zero-touch device enrollment via EDHOC extension
idea_type: mechanism
extracted_at: 2026-03-06T21:10:22.869159+00:00
description:
  Authorization mechanism for onboarding new devices to constrained networks by leveraging manufacturer-installed trust anchors through EDHOC extension points.


draft_name: draft-ietf-lake-edhoc-psk
title: PSK authentication method for EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:10:22.878887+00:00
description:
  PSK-based authentication variant for EDHOC supporting both external PSKs and resumption PSKs with reduced computational overhead and quantum resistance.


draft_name: draft-ietf-lake-ra
title: Remote attestation integration in EDHOC
idea_type: mechanism
extracted_at: 2026-03-06T21:10:22.885521+00:00
description:
  Procedure for performing remote attestation as part of the EDHOC key exchange protocol aligned with the RATS architecture.


draft_name: draft-ietf-lamps-attestation-freshness
title: Nonce-based freshness for attestation in CSRs
idea_type: mechanism
extracted_at: 2026-03-06T21:10:22.892161+00:00
description:
  Process for RA/CA-supplied nonces to demonstrate freshness of attestation evidence in certificate enrollment protocols (CMP, EST, CMC).


draft_name: draft-ietf-lamps-e2e-mail-guidance
title: E-mail End-to-End Encryption Guidance
idea_type: pattern
extracted_at: 2026-03-06T21:10:26.465719+00:00
description:
  Provides implementer guidance and vocabulary to help mail user agents safely implement end-to-end cryptographic protections while avoiding common failures and usability pitfalls.


draft_name: draft-ietf-lamps-est-renewal-info
title: Server-Directed Certificate Renewal Timing
idea_type: extension
extracted_at: 2026-03-06T21:10:26.471410+00:00
description:
  Extends EST to allow servers to signal to end-entity devices when they should begin certificate renewal rather than relying on fixed percentage-based heuristics.


draft_name: draft-ietf-lamps-private-key-stmt-attr
title: Private Key Possession Statement Attribute
idea_type: extension
extracted_at: 2026-03-06T21:10:26.476680+00:00
description:
  Defines an X.509 attribute that allows certificate subjects to provide a signed statement of private key possession as proof for CA enrollment when direct key ownership demonstration is impractical.


draft_name: draft-ietf-lisp-nexagon
title: LISP-based geospatial intelligence network
idea_type: protocol
extracted_at: 2026-03-06T21:10:28.910479+00:00
description:
  Combines LISP locator/ID separation with H3 spatial indexing to enable distributed agents to report and aggregate geospatial state information for physical-world applications.


draft_name: draft-ietf-mailmaint-pacc
title: Automatic email/calendar/contact server configuration
idea_type: mechanism
extracted_at: 2026-03-06T21:10:28.918134+00:00
description:
  Service providers publish standardized configuration information that user agents retrieve to automatically configure email, calendar, and contact server settings.


draft_name: draft-ietf-netconf-configuration-tracing
title: External trace ID for NETCONF configuration attribution
idea_type: extension
extracted_at: 2026-03-06T21:10:28.925034+00:00
description:
  NETCONF mechanism to automatically map configuration modifications to their originating NMS through trace IDs, enabling root cause analysis and handling of concurrent NMS changes.


draft_name: draft-ietf-roll-enrollment-priority
title: RPL enrollment priority control mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:10:28.933423+00:00
description:
  Allows RPL Root to globally disable enrollment announcements or adjust base priority for enrollment operations in low-power and lossy networks.


draft_name: draft-ietf-sml-trust
title: Trust and security framework for structured email
idea_type: requirement
extracted_at: 2026-03-06T21:10:32.188427+00:00
description:
  Provides recommendations for message user agents on handling trust and security considerations when processing structured data embedded in email messages.


draft_name: draft-ietf-sshm-mlkem-hybrid-kex
title: PQ/T hybrid key exchange for SSH
idea_type: protocol
extracted_at: 2026-03-06T21:10:32.195686+00:00
description:
  Defines post-quantum/traditional hybrid key exchange methods combining ML-KEM with ECDH for the SSH Transport Layer Protocol.


draft_name: draft-ietf-suit-firmware-encryption
title: Encrypted payload mechanism for SUIT manifests
idea_type: extension
extracted_at: 2026-03-06T21:10:32.209324+00:00
description:
  Defines encryption techniques for firmware, software, and data within SUIT manifests using ES-DH and AES-KW key agreement with symmetric encryption.


draft_name: draft-ietf-tls-deprecate-obsolete-kex
title: Deprecation of weak key exchanges in DTLS 1.2
idea_type: requirement
extracted_at: 2026-03-06T21:10:32.216183+00:00
description:
  Deprecates finite-field Diffie-Hellman and RSA key exchanges, and discourages static ECDH cipher suites in (D)TLS 1.2.


draft_name: draft-ietf-tls-ecdhe-mlkem
title: Hybrid ECDHE-MLKEM Key Agreement
idea_type: mechanism
extracted_at: 2026-03-06T21:10:35.156875+00:00
description:
  Three hybrid key agreement mechanisms combining post-quantum ML-KEM with classical ECDHE for TLS 1.3.


draft_name: draft-ietf-tls-extended-key-update
title: Extended Key Update with Fresh DH Exchange
idea_type: extension
extracted_at: 2026-03-06T21:10:35.164778+00:00
description:
  Enhanced key update mechanism for TLS 1.3 that performs a new Diffie-Hellman exchange within a session to provide post-compromise security.


draft_name: draft-irtf-nmrg-llm-nm
title: LLM-Assisted Network Management Framework
idea_type: architecture
extracted_at: 2026-03-06T21:10:35.172259+00:00
description:
  Interoperable framework for collaborative network management between LLMs and human operators with standardized telemetry, decision modules, and human-in-the-loop workflows.


draft_name: draft-jeong-opsawg-intent-based-sdv-framework
title: Intent-Based Management for Software-Defined Vehicles
idea_type: architecture
extracted_at: 2026-03-06T21:10:35.178598+00:00
description:
  Management framework using intent-based declarations to configure networks, security, and applications in cloud-native software-defined vehicles for autonomous transportation systems.


draft_name: draft-jewell-aibdp
title: AI Boundary Declaration Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:10:35.184493+00:00
description:
  Machine-readable protocol for expressing content usage boundaries relative to AI systems, specifying permissions and denials for indexing, training, mimicry, and agentic access.


draft_name: draft-jia-oauth-scope-aggregation
title: Scope Aggregation Pattern
idea_type: pattern
extracted_at: 2026-03-06T21:10:38.209661+00:00
description:
  OAuth 2.0 authorization pattern where AI agents collect all required scopes upfront and request them in a single authorization flow rather than multiple sequential requests.


draft_name: draft-jiang-cats-reference-acn
title: AI-Agent Communication Network Reference Model
idea_type: architecture
extracted_at: 2026-03-06T21:10:38.216711+00:00
description:
  CATS-based reference architecture for networks enabling communication between heterogeneous AI agents with defined reference points, protocol stacks, and signaling procedures.


draft_name: draft-jiang-seat-dynamic-attestation
title: Dynamic Attestation for TLS Sessions
idea_type: mechanism
extracted_at: 2026-03-06T21:10:38.224250+00:00
description:
  Mechanism for conveying real-time remote attestation information during long-lived TLS sessions to enable authorization decisions based on current AI agent runtime posture including TCB, models, and policies.


draft_name: draft-josefsson-chempat
title: PQ/T Hybrid KEM Combiner
idea_type: extension
extracted_at: 2026-03-06T21:10:38.231383+00:00
description:
  Generic family of post-quantum and traditional hybrid key encapsulation mechanisms providing analyzed security constructs with concrete instantiations across multiple curve and post-quantum algorithm combinations.


draft_name: draft-jurkovikj-collab-tunnel
title: Collaboration Tunnel Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:10:38.237789+00:00
description:
  HTTP-based protocol for efficient content delivery to automated agents using bidirectional URL discovery, content fingerprinting with ETags, and conditional request discipline to reduce bandwidth.


draft_name: draft-jurkovikj-httpapi-agentic-state
title: Semantic Validators for Multi-Representation Consistency
idea_type: extension
extracted_at: 2026-03-06T21:10:42.019269+00:00
description:
  Extend HTTP concurrency control beyond single representations by introducing semantic validators that enforce consistency across multiple synchronized representations of the same canonical resource state.


draft_name: draft-kale-agntcy-federated-privacy
title: Privacy-Preserving Federated Learning for Multi-Tenant AI Agents
idea_type: architecture
extracted_at: 2026-03-06T21:10:42.027702+00:00
description:
  Define an architecture combining federated averaging, differential privacy, and secure aggregation to enable collaborative model training across AI agent systems while maintaining tenant isolation and formal privacy guarantees.


draft_name: draft-kartha-grd
title: Geospatial Resource Discovery Service
idea_type: architecture
extracted_at: 2026-03-06T21:10:42.034941+00:00
description:
  Introduce a resource discovery mechanism that resolves physical location and observer context to network resources, enabling spatial computing applications to discover resources by presence rather than predefined identifiers.


draft_name: draft-kompella-lsr-mptecap
title: Multipath Traffic Engineering Capabilities Advertisement
idea_type: mechanism
extracted_at: 2026-03-06T21:10:42.041072+00:00
description:
  Define mechanisms to distribute multipath traffic engineering node capabilities through link state routing protocols, enabling path computation agents to identify nodes supporting MPTE-based tunnel provisioning.


draft_name: draft-kotecha-agentic-dispute-protocol
title: Agentic Dispute Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:10:42.047044+00:00
description:
  Standardize a protocol for autonomous agents to file, process, and resolve disputes through cryptographically-verifiable message formats, evidence submission, arbitrator discovery, and deterministic contract enforcement.


draft_name: draft-lake-pocero-authkem-edhoc
title: KEM-based Authentication for EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:10:46.334504+00:00
description:
  Extends EDHOC with post-quantum cryptography by enabling signature-free authentication using Key Encapsulation Mechanisms instead of traditional signatures.


draft_name: draft-lake-pocero-authkem-ikr-edhoc
title: KEM-based Authentication for IKR Scenarios
idea_type: extension
extracted_at: 2026-03-06T21:10:46.341440+00:00
description:
  Optimizes KEM-based EDHOC authentication for initiator-known-responder scenarios to reduce handshake messages while maintaining security properties.


draft_name: draft-lenders-core-dnr
title: DNS Discovery of OSCORE-based Resolvers
idea_type: pattern
extracted_at: 2026-03-06T21:10:46.348066+00:00
description:
  Proposes mechanisms for discovering DNS resolvers that communicate via OSCORE and EDHOC through DNS SVCB records, addressing the challenge of exchanging security contexts in the DNS layer.


draft_name: draft-leon-distributed-multi-signer
title: Distributed DNSSEC Multi-Signer Bootstrap Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:10:46.353960+00:00
description:
  Defines a distributed DNSSEC multi-signer model with dedicated agents for orchestration and zone data combination, including the HSYNC RRtype for provider designation and inter-agent secure communication mechanisms.


draft_name: draft-levy-llm-uri-scheme
title: LLM URI Scheme
idea_type: protocol
extracted_at: 2026-03-06T21:10:46.359823+00:00
description:
  Registers a new URI scheme for identifying and configuring Large Language Model endpoints with encoded provider, model, credentials, and inference parameters.


draft_name: draft-li-dmsc-inf-architecture
title: Network-Integrated Agent Collaboration Infrastructure
idea_type: architecture
extracted_at: 2026-03-06T21:10:49.392853+00:00
description:
  An architectural framework where network control and forwarding planes actively participate in multi-agent collaboration rather than serving as passive transport.


draft_name: draft-li-semantic-routing-architecture
title: Semantic Routing Architecture for AI Agents
idea_type: architecture
extracted_at: 2026-03-06T21:10:49.413599+00:00
description:
  An application-layer routing architecture that uses agent semantics, intent vectors, and trust scores to dynamically route and coordinate multi-agent interactions.


draft_name: draft-liang-agentdns
title: AgentDNS: Domain Naming System for LLM Agents
idea_type: protocol
extracted_at: 2026-03-06T21:10:49.420098+00:00
description:
  A DNS-inspired system providing unified namespace, semantic service discovery, protocol-aware interoperability, and authentication for autonomous agent and tool service discovery across vendors.


draft_name: draft-liu-agent-operation-authorization
title: Agent Operation Authorization Framework
idea_type: mechanism
extracted_at: 2026-03-06T21:10:52.067231+00:00
description:
  JWT-based mechanism enabling verifiable delegation of actions from humans to autonomous agents with fine-grained operation authorization and cryptographic enforcement.


draft_name: draft-liu-nmrg-ai-llm-inference-requirements
title: Network Management Requirements for LLM Inference
idea_type: requirement
extracted_at: 2026-03-06T21:10:54.873588+00:00
description:
  Systematic analysis of network and system challenges posed by large-scale LLM inference deployments and the management requirements they impose.


draft_name: draft-liu-oauth-a2a-profile
title: OAuth Transaction Tokens for Agent-to-Agent Communication
idea_type: extension
extracted_at: 2026-03-06T21:10:54.880011+00:00
description:
  Profile for embedding A2A call chain context within OAuth Transaction Tokens to maintain agent identity and authorization across distributed agent workloads.


draft_name: draft-liu-rtgwg-llmsync-multicast
title: Multicast for LLM Inference Synchronization
idea_type: pattern
extracted_at: 2026-03-06T21:10:54.892676+00:00
description:
  Use of multicast mechanisms to optimize synchronization and distribution of large language model inference across cloud service deployments.


draft_name: draft-liu-saag-zt-problem-statement
title: Zero Trust Interoperability Framework
idea_type: requirement
extracted_at: 2026-03-06T21:10:54.899208+00:00
description:
  Problem statement and requirements for open, interoperable multi-vendor zero trust protocols and standards addressing cloud services, remote workforce, and intelligent agent scenarios.


draft_name: draft-luan-rtgwg-sdaf
title: Symmetry-Driven Asynchronous Forwarding
idea_type: mechanism
extracted_at: 2026-03-06T21:10:58.402206+00:00
description:
  A forwarding plane mechanism that maintains route symmetry during asynchronous control plane convergence to eliminate micro-loops and routing black holes in dynamic satellite networks.


draft_name: draft-luan-rtgwg-sdaf
title: Fast Reroute for LEO Satellite Networks
idea_type: mechanism
extracted_at: 2026-03-06T21:10:58.402206+00:00
description:
  A pre-computed backup path mechanism optimized for LEO satellite topologies to enable rapid failover under link/node failures.


draft_name: draft-mallick-muacp
title: Micro Agent Communication Protocol (µACP)
idea_type: protocol
extracted_at: 2026-03-06T21:10:58.408973+00:00
description:
  A resource-efficient messaging protocol for autonomous agents on constrained Edge/IoT devices with deterministic memory and processing bounds.


draft_name: draft-mao-rtgwg-apn-framework-for-ioa
title: APN Framework for Internet of Agents
idea_type: architecture
extracted_at: 2026-03-06T21:10:58.419852+00:00
description:
  Application of Application-aware Networking (APN) to provide fine-grained network services tailored to AI agent interaction requirements across distributed cloud platforms.


draft_name: draft-messous-eat-ai
title: EAT Profile for AI Agent Attestation
idea_type: extension
extracted_at: 2026-03-06T21:11:01.281583+00:00
description:
  A standardized token format for remote attestation of autonomous AI agents that includes claims for model integrity, training data provenance, and inference-time access policies.


draft_name: draft-meta-layer-overview
title: Meta-Layer Coordination Substrate
idea_type: architecture
extracted_at: 2026-03-06T21:11:01.288478+00:00
description:
  A programmable coordination infrastructure operating above content layers enabling communities and agents to coordinate presence, annotation, and governance across platforms.


draft_name: draft-meunier-webbotauth-registry
title: Signature Agent Card Format and Registry
idea_type: protocol
extracted_at: 2026-03-06T21:11:01.294955+00:00
description:
  A JSON-based metadata format for signature agents to advertise identity, capabilities, and cryptographic keys with an extensible IANA registry for interoperable agent discovery.


draft_name: draft-meyerzuselha-oauth-web-message-response-mode
title: Web Message Response Mode
idea_type: extension
extracted_at: 2026-03-06T21:11:03.933481+00:00
description:
  OAuth 2.0 response mode using postMessage API to transmit authorization parameters to popup/iframe-based clients.


draft_name: draft-mishra-oauth-agent-grants
title: Delegated Agent Authorization Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:03.941187+00:00
description:
  OAuth 2.0-modeled protocol for AI agents to obtain human-delegated authorization with cryptographic identity, signed JWT grants, revocation, audit trails, and policy-based decision making.


draft_name: draft-moreno-lisp-uberlay
title: Uberlay Transit Overlay Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:11:03.947921+00:00
description:
  LISP-based architecture using a transit overlay to interconnect multiple heterogeneous network overlays with independent control and data planes.


draft_name: draft-mozleywilliams-dnsop-bandaid
title: DNS-based AI Agent Discovery
idea_type: mechanism
extracted_at: 2026-03-06T21:11:03.957108+00:00
description:
  DNS namespace convention using SVCB records under _agents leaf zones to enable metadata exchange and capability advertisement for agent-to-agent discovery.


draft_name: draft-mozleywilliams-dnsop-dnsaid
title: DNS namespace for AI agent discovery
idea_type: mechanism
extracted_at: 2026-03-06T21:11:07.414961+00:00
description:
  Defines a structured DNS namespace and record usage model for publishing AI agent metadata and capability advertisements within an organization's DNS domain.


draft_name: draft-mp-agntcy-ads
title: Distributed agent directory service with content-routing
idea_type: architecture
extracted_at: 2026-03-06T21:11:07.422689+00:00
description:
  A distributed directory service for storing and discovering AI agent metadata across interconnected directories using content-routing protocol.


draft_name: draft-mpsb-agntcy-messaging
title: Comparative analysis of messaging protocols for agentic AI
idea_type: pattern
extracted_at: 2026-03-06T21:11:07.430004+00:00
description:
  Evaluates six messaging protocols (AMQP, MQTT, NATS, Kafka, WebSocket variants, and SLIM) against security and performance requirements specific to GenAI agent systems.


draft_name: draft-mpsb-agntcy-slim
title: SLIM protocol for real-time interactive messaging
idea_type: protocol
extracted_at: 2026-03-06T21:11:07.436783+00:00
description:
  A protocol combining gRPC over HTTP/2 and HTTP/3 with group communication, MLS-based end-to-end encryption, and RPC semantics for real-time AI agent interactions.


draft_name: draft-mvieuille-kerpass-ephemsec
title: EPHEMSEC public-key-based one-time password algorithm
idea_type: mechanism
extracted_at: 2026-03-06T21:11:07.443269+00:00
description:
  An OTP/OTK generation algorithm using public key cryptography with support for context binding and embedded time synchronization to resist phishing and MITM attacks.


draft_name: draft-mw-spice-actor-chain
title: Cryptographically Verifiable Actor Chain
idea_type: extension
extracted_at: 2026-03-06T21:11:13.343969+00:00
description:
  A tamper-evident ordered record of all actors in a delegation chain using a new actor_chain claim to replace the informational-only nested act claim in OAuth 2.0 Token Exchange.


draft_name: draft-mw-spice-actor-chain
title: High-Assurance Data-Plane Policy Enforcement
idea_type: requirement
extracted_at: 2026-03-06T21:11:13.343969+00:00
description:
  Enable cryptographic verification of actual delegation paths to enforce policies and prevent unauthorized delegation in multi-hop service environments.


draft_name: draft-mw-wimse-transitive-attestation
title: Transitive Attestation Profile for WIMSE
idea_type: architecture
extracted_at: 2026-03-06T21:11:13.352470+00:00
description:
  A WIMSE profile that cryptographically binds software workloads to their local execution environment through a transitive chain of trust consuming platform evidence.


draft_name: draft-mw-wimse-transitive-attestation
title: Identity Portability Binding via Proof of Residency
idea_type: mechanism
extracted_at: 2026-03-06T21:11:13.352470+00:00
description:
  Mechanism to prevent misappropriation of software credentials by binding them to authorized execution environments through proof of local residency.


draft_name: draft-mzsg-rtgwg-agent-cross-device-comm-framework
title: Cross-Device Communication Framework for Network AI Agents
idea_type: architecture
extracted_at: 2026-03-06T21:11:13.358949+00:00
description:
  A framework defining requirements and protocols for AI agents deployed on different network devices to collaborate on tasks like network measurement and troubleshooting.


draft_name: draft-nandakumar-a2a-moqt-transport
title: A2A Protocol Transport over MOQT
idea_type: protocol
extracted_at: 2026-03-06T21:11:13.365099+00:00
description:
  Specification for transporting Agent-to-Agent protocol over Media over QUIC Transport using its publish/subscribe model and prioritization for agent discovery and collaboration.


draft_name: draft-nandakumar-agent-sd-jwt
title: SD-Card Format for Agent Identity and Discovery
idea_type: pattern
extracted_at: 2026-03-06T21:11:13.371299+00:00
description:
  An SD-JWT encoding of Agent Cards enabling selective disclosure of agent capabilities and metadata while maintaining cryptographic integrity without correlation across contexts.


draft_name: draft-narajala-ans
title: Agent Name Service (ANS)
idea_type: architecture
extracted_at: 2026-03-06T21:11:17.243783+00:00
description:
  A DNS-inspired protocol-agnostic registry architecture for secure AI agent discovery using PKI certificates for verifiable identity and trust.


draft_name: draft-narajala-ans
title: Capability-Aware DNS Resolution
idea_type: extension
extracted_at: 2026-03-06T21:11:17.243783+00:00
description:
  Extension of DNS naming conventions to enable agents to advertise and resolve capabilities during service discovery.


draft_name: draft-narvaneni-agent-uri
title: agent:// URI Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:17.251611+00:00
description:
  A URI-based framework using RFC 6570 templates for uniform addressing, invocation, and interoperation of autonomous agents across platforms.


draft_name: draft-narvaneni-agent-uri
title: Layered Agent Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:11:17.251611+00:00
description:
  A stratified design supporting minimal implementations (addressing and transport) with optional extensible features (capability discovery, contracts, orchestration).


draft_name: draft-nennemann-wimse-ect
title: Execution Context Tokens (ECT)
idea_type: extension
extracted_at: 2026-03-06T21:11:17.264549+00:00
description:
  JWT-based tokens that record task execution across distributed agentic workflows as a directed acyclic graph linked through predecessor references.


draft_name: draft-nsiangani-authenticatedsecuredlayer
title: ASL Protocol with Phi-Ns Cryptosystem
idea_type: protocol
extracted_at: 2026-03-06T21:11:19.696022+00:00
description:
  Cryptographic protocol for authenticated and encrypted communication using phi-ns-x arithmetic primitive as alternative to RSA, DH, AES, and SHA.


draft_name: draft-oauth-ai-agents-on-behalf-of-user
title: OAuth 2.0 On-Behalf-Of User Authorization for AI Agents
idea_type: extension
extracted_at: 2026-03-06T21:11:19.702952+00:00
description:
  OAuth 2.0 extension enabling AI agents to obtain access tokens for acting on behalf of users with explicit consent through requested_actor and actor_token parameters.


draft_name: draft-oauth-transaction-tokens-for-agents
title: Transaction Tokens Agent Context Extension
idea_type: extension
extracted_at: 2026-03-06T21:11:19.709890+00:00
description:
  Extension to OAuth Transaction Tokens framework adding 'actor' and 'principal' fields to propagate agent context for granular access control decisions.


draft_name: draft-pbs-sidrops-roaanycast
title: ROA Governance for Anycast Services
idea_type: pattern
extracted_at: 2026-03-06T21:11:22.979105+00:00
description:
  Best practices for managing Route Origin Authorizations in globally anycasted services with unique origin ASNs per node, including risk mitigation for RPKI operations.


draft_name: draft-pioli-agent-discovery
title: Agent Registration and Discovery Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:22.987277+00:00
description:
  Lightweight protocol for registering, discovering, and reaching autonomous software agents in distributed environments with stable identities, dynamic endpoint resolution, and capability advertisement.


draft_name: draft-pocero-authkem-edhoc
title: KEM-based Authentication for EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:11:22.994093+00:00
description:
  Extension to EDHOC protocol incorporating post-quantum cryptography for both key exchange and authentication using signature-free KEM-based authentication with NIST-standardized ML-KEM.


draft_name: draft-pocero-authkem-ikr-edhoc
title: Efficient KEM-based Authentication for EDHOC in IKR Scenarios
idea_type: extension
extracted_at: 2026-03-06T21:11:23.009160+00:00
description:
  Optimized variant of KEM-based EDHOC authentication for initiator-known responder scenarios enabling post-quantum authentication with a three-message handshake in constrained environments.


draft_name: draft-ra-emu-pqc-eapaka
title: Post-Quantum Key Encapsulation in EAP-AKA prime
idea_type: extension
extracted_at: 2026-03-06T21:11:23.019386+00:00
description:
  Enhancement to EAP-AKA' to use post-quantum Key Encapsulation Mechanisms instead of traditional ECDHE for achieving forward secrecy against quantum computer threats.


draft_name: draft-ramakrishna-satp-data-sharing
title: Cross-System Asset View Request-Response Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:26.254635+00:00
description:
  A request-response protocol enabling systems to request and supply views of virtual assets across network boundaries through gateway nodes with access control policies.


draft_name: draft-ramakrishna-satp-views-addresses
title: Global Addressing Scheme for Asset Views
idea_type: mechanism
extracted_at: 2026-03-06T21:11:26.262434+00:00
description:
  A mechanism for systems to project views of assets to external parties and for those parties to locate and address views using globally unique identifiers.


draft_name: draft-ravikiran-clawdentity-protocol
title: Cryptographic Identity and Trust Protocol for AI Agents
idea_type: protocol
extracted_at: 2026-03-06T21:11:26.269421+00:00
description:
  A protocol providing per-agent Ed25519 identity, registry-issued credentials, proof-of-possession signing, and bilateral trust establishment for AI agent-to-agent communication.


draft_name: draft-romanchuk-normative-admissibility
title: Normative Framework for Evaluating Agent Speech Acts
idea_type: framework
extracted_at: 2026-03-06T21:11:26.275668+00:00
description:
  A framework for deterministically evaluating whether autonomous agent statements are admissible based on modality and grounding state without requiring truth verification.


draft_name: draft-rosenberg-aiproto
title: Normalized API for AI Agents Calling Tools (N-ACT)
idea_type: protocol
extracted_at: 2026-03-06T21:11:26.281689+00:00
description:
  An OpenAI-compatible protocol enabling AI agents to enumerate and invoke third-party tools through a standardized interface for enterprise agent integration.


draft_name: draft-rosenberg-aiproto-a2t
title: Agent-to-Tool Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:29.017478+00:00
description:
  A protocol enabling AI agents to enumerate and invoke third-party tools through a standardized interface based on OpenAI specifications.


draft_name: draft-rosenberg-aiproto-cheq
title: Human-in-the-Loop Confirmation Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:29.025216+00:00
description:
  A protocol allowing humans to confirm AI agent decisions and provide sensitive information without exposing it to the agent, preventing hallucination-driven unwanted actions.


draft_name: draft-rosenberg-aiproto-framework
title: AI Agent Protocol Framework
idea_type: architecture
extracted_at: 2026-03-06T21:11:29.031279+00:00
description:
  A framework identifying protocols, use cases, and requirements for AI agent communications across users, APIs, and other agents on the Internet.


draft_name: draft-rosenberg-aiproto-nact
title: Normalized API for AI Agents Calling Tools
idea_type: protocol
extracted_at: 2026-03-06T21:11:29.037024+00:00
description:
  A protocol providing standardized enumeration and invocation of third-party tools for enterprise AI agents through a normalized interface.


draft_name: draft-rosenberg-oauth-aauth
title: Agent Authorization Grant
idea_type: extension
extracted_at: 2026-03-06T21:11:31.886748+00:00
description:
  OAuth 2.1 extension enabling AI agents to obtain access tokens by collecting PII through natural language conversation rather than traditional grant flows, with LLM hallucination constraints.


draft_name: draft-sarischo-6gip-aiagent-requirements
title: 6G AI Agent Requirements Framework
idea_type: requirement
extracted_at: 2026-03-06T21:11:31.893892+00:00
description:
  Requirements specification for AI/ML agents in 6G networks covering agent types, application areas, and implementation approaches including DNS-based discovery.


draft_name: draft-schulze-ecap
title: Ethical Crawler Agreement Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:31.900882+00:00
description:
  Application-layer protocol using cryptographic signatures and declarative policy handshakes to establish verifiable, consent-based access between web hosts and automated agents.


draft_name: draft-scim-agent-extension
title: SCIM 2.0 Agent Extension
idea_type: extension
extracted_at: 2026-03-06T21:11:31.906865+00:00
description:
  SCIM specification extension providing schemas and resource types for managing agents and agentic applications across identity provider and service provider domains.


draft_name: draft-sipos-dtn-bp-safe
title: Bundle Protocol Security Association Negotiation
idea_type: protocol
extracted_at: 2026-03-06T21:11:31.913369+00:00
description:
  Protocol for negotiating scoped security associations between BPv7 agents in delay-tolerant networks to amortize asymmetric cryptography costs and enable efficient high-throughput security.


draft_name: draft-sogomonian-ai-uri-scheme
title: AI URI Scheme
idea_type: protocol
extracted_at: 2026-03-06T21:11:35.061460+00:00
description:
  A dedicated URI scheme enabling autonomous systems and robots to connect natively while allowing human-facing applications to interoperate via HTTPS gateways.


draft_name: draft-sogomonian-aiip-architecture
title: AIIP Architecture with Resolve-Invoke-Receipt Pattern
idea_type: architecture
extracted_at: 2026-03-06T21:11:35.069208+00:00
description:
  An architectural model for stateless, verifiable invocation of autonomous systems using a resolve-invoke-receipt pattern with cryptographically verifiable execution receipts and optional attestation evidence.


draft_name: draft-sogomonian-aiip-architecture
title: Byte-Precise Native Envelope
idea_type: mechanism
extracted_at: 2026-03-06T21:11:35.069208+00:00
description:
  A decentralized discovery mechanism using a byte-precise native envelope for machine-to-machine operation in AIIP.


draft_name: draft-song-oauth-ai-agent-authorization
title: OAuth 2.0 Target-Based Authorization Extension
idea_type: extension
extracted_at: 2026-03-06T21:11:35.076163+00:00
description:
  An OAuth 2.0 extension adding a target_id field to enable granular authorization and precise permission management for AI agents.


draft_name: draft-song-oauth-ai-agent-collaborate-authz
title: OAuth 2.0 Multi-Agent Task Group Authorization
idea_type: extension
extracted_at: 2026-03-06T21:11:35.082758+00:00
description:
  An OAuth 2.0 extension enabling task-oriented collaboration where a leading agent coordinates sub-agents through group-based token and message flows.


draft_name: draft-spm-lake-pqsuites
title: Post-Quantum EDHOC Cipher Suites
idea_type: extension
extracted_at: 2026-03-06T21:11:35.089102+00:00
description:
  Quantum-resistant cipher suites for EDHOC using ML-DSA for signatures and ML-KEM for key exchange with both signature-based and PSK-based authentication methods.


draft_name: draft-srijal-agents-policy
title: AGENTS.TXT Policy File
idea_type: protocol
extracted_at: 2026-03-06T21:11:38.337938+00:00
description:
  A plaintext policy file format for automated clients and crawlers with directives, hash verification, and mandatory failure-closed behavior for malformed files.


draft_name: draft-steckbeck-ua-conn-sec
title: Authorized Connection Policy Framework
idea_type: mechanism
extracted_at: 2026-03-06T21:11:38.345258+00:00
description:
  A framework allowing websites to declare authorized second- and third-party resources that user agents should or must connect to, reducing attack surface from compromised dependencies.


draft_name: draft-sun-zhang-iaip
title: Intent-based Agent Interconnection Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:38.363614+00:00
description:
  A protocol for dynamic interconnection among AI agents at gateway nodes based on semantic intent, with capability advertisement, intent matching, and routing mechanisms.


draft_name: draft-sz-dmsc-iaip
title: Intent-based Agent Interconnection Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:41.721026+00:00
description:
  A protocol for dynamic agent discovery and routing at interconnection gateways based on semantic intent and capability matching rather than static network addressing.


draft_name: draft-sz-dmsc-iaip
title: Capability Advertisement and Intent Resolution
idea_type: mechanism
extracted_at: 2026-03-06T21:11:41.721026+00:00
description:
  Mechanisms for agents to advertise capabilities and gateways to resolve and match intent queries against available agent capabilities.


draft_name: draft-takagi-srta-trinity
title: Structured Responsibility and Traceability Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:11:41.727220+00:00
description:
  A framework for ensuring safety and reliability in autonomously coordinating multi-agent AI systems through structured responsibility and traceability.


draft_name: draft-takagi-srta-trinity
title: Trinity Configuration for Agent Coordination
idea_type: pattern
extracted_at: 2026-03-06T21:11:41.727220+00:00
description:
  A role-based agent model with dynamic information flows (Filioque Command) for coordinating AI systems with theological Trinity analogy.


draft_name: draft-tejido-swp-core
title: SlimWire Protocol Core Binary Framing
idea_type: protocol
extracted_at: 2026-03-06T21:11:41.732691+00:00
description:
  A slim binary framing and envelope format for transport-independent agent/tool messaging with profile-based extensibility and machine-verifiable conformance.


draft_name: draft-templin-6man-mla
title: Multilink Local Address for Ad Hoc Networks
idea_type: extension
extracted_at: 2026-03-06T21:11:41.739252+00:00
description:
  A new IPv6 address type enabling nodes to autonomously assign topology-independent addresses in ad hoc networks without centralized delegation services.


draft_name: draft-templin-manet-inet-omni
title: AERO/OMNI MANET Internetworking Framework
idea_type: architecture
extracted_at: 2026-03-06T21:11:44.584187+00:00
description:
  A framework for connecting isolated mobile ad-hoc networks to fixed infrastructure using Automatic Extended Route Optimization and Overlay Multilink Network Interface technologies.


draft_name: draft-tiloca-lake-exporter-output-length
title: In-band Exporter Output Length Agreement for EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:11:44.591696+00:00
description:
  An EAD-based mechanism allowing EDHOC peers to negotiate output lengths for the EDHOC_Exporter interface during session establishment.


draft_name: draft-tojens-dhcp-option-concat-considerations
title: Flexible DHCP Option Concatenation Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:11:44.598586+00:00
description:
  An update to RFC 3396 relaxing mandatory concatenation support for all DHCP option types to reflect real-world deployment practices.


draft_name: draft-tong-network-agent-use-cases-in-6g
title: 6G Network AI Agent Use Cases and Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:11:44.603861+00:00
description:
  Definition of use cases and requirements for AI agents in 6G networks across connectivity and third-party application service scenarios.


draft_name: draft-vandoulas-aidp
title: Agent Interaction & Delegation Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:44.609254+00:00
description:
  A control-plane protocol enabling secure, auditable agent interactions with standardized mechanisms for intent expression, capability delegation, action execution, and result binding across heterogeneous domains.


draft_name: draft-verma-dmsc-nlip-notes
title: Natural Language Protocol for Agent Communication
idea_type: protocol
extracted_at: 2026-03-06T21:11:47.759695+00:00
description:
  Use generative AI to enable flexible agent-to-agent communication through natural language instead of strict APIs, reducing API proliferation and improving interoperability.


draft_name: draft-vinaysingh-awp-wellknown
title: Agent Workflow Protocol Well-Known Resource
idea_type: extension
extracted_at: 2026-03-06T21:11:47.765095+00:00
description:
  Define a standard well-known URI and JSON format for machines to discover website workflows (states, actions, events) enabling automated agent interaction without scraping.


draft_name: draft-wahl-scim-agent-schema
title: SCIM Agentic Identity Schema
idea_type: extension
extracted_at: 2026-03-06T21:11:47.770889+00:00
description:
  Extend SCIM protocol with a platform-neutral JSON schema for representing and transferring AI agent identities to enable agent authentication and authorization.


draft_name: draft-wang-hjs-accountability
title: HJS Accountability Layer for AI Agents
idea_type: architecture
extracted_at: 2026-03-06T21:11:47.776882+00:00
description:
  Provide an independent accountability layer with primitives for judgment, delegation, termination, and verification, using blockchain-anchored timestamps to create immutable evidence of agent responsibility chains.


draft_name: draft-wang-nmrg-magent-im
title: Protocol-Agnostic Multi-Agent Interaction Model
idea_type: architecture
extracted_at: 2026-03-06T21:11:47.782582+00:00
description:
  Define a protocol-agnostic data model for secure and efficient multi-agent interactions across autonomous network layers, enabling Level 4 network autonomy.


draft_name: draft-wang-ppm-ecdh-psi
title: ECDH-based Private Set Intersection
idea_type: protocol
extracted_at: 2026-03-06T21:11:51.547887+00:00
description:
  A two-party protocol using elliptic curve cryptography to compute set intersection while keeping non-intersecting records private from each participant.


draft_name: draft-wendt-stir-vesper
title: Verifiable STI Persona Framework with Notary Logging
idea_type: architecture
extracted_at: 2026-03-06T21:11:51.554878+00:00
description:
  A framework binding telephone number assignments to authorized entities through delegate certificates, authority tokens, and a notary agent that transparently logs RTU associations.


draft_name: draft-williams-http-bearer-extension
title: Automatic Bearer Token Fetching in HTTP 401/407
idea_type: protocol
extracted_at: 2026-03-06T21:11:51.561371+00:00
description:
  An improved HTTP authentication flow allowing user-agents to automatically retrieve Bearer tokens from a Security Token Service without PKCE or URI query parameter constraints.


draft_name: draft-willman-rtgwg-conduit-tunnels
title: CONDUIT Tunnel Fabric Management Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:51.568450+00:00
description:
  A protocol automating IPsec tunnel lifecycle management and health monitoring over heterogeneous WAN links, with quality metrics publication for SRv6-based traffic engineering.


draft_name: draft-wmz-nmrg-agent-ndt-arch
title: AI-Driven Network Operations with Digital Twin
idea_type: architecture
extracted_at: 2026-03-06T21:11:51.575337+00:00
description:
  An architecture integrating Network Digital Twin emulation with Agentic AI to enable autonomous network management that translates user intent into optimized strategies in a risk-free environment.


draft_name: draft-wolfe-faf-format
title: YAML-based project context format for AI tools
idea_type: format
extracted_at: 2026-03-06T21:11:54.665883+00:00
description:
  FAF standardizes software project metadata (architecture, conventions, dependencies, goals) in YAML format for consumption by AI development agents.


draft_name: draft-xie-ai-agent-multimodal
title: Agent-to-Agent multimodal protocol with QoS adaptation
idea_type: protocol
extracted_at: 2026-03-06T21:11:54.671670+00:00
description:
  Protocol enabling autonomous agents to negotiate and exchange heterogeneous data types (text, files, audio/video, sensor streams) with adaptive quality-of-service policies.


draft_name: draft-yan-a2a-device-agent-applicability
title: A2A protocol deployment for network device autonomy
idea_type: architecture
extracted_at: 2026-03-06T21:11:54.677282+00:00
description:
  Framework for applying Agent-to-Agent communication between controller and device agents to enable autonomous decision-making on network elements.


draft_name: draft-yang-dmsc-ioa-task-protocol
title: Internet of Agents Task Protocol for heterogeneous collaboration
idea_type: protocol
extracted_at: 2026-03-06T21:11:54.683043+00:00
description:
  Protocol supporting dynamic agent team formation, adaptive task coordination, and decentralized deployment across diverse agent architectures and network environments.


draft_name: draft-yang-ioa-protocol
title: Internet of Agents Protocol for heterogeneous agent collaboration
idea_type: protocol
extracted_at: 2026-03-06T21:11:54.688798+00:00
description:
  Layered protocol with extensible messaging enabling distributed collaboration among agents with diverse architectures, tools, and knowledge sources in 6G scenarios.


draft_name: draft-yao-agent-auth-considerations
title: OAuth-based Agent Authentication Framework
idea_type: extension
extracted_at: 2026-03-06T21:11:57.295796+00:00
description:
  Extension of OAuth protocol to support authentication and authorization workflows specifically designed for AI agents in Agent Communication Networks.


draft_name: draft-ye-problems-and-requirements-of-dns-for-ioa
title: DNS Evolution for Internet of Agents
idea_type: requirement
extracted_at: 2026-03-06T21:11:57.302154+00:00
description:
  Identification of DNS requirements and technical gaps necessary to support agent collaboration and discovery in Internet of Agents environments.


draft_name: draft-yl-agent-id-requirements
title: Universal AI Agent Digital Identity Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:11:57.323520+00:00
description:
  A cross-industry digital identity protocol for AI agents that enables secure, interoperable communication between agents, users, and tools.


draft_name: draft-yu-ai-agent-use-cases-in-6g
title: AI Agent Integration Requirements in 6G Networks
idea_type: requirement
extracted_at: 2026-03-06T21:11:57.329598+00:00
description:
  Specification of operational and technical requirements for deploying AI agents within 6G network infrastructure from operator perspectives.


draft_name: draft-yuan-rtgwg-security-agent-usecase
title: AI Network Security Agent
idea_type: architecture
extracted_at: 2026-03-06T21:12:00.746435+00:00
description:
  Intelligent software components using machine learning and behavioral analysis to provide adaptive, automated, and distributed network security with real-time threat detection and response.


draft_name: draft-yuan-rtgwg-traffic-agent-usecase
title: AI Network Traffic Optimization Agent
idea_type: architecture
extracted_at: 2026-03-06T21:12:00.753442+00:00
description:
  Autonomous learning entities that dynamically analyze real-time network status and adjust resources to optimize efficiency, QoS, and costs through adaptive routing and intelligent traffic steering.


draft_name: draft-yue-anima-agent-recovery-networks
title: Task-Oriented Multi-Agent Recovery Framework
idea_type: architecture
extracted_at: 2026-03-06T21:12:00.759701+00:00
description:
  A multi-agent collaboration framework enabling autonomous failure detection, inter-domain coordination, and intent-driven policy reconfiguration for fault recovery in converged mobile networks.


draft_name: draft-yue-moq-transporting-sensing-data
title: Media Over QUIC for Sensing Data Transport
idea_type: protocol
extracted_at: 2026-03-06T21:12:00.765198+00:00
description:
  Protocol mechanism leveraging QUIC's low-latency and multiplexing to efficiently transport large-scale real-time sensing data in 6G networks with adaptive fragmentation and end-to-end QoS awareness.


draft_name: draft-yusef-lamps-rfc7030-renewal-recommendation
title: Certificate Renewal Recommendation Extension
idea_type: extension
extracted_at: 2026-03-06T21:12:00.770599+00:00
description:
  Extension to RFC 7030 enabling servers to signal optimal certificate renewal timing to clients instead of relying on fixed percentage-based renewal triggers.


draft_name: draft-zeng-mcp-troubleshooting
title: MCP for Network Device Management
idea_type: architecture
extracted_at: 2026-03-06T21:12:04.691869+00:00
description:
  Map Model Context Protocol roles, primitives, and security model to network management domain enabling network devices as MCP servers and controllers as MCP clients.


draft_name: draft-zeng-mcp-troubleshooting
title: Intent-Based Network Troubleshooting Automation
idea_type: pattern
extracted_at: 2026-03-06T21:12:04.691869+00:00
description:
  Provide conversational, intent-driven automated network troubleshooting, configuration validation, and closed-loop remediation using LLM applications over MCP.


draft_name: draft-zeng-nmrg-mcp-usecases-requirements
title: MCP Network Management Architecture Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:12:04.699398+00:00
description:
  Define functional, security, and interoperability requirements for integrating Model Context Protocol into network management to enable AI agent discovery and invocation of network capabilities.


draft_name: draft-zeng-nmrg-mcp-usecases-requirements
title: LLM-Driven Network Operations Use Cases
idea_type: pattern
extracted_at: 2026-03-06T21:12:04.699398+00:00
description:
  Identify and systematize key network management use cases for AI agents including troubleshooting, measurement, security, and optimization.


draft_name: draft-zeng-opsawg-applicability-mcp-a2a
title: MCP and A2A Complementary Solutions for Network Management
idea_type: architecture
extracted_at: 2026-03-06T21:12:04.706688+00:00
description:
  Analyze functional gaps in NETCONF for AI-driven semantic translation, cross-domain orchestration, multi-agent consensus, and rapid DevOps iteration, positioning MCP and Agent-to-Agent as complementary solutions.


draft_name: draft-zeng-opsawg-applicability-mcp-a2a
title: MCP and NETCONF Coexistence Model
idea_type: pattern
extracted_at: 2026-03-06T21:12:04.706688+00:00
description:
  Provide implementation guidance and coexistence strategies for deploying Model Context Protocol alongside NETCONF in network management systems.


draft_name: draft-zeng-opsawg-llm-netconf-gap
title: Intent-Based Networking Protocol Gap Analysis
idea_type: requirement
extracted_at: 2026-03-06T21:12:04.713346+00:00
description:
  Systematically analyze gaps in existing south-bound protocols (NETCONF, RESTCONF, gNMI, MCP, A2A) for conversational, semantically-rich, multi-agent orchestration and identify extension points.


draft_name: draft-zhang-dmsc-ioa-semantic-interaction
title: Semantic Ontology Layer for Agent Interaction
idea_type: architecture
extracted_at: 2026-03-06T21:12:07.017305+00:00
description:
  A normative semantic framework using JSON-LD and RDF to enable deterministic interoperability across heterogeneous agent domains through shared ontology and negotiation procedures.


draft_name: draft-zhang-dmsc-mas-communication
title: Agent-Native Security Gap Analysis
idea_type: requirement
extracted_at: 2026-03-06T21:12:07.023109+00:00
description:
  Identifies security limitations of existing Internet protocols (TLS, HTTP, MQTT) for agent-specific requirements including dynamic identity, computation-bounded requests, and context integrity.


draft_name: draft-zhang-rtgwg-llmmoe-multicast
title: Multicast for LLM MoE Token Dispatching
idea_type: pattern
extracted_at: 2026-03-06T21:12:11.314578+00:00
description:
  Analysis of multicast use cases in Mixture of Experts architectures for efficient token routing and distribution across expert models.


draft_name: draft-zhang-rvp-problem-statement
title: Real-Virtual Agent Protocol (RVP)
idea_type: protocol
extracted_at: 2026-03-06T21:12:11.321538+00:00
description:
  Unified communication protocol enabling coordination between physical agents (robots, IoT, manufacturing systems) and digital agents (AI systems, virtual twins) through composite identity management and physical constraint integration.


draft_name: draft-zhao-ccamp-actn-optical-network-agent
title: AI-Based Network Management Agent Integration into ACTN
idea_type: architecture
extracted_at: 2026-03-06T21:12:11.327761+00:00
description:
  Extension of ACTN-based optical networks with AI-driven autonomous capabilities for perception, decision-making, and execution beyond traditional control functions.


draft_name: draft-zhao-nmop-network-management-agent
title: Network Management Agent (NMA) for Autonomous L4 Networks
idea_type: architecture
extracted_at: 2026-03-06T21:12:11.333294+00:00
description:
  AI-driven entity architecture that provides intent-based reasoning, autonomous planning, and context-aware decision-making for Level 4 autonomous networks while leveraging existing SDN controller capabilities.


draft_name: draft-zhao-nmrg-ai-agent-for-dtn
title: AI Agent Architecture for Digital Twin Networks
idea_type: architecture
extracted_at: 2026-03-06T21:12:11.339734+00:00
description:
  Integration of autonomous AI agents at component levels within Digital Twin Network architecture to enable intelligent and adaptive network management.


draft_name: draft-zl-agents-networking-architecture
title: Agents Networking Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:12:14.100690+00:00
description:
  Defines the core components and key interactions for a networking architecture supporting agent-based systems in enterprise and broadband environments.


draft_name: draft-zl-agents-networking-framework
title: Agent Networking Architecture for Enterprise/Broadband
idea_type: architecture
extracted_at: 2026-03-06T21:12:16.637705+00:00
description:
  Defines core components and interaction patterns for agent-based networking systems in enterprise and broadband environments.


draft_name: draft-zm-rtgwg-mcp-troubleshooting
title: MCP-based Intent-Driven Network Troubleshooting
idea_type: protocol
extracted_at: 2026-03-06T21:12:16.654054+00:00
description:
  Maps Model Context Protocol to network management enabling LLM-driven automated fault detection and resolution with device-to-device collaboration.


draft_name: draft-zw-rtgwg-mcp-network-mgmt
title: MCP Extensions for Network Equipment
idea_type: extension
extracted_at: 2026-03-06T21:12:16.660342+00:00
description:
  Defines capability tokens, tools, resources, and prompts to enable network devices as MCP servers and controllers as MCP clients.


draft_name: draft-zyyhl-agent-networks-framework
title: AI Agent Network Framework
idea_type: architecture
extracted_at: 2026-03-06T21:12:17.967037+00:00
description:
  A foundational architecture defining how AI agents organize, discover, and interact within networked systems.


draft_name: draft-aft-ai-traffic
title: AI Traffic Characterization Framework
idea_type: requirement
extracted_at: 2026-03-06T21:13:08.180715+00:00
description:
  Define specific network traffic characteristics and requirements for LLM training and inference workloads in federated datacenter deployments.


draft_name: draft-aft-ai-traffic
title: Federated AI Service Operations Model
idea_type: pattern
extracted_at: 2026-03-06T21:13:08.180715+00:00
description:
  Establish operational patterns for cooperating service providers to train, specialize, and serve LLM-based services across distributed datacenters.


draft_name: draft-akhavain-moussa-ai-network
title: Network Architecture for AI Training and Inference
idea_type: architecture
extracted_at: 2026-03-06T21:13:08.198641+00:00
description:
  Define network requirements and architecture patterns to support both AI model training and inference workloads with reliable data management and computation delivery.


draft_name: draft-allman-tcp-early-rexmt
title: Early Retransmit Mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:13:08.204209+00:00
description:
  Enable fast retransmission of lost segments in TCP and SCTP when the congestion window is small by reducing the required duplicate acknowledgment threshold.


draft_name: draft-altanai-aipref-realtime-protocol-bindings
title: AI Preferences Binding for SIP/SDP
idea_type: mechanism
extracted_at: 2026-03-06T21:13:11.001143+00:00
description:
  Define mechanisms to bind AI preference expressions to SIP headers and SDP attributes for negotiating AI-driven processing in real-time communications.


draft_name: draft-barnes-hpke-hpke
title: Hybrid Public Key Encryption Scheme
idea_type: protocol
extracted_at: 2026-03-06T21:13:14.725863+00:00
description:
  A modular encryption scheme combining asymmetric KEM, KDF, and AEAD primitives with multiple authentication variants for arbitrary-sized plaintexts.


draft_name: draft-bernardos-anima-fog-monitoring
title: Autonomic Orchestration for Dynamic Fog Resources
idea_type: architecture
extracted_at: 2026-03-06T21:13:14.733475+00:00
description:
  An autonomic approach to dynamically compose and manage service function chains across heterogeneous, volatile, and mobile fog computing resources.


draft_name: draft-bernardos-nmrg-agentic-network-optimization
title: Distributed Sensing with Network Optimization
idea_type: architecture
extracted_at: 2026-03-06T21:13:14.740044+00:00
description:
  A framework for optimizing distributed sensing tasks where multiple sensors contribute data processed by a centralized sensing service integrated with network communication.


draft_name: draft-calabria-bmwg-ai-fabric-inference-bench
title: AI Inference Fabric Benchmarking Methodology
idea_type: mechanism
extracted_at: 2026-03-06T21:13:14.746566+00:00
description:
  Vendor-independent benchmarking procedures and KPIs for evaluating Ethernet fabrics supporting disaggregated LLM inference architectures with metrics for TTFT, ITL, and throughput.


draft_name: draft-calabria-bmwg-ai-fabric-terminology
title: Unified AI Network Fabric Benchmarking Terminology
idea_type: requirement
extracted_at: 2026-03-06T21:13:14.752555+00:00
description:
  Standardized vocabulary for evaluating AI training and inference network fabrics, defining collective communication primitives, RDMA mechanisms, and AI-specific KPIs.


draft_name: draft-cgfabk-nmrg-ibn-generative-ai
title: Specialized AI Model Generation for Intent-Based Networking
idea_type: pattern
extracted_at: 2026-03-06T21:13:17.464065+00:00
description:
  Framework for creating targeted generative models optimized for Intent-Based Networking through specialization techniques.


draft_name: draft-chen-nmrg-ibn-management
title: Intent-Based Network Management Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:17.470612+00:00
description:
  Architecture for fully automated network management lifecycle using intent abstractions with five levels of autonomous capability.


draft_name: draft-chen-nmrg-multi-provider-inference-api
title: Multi-Provider AI Inference Extensions
idea_type: extension
extracted_at: 2026-03-06T21:13:17.477437+00:00
description:
  Extensions to OpenAI API standard enabling provider diversity, load balancing, failover, and capability negotiation in distributed inference.


draft_name: draft-chen-nmrg-semantic-inference-routing
title: Semantic Inference Routing Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:13:17.484058+00:00
description:
  Routing protocol for AI inference systems that makes routing decisions based on content-level semantic analysis rather than metadata alone.


draft_name: draft-chen-oauth-scope-agent-extensions
title: Structured OAuth Scope Syntax for Agent Permissions
idea_type: extension
extracted_at: 2026-03-06T21:13:17.490670+00:00
description:
  Extension to OAuth 2.0 scope parameter with structured syntax for fine-grained authorization of modular capability units in agent ecosystems.


draft_name: draft-chen-tsvwg-high-speed-data-express
title: High-elasticity high-throughput IP data express architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:21.244741+00:00
description:
  A reference architecture for wide-area massive data transmission leveraging IP network statistical multiplexing and elastic provisioning with differentiated service quality.


draft_name: draft-chen-tsvwg-high-speed-data-express
title: One-low three-high technical system for data express
idea_type: pattern
extracted_at: 2026-03-06T21:13:21.244741+00:00
description:
  A technical framework combining low latency with high throughput, high reliability, and high elasticity to support task-type data express services.


draft_name: draft-chu-oauth-as-attested-user-cert
title: OAuth 2.0 RAR extension for AS-attested user certificates
idea_type: extension
extracted_at: 2026-03-06T21:13:21.252624+00:00
description:
  An extension to Rich Authorization Requests enabling clients to request Authorization Servers include or bind Resource Owner public key certificates to Access Tokens for verifying delegated credentials.


draft_name: draft-chuyi-nmrg-agentic-network-inference
title: Agentic network architecture for multi-agent coordination
idea_type: architecture
extracted_at: 2026-03-06T21:13:21.260812+00:00
description:
  A network architecture and orchestration framework enabling efficient information exchange, cross-domain coordination, and multi-level inference for interconnected AI agents.


draft_name: draft-cowles-aee
title: Agent Envelope Exchange (AEE) JSON envelope format
idea_type: protocol
extracted_at: 2026-03-06T21:13:21.267942+00:00
description:
  A transport-independent minimal JSON envelope format with 14 defined fields providing message identity, typing, correlation, tracing, and priority for inter-agent communication.


draft_name: draft-cowles-volt
title: Verifiable Operations Ledger and Trace (VOLT) protocol
idea_type: protocol
extracted_at: 2026-03-06T21:13:21.275305+00:00
description:
  A tamper-evident execution trace protocol for AI agent workflows using SHA-256 hash chains and Evidence Bundles to provide cryptographic integrity verification of agent operations.


draft_name: draft-daviel-html-geo-tag
title: Geographic META Tags for HTML Documents
idea_type: mechanism
extracted_at: 2026-03-06T21:13:24.220289+00:00
description:
  Embedded META tags encode geographic coordinates (latitude, longitude, elevation) and civic location information in HTML documents to enable automated geographic resource discovery.


draft_name: draft-du-catalist-routing-considerations
title: Agent-to-Agent Overlay Routing
idea_type: architecture
extracted_at: 2026-03-06T21:13:24.232963+00:00
description:
  Routing mechanisms for AI agents communicating across domains through an overlay network layer above IP infrastructure, abstracting underlying network topology.


draft_name: draft-dunbar-agent-attachment
title: Agent Attachment Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:13:24.240690+00:00
description:
  A protocol enabling autonomous agents to securely attach to overlay network infrastructure with identity validation, capability exchange, and session establishment to edge nodes.


draft_name: draft-dunbar-neotec-ac-pe2pe-ucmp-applicability
title: Time-Scoped Dynamic UCMP Policy via YANG Models
idea_type: pattern
extracted_at: 2026-03-06T21:13:24.247936+00:00
description:
  Framework for applying IETF YANG data models to enforce dynamic, time-bounded unequal cost multipath policies across multiple network segments for edge cloud data exchanges.


draft_name: draft-dunbar-neotec-ac-te-applicability
title: AC/TE YANG Models for Edge AI Placement
idea_type: pattern
extracted_at: 2026-03-06T21:13:27.874734+00:00
description:
  Applies existing ACaaS and TE topology YANG data models to optimize AI model placement at edge cloud sites based on end-to-end network performance metrics.


draft_name: draft-dunbar-onsen-ac-pe2pe-ucmp-applicability
title: Cloud-Initiated Time-Bounded Network Policy Activation
idea_type: requirement
extracted_at: 2026-03-06T21:13:27.919756+00:00
description:
  Enables cloud systems to request time-scoped UCMP policies across network segments for dynamic, periodic data exchanges between distributed edge AI inference modules.


draft_name: draft-dunbar-onsen-ac-pe2pe-ucmp-applicability
title: Multi-Model YANG Composition for Dynamic Policies
idea_type: pattern
extracted_at: 2026-03-06T21:13:27.919756+00:00
description:
  Demonstrates mapping of AC, TE Topology, SR Policy, and QoS YANG models together to enforce bandwidth, latency, and availability constraints for edge cloud interconnection.


draft_name: draft-ecdh-psi
title: ECDH-PSI Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:13:27.933088+00:00
description:
  A private set intersection protocol using elliptic curve Diffie-Hellman where both parties mask data items with private keys and compute intersection over mutually masked datasets.


draft_name: draft-farzdusa-aipref-enduser
title: End User Impact Assessment Framework for AI Preference Signaling
idea_type: requirement
extracted_at: 2026-03-06T21:13:31.696852+00:00
description:
  A framework for evaluating how AI preference signaling standards affect end users across technological, legal, ethical, and governance dimensions, particularly regarding information access and data privacy.


draft_name: draft-han-anima-ai-asa
title: LLM-Enhanced Autonomic Service Agent Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:31.710118+00:00
description:
  Integration of Large Language Model-driven AI into ANIMA's Autonomic Service Agents to enhance device-built-in management functions while considering impacts on ASA communication protocols.


draft_name: draft-han-rtgwg-codeployment-pfc-fgfc
title: Fine-Grained Flow Control Mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:13:31.716138+00:00
description:
  A flow control mechanism that operates at flow/tenant granularity across data center and wide area networks to replace or complement PFC with intelligent congestion backpressure.


draft_name: draft-han-rtgwg-codeployment-pfc-fgfc
title: Cross-Domain Flow Control Collaboration Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:31.716138+00:00
description:
  A coordinated framework enabling flow control mechanisms in DCNs and WANs to interoperate for end-to-end lossless transmission.


draft_name: draft-he-yi-srv6ops-ipv6-enhancemnet-in-cloud-uc
title: Virtual In-Cloud Router as IPv6 Enhancement Agent
idea_type: architecture
extracted_at: 2026-03-06T21:13:31.722175+00:00
description:
  A virtual router deployed in cloud environments that acts as an agent supporting IPv6 enhancement techniques like SRv6-based service function chaining and IPv6 performance measurement.


draft_name: draft-herbert-fast
title: Firewall and Service Tickets Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:13:35.281807+00:00
description:
  A generic protocol for hosts to signal network nodes by attaching tickets to packets that grant traversal rights or indicate requested services.


draft_name: draft-housley-lamps-private-key-attest-attr
title: X.509 Private Key Possession Attribute
idea_type: extension
extracted_at: 2026-03-06T21:13:35.296899+00:00
description:
  An attribute for certificate subjects to provide a cryptographic statement of possession of their private key during certificate enrollment.


draft_name: draft-howe-sipcore-mcp-extension
title: SIP Extension for Model Context Protocol
idea_type: extension
extracted_at: 2026-03-06T21:13:35.303573+00:00
description:
  A SIP extension mechanism to negotiate and carry Model Context Protocol payloads during session establishment and mid-dialog exchanges.


draft_name: draft-hu-neotec-usecases-notc
title: Telco Cloud Orchestration with Dynamic Resource Scheduling
idea_type: architecture
extracted_at: 2026-03-06T21:13:35.309958+00:00
description:
  A framework for coordinating AI-based services across heterogeneous computing and network domains with elastic bandwidth provisioning and standardized cloud-network controller interfaces.


draft_name: draft-hw-protocol-agent
title: AI Agent Protocol Framework
idea_type: protocol
extracted_at: 2026-03-06T21:13:38.754974+00:00
description:
  Protocol mechanisms enabling networks to recognize and differentiate transmission requirements for multi-modal AI Agent traffic.


draft_name: draft-hw-protocol-agent
title: Multi-modal Data Quality Awareness
idea_type: requirement
extracted_at: 2026-03-06T21:13:38.754974+00:00
description:
  Network capability to classify and handle diverse quality-of-service requirements within single AI Agent traffic flows.


draft_name: draft-hy-srv6ops-sfc-in-cloud-uc
title: SRv6-based Service Function Chaining in Cloud
idea_type: architecture
extracted_at: 2026-03-06T21:13:38.762622+00:00
description:
  Architecture for implementing SFC using SRv6 segment routing in cloud environments via virtual router agents.


draft_name: draft-ietf-aipref-attach
title: HTTP Content Usage Preference Signaling
idea_type: extension
extracted_at: 2026-03-06T21:13:38.769455+00:00
description:
  Mechanism to attach AI usage preference declarations to HTTP content acquisition for expressing creator preferences on automated processing.


draft_name: draft-ietf-aipref-vocab
title: AI Usage Preference Vocabulary
idea_type: pattern
extracted_at: 2026-03-06T21:13:38.775788+00:00
description:
  Standardized vocabulary for expressing permissions and restrictions on digital asset usage by automated processing systems.


draft_name: draft-ietf-anima-brski-ae
title: Alternative Enrollment Protocols in BRSKI
idea_type: extension
extracted_at: 2026-03-06T21:13:38.781553+00:00
description:
  Extension to BRSKI enabling flexible certificate enrollment mechanisms beyond EST, such as CMP with authenticated self-contained signed objects.


draft_name: draft-ietf-core-oscore-edhoc
title: EDHOC-OSCORE combined handshake
idea_type: mechanism
extracted_at: 2026-03-06T21:13:41.843402+00:00
description:
  Optimization technique combining EDHOC key exchange with first OSCORE transaction to reduce round trips for establishing secured CoAP communication.


draft_name: draft-ietf-dnsop-dns-error-reporting
title: DNS error reporting mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:13:41.849835+00:00
description:
  Lightweight reporting system enabling validating resolvers to signal DNS resolution and validation failures back to authoritative server operators using extended DNS errors encoded in QNAMEs.


draft_name: draft-ietf-dnsop-dnssec-bootstrapping
title: Authenticated in-band DNSSEC bootstrapping
idea_type: mechanism
extracted_at: 2026-03-06T21:13:41.856521+00:00
description:
  In-band method allowing DNS operators to cryptographically publish and validate DNSSEC key parameters for zones without requiring out-of-band validation or parent-side delays.


draft_name: draft-ietf-dtn-dtnma
title: DTN Management Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:45.214875+00:00
description:
  A management architecture designed for challenged networks with long delays and frequent disruptions that supports asynchronous transport, autonomous local control, and minimal resource footprint.


draft_name: draft-ietf-dtn-dtnma
title: Management Protocol for Bundle Protocol Networks
idea_type: protocol
extracted_at: 2026-03-06T21:13:45.214875+00:00
description:
  A management protocol operating over the DTN Bundle Protocol that does not presume synchronized transport behavior.


draft_name: draft-ietf-grow-va
title: Virtual Aggregation for FIB Suppression
idea_type: mechanism
extracted_at: 2026-03-06T21:13:45.221707+00:00
description:
  A mechanism that reduces FIB size by an order of magnitude through selective RIB entry suppression with minimal path length penalty, deployable autonomously within an ISP without inter-ISP coordination.


draft_name: draft-ietf-httpbis-zstd-window-size
title: Zstandard Window Size Requirement for HTTP
idea_type: requirement
extracted_at: 2026-03-06T21:13:45.240493+00:00
description:
  Updates RFC8878 to mandate a specific window size limit for Zstandard content encoding in HTTP contexts to ensure interoperability across browsers and user agents.


draft_name: draft-ietf-idr-bgp-dpa
title: Destination Preference Attribute
idea_type: extension
extracted_at: 2026-03-06T21:13:47.158343+00:00
description:
  A new BGP path attribute enabling autonomous systems to specify globally transitive metrics for influencing return traffic paths and facilitating symmetric routing.


draft_name: draft-ietf-idr-bgp-metrics
title: Per-Hop BGP Metrics
idea_type: extension
extracted_at: 2026-03-06T21:13:47.164508+00:00
description:
  Extended per-hop metrics for BGP to enable implementation of common routing policies without complex ad-hoc configurations.


draft_name: draft-ietf-lake-edhoc
title: Ephemeral Diffie-Hellman Over COSE
idea_type: protocol
extracted_at: 2026-03-06T21:13:47.170000+00:00
description:
  A compact lightweight authenticated key exchange protocol providing mutual authentication, forward secrecy, and identity protection optimized for constrained environments.


draft_name: draft-ietf-pim-zeroconf-mcast-addr-alloc-ps
title: Zeroconf Multicast Address Allocation Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:13:50.604156+00:00
description:
  Defines functional and operational requirements for decentralized, zero-configuration multicast group address allocation with conflict detection and lease management.


draft_name: draft-ietf-pim-zeroconf-mcast-addr-alloc-ps
title: Link-layer Collision Avoidance in Multicast Allocation
idea_type: mechanism
extracted_at: 2026-03-06T21:13:50.604156+00:00
description:
  Addresses the problem of multicast address collisions at the link layer in zeroconf environments without central coordination.


draft_name: draft-ietf-sip-location-conveyance
title: SIP Location Information Extension
idea_type: extension
extracted_at: 2026-03-06T21:13:50.611427+00:00
description:
  Defines a SIP protocol extension to convey geographic location information between SIP entities and enable location-based routing decisions.


draft_name: draft-ietf-vwrap-authentication
title: VWRAP Application-Layer Authentication Model
idea_type: protocol
extracted_at: 2026-03-06T21:13:50.617562+00:00
description:
  Establishes an authentication mechanism for virtual world applications that verifies user credentials and grants capabilities for agent and asset control.


draft_name: draft-ietf-websec-mime-sniff
title: HTTP Media Type Sniffing Algorithm
idea_type: mechanism
extracted_at: 2026-03-06T21:13:50.623455+00:00
description:
  Specifies an algorithm for determining effective media types of HTTP responses that balances compatibility with legacy servers against security risks.


draft_name: draft-illyes-aipref-jafar
title: JSON format for automated HTTP client IP range publication
idea_type: protocol
extracted_at: 2026-03-06T21:13:53.713840+00:00
description:
  A standardized machine-readable JSON schema for web crawlers and AI bots to publicly disclose their IP address ranges.


draft_name: draft-illyes-rep-purpose
title: Purpose-based targeting in Robots Exclusion Protocol
idea_type: extension
extracted_at: 2026-03-06T21:13:53.720714+00:00
description:
  An extension to RFC 9309 that enables robots.txt rules to target automated clients based on their stated purpose rather than just user-agent matching.


draft_name: draft-improving-data-quality-tags
title: Special text tags for conversational AI training and inference
idea_type: pattern
extracted_at: 2026-03-06T21:13:53.727733+00:00
description:
  A tagging scheme that provides contextual and structural metadata to improve data quality and model accuracy in conversational AI systems.


draft_name: draft-ingles-eap-edhoc
title: EAP-EDHOC authentication method
idea_type: protocol
extracted_at: 2026-03-06T21:13:53.737884+00:00
description:
  Integration of the lightweight EDHOC key exchange protocol with the Extensible Authentication Protocol for efficient authenticated exchange.


draft_name: draft-intellinode-ai-semantic-contract
title: Semantic-driven traffic shaping contract for AI networks
idea_type: architecture
extracted_at: 2026-03-06T21:13:53.743980+00:00
description:
  A network contract mechanism allowing AI applications to communicate semantic metadata about tensor flows to enable fine-grained differentiated forwarding and resource allocation.


draft_name: draft-intellinode-cats-in-network-scheduling
title: In-Network Intelligent Scheduling for AI Clusters
idea_type: mechanism
extracted_at: 2026-03-06T21:13:57.194842+00:00
description:
  FPGA and programmable switch integration enables closed-loop perception-inference-decision-execution scheduling aware of tensor semantics and heterogeneous computing capabilities.


draft_name: draft-irtf-nmrg-ai-deploy
title: Distributed AI Service Deployment Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:13:57.203048+00:00
description:
  Framework for configuring networks and systems to distribute AI inference across commodity servers, edge devices, and cloud with client selection considerations.


draft_name: draft-it-aipref-attachment
title: HTTP Content Usage Preference Signaling
idea_type: extension
extracted_at: 2026-03-06T21:13:57.210598+00:00
description:
  HTTP mechanism allowing content creators to signal preferences about automated system consumption of their content.


draft_name: draft-jadoon-nmrg-agentic-ai-autonomous-networks
title: Agentic AI Augmentation of IP Protocol Stack
idea_type: architecture
extracted_at: 2026-03-06T21:13:57.216801+00:00
description:
  Architectural principles introducing AI agents as first-class entities at each IP suite layer with coordinated agent controllers to enable goal-driven closed-loop autonomous operations.


draft_name: draft-jain-lisp-network-ai-infra
title: LISP-Based Unified Control Plane for AI Infrastructure Scaling
idea_type: architecture
extracted_at: 2026-03-06T21:13:57.222503+00:00
description:
  LISP control plane mechanisms supporting scale-up, scale-out, and scale-across operations with EID-to-RLOC mapping and VPN segmentation for high-bandwidth and ultra-low-latency collective operations.


draft_name: draft-jennings-ai-mcp-over-moq
title: Model Context Protocol over MOQT
idea_type: protocol
extracted_at: 2026-03-06T21:14:00.927620+00:00
description:
  Defines a protocol mapping for transporting MCP messages (resources, tools, prompts, notifications) over MOQT with priority management and delivery guarantees.


draft_name: draft-jennings-mcp-over-moqt
title: Model Context Protocol over MOQT
idea_type: protocol
extracted_at: 2026-03-06T21:14:00.934446+00:00
description:
  Defines a protocol mapping for transporting MCP messages and procedures for establishing and maintaining MCP sessions over MOQT transport.


draft_name: draft-jimenez-tbd-robotstxt-update
title: AI-Specific Robots.txt Extensions
idea_type: extension
extracted_at: 2026-03-06T21:14:00.940110+00:00
description:
  Updates robots.txt syntax to distinguish AI crawlers from traditional bots and differentiate between training and inference access policies.


draft_name: draft-kartha-internet20-ainative
title: HTTP+AI Extension for AI-Aware Communication
idea_type: extension
extracted_at: 2026-03-06T21:14:00.946142+00:00
description:
  Extends HTTP with AI-aware primitives to enable protocol-level AI interaction and intent-based routing.


draft_name: draft-kartha-internet20-ainative
title: Model Resolution Network (MRN)
idea_type: architecture
extracted_at: 2026-03-06T21:14:00.946142+00:00
description:
  Creates an AI-native service discovery mechanism analogous to DNS for distributed model discovery and resolution.


draft_name: draft-kartha-internet20-ainative
title: AI-Native Web Architecture
idea_type: architecture
extracted_at: 2026-03-06T21:14:00.946142+00:00
description:
  Proposes a decentralized internet architecture that treats AI models as first-class network entities with intent-aware routing and semantic communication.


draft_name: draft-keller-aipref-vocab
title: Standardized TDM/AI Opt-Out Vocabulary
idea_type: pattern
extracted_at: 2026-03-06T21:14:04.572403+00:00
description:
  A machine-readable vocabulary defining three standardized use cases (TDM, AI Training, Generative AI Training) that enables consistent expression of digital asset usage restrictions across different opt-out mechanism implementations.


draft_name: draft-kim-nmrg-2nmai5g
title: AI-Driven Autonomous Network Management for B5G
idea_type: architecture
extracted_at: 2026-03-06T21:14:04.589338+00:00
description:
  Framework integrating SOAR-based security orchestration, AI-driven network management, and intent-based autonomous operations to maintain security quality in Beyond 5G networks.


draft_name: draft-klrc-aiagent-auth
title: AI Agent Authentication Framework
idea_type: architecture
extracted_at: 2026-03-06T21:14:04.596744+00:00
description:
  A framework applying WIMSE workload identity and OAuth 2.0 standards to establish authentication and authorization for AI agent interactions, identifying gaps for future standardization.


draft_name: draft-kwiatkowski-tls-ecdhe-mlkem
title: Post-Quantum Hybrid ECDHE-MLKEM Key Agreement for TLS 1.3
idea_type: extension
extracted_at: 2026-03-06T21:14:04.603824+00:00
description:
  Defines three hybrid key agreement schemes combining ECDHE with post-quantum ML-KEM for TLS 1.3 to provide quantum-resistant security.


draft_name: draft-li-cats-aisemantic-contract
title: Semantic-Driven Traffic Shaping Contract
idea_type: mechanism
extracted_at: 2026-03-06T21:14:08.457817+00:00
description:
  Applications explicitly communicate minimum necessary semantics of AI tensor flows to the network, which commits to differentiated forwarding and resource allocation based on predefined rules and real-time state.


draft_name: draft-li-cats-idn
title: Intelligence Delivery Network (IDN)
idea_type: architecture
extracted_at: 2026-03-06T21:14:08.464614+00:00
description:
  A geographically distributed architecture that deploys heterogeneous inference models across servers and intelligently selects them to serve inference requests based on latency, privacy, and scalability requirements.


draft_name: draft-li-cats-intellinode-network-scheduling
title: IntelliNode In-Network Intelligent Scheduling
idea_type: mechanism
extracted_at: 2026-03-06T21:14:08.470518+00:00
description:
  A mechanism that integrates FPGAs and programmable switch ASICs to create a rapid data-plane closed loop for perception-inference-decision-execution, enabling tensor-aware and heterogeneous-compute-aware scheduling without traditional probes and controllers.


draft_name: draft-li-cdn-node-selection-enhancement
title: Dynamic CDN Node Selection via Anycast and QUIC
idea_type: mechanism
extracted_at: 2026-03-06T21:14:08.476489+00:00
description:
  An enhanced CDN node selection mechanism where nodes dynamically advertise or withdraw anycast routes based on real-time load and link quality, with QUIC enabling clients to make informed selection decisions.


draft_name: draft-li-individual-inip
title: In-Network Inference Protocol (INIP)
idea_type: protocol
extracted_at: 2026-03-06T21:14:08.482940+00:00
description:
  A lightweight protocol enabling data plane devices (switches, DPUs, SmartNICs) to perform inference tasks without affecting core forwarding, using a centralized control plane for model deployment and minimal data plane execution.


draft_name: draft-li-iotops-intelligent-security
title: Information-Centric Network Security Framework
idea_type: architecture
extracted_at: 2026-03-06T21:14:11.531047+00:00
description:
  A unified security optimization system for IoT networks that addresses naming, data exchange, and caching layers to protect content privacy without additional overhead.


draft_name: draft-li-spring-rdma-multicast-over-srv6
title: SRv6 RDMA Multicast Transport
idea_type: extension
extracted_at: 2026-03-06T21:14:11.538390+00:00
description:
  SRv6 extensions enabling RDMA Reliable Connection traffic to traverse multicast trees using per-receiver BTH modifications and hop-by-hop ACK aggregation while preserving point-to-point QP semantics.


draft_name: draft-liao-aipref-autoctl-core
title: Machine-Readable Automation Control Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:14:11.544370+00:00
description:
  A protocol for servers to specify machine-readable permissions governing AI-driven web automation activities including HTTP method restrictions and purpose requirements.


draft_name: draft-liao-aipref-autoctl-ext
title: Granular Automation Control Extensions
idea_type: extension
extracted_at: 2026-03-06T21:14:11.550223+00:00
description:
  Protocol extensions providing rate limiting, technology restrictions, API permissions, session requirements, and asset annotations for fine-grained control over automated interactions.


draft_name: draft-liu-agent-protocol-over-moq
title: Agent-to-Agent Communication Framework over MoQ
idea_type: protocol
extracted_at: 2026-03-06T21:14:11.556160+00:00
description:
  A structured protocol for low-latency communication between autonomous agents over Media over QUIC with application-layer framing supporting session management and data fragmentation.


draft_name: draft-liu-rtgwg-mdt-in-high-bdp
title: Terminal-driven intelligent parameter optimization for massive data transmission
idea_type: mechanism
extracted_at: 2026-03-06T21:14:15.426654+00:00
description:
  A mechanism for end hosts to dynamically adjust local configurations (disk I/O, NIC bandwidth, TCP buffer) to optimize transmission performance in high BDP networks.


draft_name: draft-liu-rtgwg-mdt-in-high-bdp
title: MDT framework for nighttime bandwidth utilization
idea_type: architecture
extracted_at: 2026-03-06T21:14:15.426654+00:00
description:
  An architecture enabling efficient use of idle network bandwidth during off-peak hours to deliver large data transfers with guaranteed completion times.


draft_name: draft-lkspa-wimse-verifiable-geo-fence
title: Hardware-rooted cryptographic geofencing for workload identity
idea_type: architecture
extracted_at: 2026-03-06T21:14:15.434320+00:00
description:
  A high-assurance profile that replaces IP-based location verification with cryptographically verifiable geofencing using hardware-rooted attestation to prevent unauthorized location spoofing.


draft_name: draft-lopez-lake-edhoc-psk
title: Pre-Shared Key authentication for EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:14:15.441282+00:00
description:
  A PSK-based authentication method for the EDHOC key exchange protocol with defined message flows and security considerations.


draft_name: draft-madhavan-aipref-displaybasedpref
title: Standardized vocabulary for content usage preferences by AI/search crawlers
idea_type: pattern
extracted_at: 2026-03-06T21:14:15.454329+00:00
description:
  A structured vocabulary format allowing content creators to declare permissions and restrictions on how their digital content can be used by search engines and AI systems.


draft_name: draft-mangrove-workgroup-mangrove
title: Unified Internet Topology and Routing Model
idea_type: architecture
extracted_at: 2026-03-06T21:14:18.602795+00:00
description:
  A system that aggregates diverse data sources (traceroute, BGP tables, IP allocations, broadband measurements) into a comprehensive, unified representation of Internet topology and routing behavior for global visibility.


draft_name: draft-mao-rtgwg-agent-comm-protocol-gap-analysis
title: Cross-device Communication Protocol Gap Analysis for Network AI Agents
idea_type: requirement
extracted_at: 2026-03-06T21:14:18.608791+00:00
description:
  Analyzes the applicability and gaps of existing networking and AI protocols for enabling communication and collaboration between AI agents deployed on different network devices.


draft_name: draft-mapmw-task-discovery
title: Task Discovery Architecture for Agentic Networks
idea_type: architecture
extracted_at: 2026-03-06T21:14:18.613899+00:00
description:
  Defines a multi-layer architectural framework enabling task owners to publish structured task cards to a platform where autonomous agents discover tasks, negotiate execution terms, and coordinate multi-agent collaboration.


draft_name: draft-mcp-over-moqt
title: Model Context Protocol over MOQT
idea_type: protocol
extracted_at: 2026-03-06T21:14:18.619652+00:00
description:
  Defines the transport mapping of the Model Context Protocol (MCP) onto Media over QUIC Transport (MOQT) for efficient, low-latency communication between language model applications and external data sources.


draft_name: draft-mozley-aidiscovery
title: AI Agent Discovery Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:14:21.586294+00:00
description:
  Requirements and considerations for mechanisms enabling AI agents to discover and identify other agents in distributed deployments.


draft_name: draft-mzhang-nfsv4-recursively-setting
title: Recursive Attribute Setting for NFSv4
idea_type: mechanism
extracted_at: 2026-03-06T21:14:21.592191+00:00
description:
  A mechanism to set attributes recursively across all files and subdirectories in a parent directory, enabling near-data computing optimizations.


draft_name: draft-nandakumar-ai-agent-moq-transport
title: MOQT Protocol Abstraction for Agent Communication
idea_type: architecture
extracted_at: 2026-03-06T21:14:25.456951+00:00
description:
  A protocol abstraction layer that maps request-response and streaming patterns from diverse agent protocols onto MOQT's publish/subscribe model to enable unified inter-agent communication.


draft_name: draft-nandakumar-ai-agent-moq-transport
title: MOQT Binding for A2A and MCP Protocols
idea_type: extension
extracted_at: 2026-03-06T21:14:25.456951+00:00
description:
  Concrete mappings demonstrating how agent discovery, task delegation, tool access, and resource sharing protocols can be implemented over MOQT transport.


draft_name: draft-ounsworth-lamps-cms-dhkem
title: DHKEM in CMS
idea_type: mechanism
extracted_at: 2026-03-06T21:14:25.470587+00:00
description:
  A mechanism to wrap Ephemeral-Static and Elliptic Curve Diffie-Hellman as KEM interfaces within Cryptographic Message Syntax for unified cryptographic protocol design.


draft_name: draft-pan-aqm-pie
title: PIE Queue Management Scheme
idea_type: mechanism
extracted_at: 2026-03-06T21:14:25.477645+00:00
description:
  A lightweight active queue management algorithm using proportional-integral control to regulate buffer occupancy and maintain target latency without per-flow state.


draft_name: draft-papon-lake-pq-edhoc
title: Post-Quantum EDHOC with Mixed Authentication
idea_type: protocol
extracted_at: 2026-03-06T21:14:28.164252+00:00
description:
  A quantum-resistant variant of EDHOC protocol using signature-based authentication for initiators and KEM-based authentication for responders to reduce message complexity.


draft_name: draft-patwhite-aauth
title: Agent Authorization Grant for OAuth 2.1
idea_type: extension
extracted_at: 2026-03-06T21:14:28.171646+00:00
description:
  An OAuth 2.1 extension enabling confidential AI agents with long-lived identities to request user consent and obtain access tokens through polling, SSE, or WebSocket with natural-language scoped descriptions.


draft_name: draft-peterson-mimi-idprover
title: Identifier Proof-of-Possession Mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:14:28.177850+00:00
description:
  A mechanism for third-party services to cryptographically prove and attest the association between a communications platform user identity and their telephone number or other Service Independent Identifiers.


draft_name: draft-ramadan-mboned-sonar
title: SONAR: Verifiable Multicast Delivery with Cryptoeconomic Accountability
idea_type: protocol
extracted_at: 2026-03-06T21:14:28.184047+00:00
description:
  A protocol combining IP multicast efficiency, VRF-based sampling, blockchain attestations, and ALTA authentication to enable verifiable multicast delivery claims without trusted intermediaries.


draft_name: draft-reilly-sentinel-protocol
title: Sentinel Evidence Package (SEP)
idea_type: architecture
extracted_at: 2026-03-06T21:14:31.899872+00:00
description:
  A dual-layer tamper-evident package binding payload digests, provenance metadata, signatures, blockchain timestamp proofs, and resolvable identifiers to create independently verifiable receipts across the AI lifecycle.


draft_name: draft-reilly-sentinel-protocol
title: Blockchain-Anchored Integrity for AI Provenance
idea_type: mechanism
extracted_at: 2026-03-06T21:14:31.899872+00:00
description:
  Method for establishing integrity and auditability of AI datasets, training jobs, and inference outputs through blockchain timestamp proofs integrated with cryptographic signatures.


draft_name: draft-romm-aipref-contentsignals
title: Content Signals Vocabulary for Automated Processing
idea_type: pattern
extracted_at: 2026-03-06T21:14:31.907082+00:00
description:
  Three-category taxonomy enabling parties to express preferences about how digital assets are used by automated processing systems.


draft_name: draft-ruan-spring-priority-flow-control-sid
title: SRv6 Flow Control Extension for WAN
idea_type: extension
extracted_at: 2026-03-06T21:14:31.919402+00:00
description:
  Extension of SRv6 to enable flow control and congestion notification capabilities in wide area networks supporting RDMA-based distributed AI workloads.


draft_name: draft-serafin-lake-ta-hint
title: Trust Anchor Hints Transport in EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:14:35.288944+00:00
description:
  A format for encoding and transporting hints about trusted third-party trust anchors during EDHOC key exchange to enable efficient trust anchor discovery.


draft_name: draft-shi-moq-kvcache
title: KVCache Distribution over MoQT
idea_type: protocol
extracted_at: 2026-03-06T21:14:35.296417+00:00
description:
  A protocol specification for transmitting and caching LLM key-value cache artifacts over publish-subscribe transport to enable local reuse and reduce inference prefill computation.


draft_name: draft-silver-aipref-vocab-substitutive
title: AI Substitutive Use Preference Category
idea_type: extension
extracted_at: 2026-03-06T21:14:35.302377+00:00
description:
  A vocabulary extension for expressing user preferences about whether digital assets can be used to generate AI-substitutive outputs during inference-time processing.


draft_name: draft-song-lake-ra
title: Remote Attestation in EDHOC
idea_type: extension
extracted_at: 2026-03-06T21:14:35.307511+00:00
description:
  Integration of the RATS remote attestation architecture into EDHOC to enable device attestation as part of the lightweight key exchange flow.


draft_name: draft-song-tsvwg-camp
title: Consistency-Aware Multipath Transport
idea_type: protocol
extracted_at: 2026-03-06T21:14:39.588556+00:00
description:
  A multipath transport protocol that manages heterogeneous modalities (video, audio, text) across separate streams while maintaining inter-modal arrival consistency for low-latency interactive LLM systems.


draft_name: draft-song-tsvwg-camp
title: Differentiated Multimodal Stream Encapsulation
idea_type: mechanism
extracted_at: 2026-03-06T21:14:39.588556+00:00
description:
  A mechanism to enable per-modality stream management and scheduling policies within multipath transport to handle heterogeneous QoS requirements.


draft_name: draft-tan-ccamp-uonaco-problem-statement
title: Unified Optical Network and AI Compute Orchestration
idea_type: architecture
extracted_at: 2026-03-06T21:14:39.594793+00:00
description:
  A coordinated control plane architecture that integrates optical transport network awareness with AI compute scheduler visibility to optimize distributed training and inference workloads.


draft_name: draft-templin-6man-aero3
title: Automatic Extended Route Optimization over OMNI Interfaces
idea_type: protocol
extracted_at: 2026-03-06T21:14:39.600895+00:00
description:
  An IPv6-based routing and mobility service using Neighbor Discovery over virtual overlay multilink interfaces to support secure path selection, multinet traversal, and mobility management across heterogeneous network types.


draft_name: draft-templin-intarea-aero
title: Automatic Extended Route Optimization over OMNI Interfaces
idea_type: protocol
extracted_at: 2026-03-06T21:14:39.607036+00:00
description:
  An IPv6-based routing and mobility service using Neighbor Discovery with unique-local addressing over virtual overlay multilink interfaces to support secure multilink path selection and mobility across diverse network topologies.


draft_name: draft-templin-intarea-aero2
title: Automatic Extended Route Optimization over OMNI Interfaces
idea_type: protocol
extracted_at: 2026-03-06T21:14:39.612809+00:00
description:
  An IPv6-based routing and mobility service using Neighbor Discovery over virtual overlay multilink interfaces with support for both Provider-Aggregated and Provider-Independent addressing schemes.


draft_name: draft-templin-manet-inet
title: MANET Internetworking Problem Statement
idea_type: requirement
extracted_at: 2026-03-06T21:14:43.183232+00:00
description:
  Identifies gaps and challenges in connecting autonomous mobile ad-hoc networks to fixed infrastructure like the Internet.


draft_name: draft-thomson-aipref-sup
title: Short Usage Preference Strings Format
idea_type: protocol
extracted_at: 2026-03-06T21:14:43.190424+00:00
description:
  Defines a simple machine-readable format for content creators to signal preferences about automated consumption of their content.


draft_name: draft-tiloca-lake-app-profiles
title: EDHOC Application Profile Coordination
idea_type: pattern
extracted_at: 2026-03-06T21:14:43.196956+00:00
description:
  Defines mechanisms for discovering and coordinating the use of application profiles that specify EDHOC parameters and processing rules.


draft_name: draft-tiloca-lake-app-profiles
title: CBOR-based EDHOC Profile Representation
idea_type: protocol
extracted_at: 2026-03-06T21:14:43.196956+00:00
description:
  Establishes a canonical CBOR format for describing, distributing, and storing EDHOC application profiles.


draft_name: draft-tjw-dbound2-problem-statement
title: DNS-based Administrative Boundary Verification
idea_type: requirement
extracted_at: 2026-03-06T21:14:43.206895+00:00
description:
  Addresses the problem that organizational boundaries cannot be reliably confirmed through DNS, hindering client security inferences.


draft_name: draft-traffic-analysis-and-network-mode-mapping
title: LLM-based Traffic Classification and Resource Mapping
idea_type: architecture
extracted_at: 2026-03-06T21:14:46.758209+00:00
description:
  Framework using large language models to automatically extract multi-dimensional traffic features from pcap data and dynamically map traffic patterns to computing-storage-transmission resource allocation strategies.


draft_name: draft-tschofenig-lamps-nonce-cmp-est
title: Nonce Provisioning for Attestation Evidence Freshness in CSRs
idea_type: mechanism
extracted_at: 2026-03-06T21:14:46.765673+00:00
description:
  Mechanism for RA/CA to issue nonces to end entities for inclusion in attestation Evidence within Certificate Signing Requests to demonstrate freshness in CMP and EST protocols.


draft_name: draft-tsyrulnikov-rats-attested-inference-receipt
title: Attested Inference Receipt (AIR) Profile
idea_type: protocol
extracted_at: 2026-03-06T21:14:46.772698+00:00
description:
  COSE_Sign1 envelope carrying CWT claims profiled per EAT framework that binds model identity, input/output hashes, and platform attestation metadata for auditable confidential AI inference events.


draft_name: draft-tu-nmrg-blockchain-trusted-protocol
title: Blockchain-based Trusted Protocol for 6G Networks
idea_type: protocol
extracted_at: 2026-03-06T21:14:46.779251+00:00
description:
  Blockchain-based protocol framework designed to establish trust in sixth-generation intelligent communication networks.


draft_name: draft-vattaparambil-positioning-of-poa
title: Power of Attorney (PoA) Authorization Framework
idea_type: pattern
extracted_at: 2026-03-06T21:14:46.785642+00:00
description:
  Decentralized subgranting authorization technique enabling a principal to delegate limited capabilities to an agent for constrained time and context via self-contained signed documents.


draft_name: draft-vaughan-aipref-vocab
title: AI Training Content Preference Vocabulary
idea_type: pattern
extracted_at: 2026-03-06T21:14:49.624650+00:00
description:
  A standardized vocabulary enabling rightsholders to express granular preferences about AI training use of their content through metadata and content-delivery protocols.


draft_name: draft-vesely-dmarc-mlm-transform
title: Author-Centric DMARC MLM From: Header Restoration
idea_type: mechanism
extracted_at: 2026-03-06T21:14:49.632583+00:00
description:
  A mechanism to restore original From: headers rewritten by mailing list managers through author-side signing measures, enabling DMARC compliance without header rewriting.


draft_name: draft-wang-cats-innetwork-infer
title: Decentralized LLM Inference Coordination Framework
idea_type: architecture
extracted_at: 2026-03-06T21:14:49.639700+00:00
description:
  An open framework for executing LLM inference across mutually untrusted participants with network and transport mechanisms supporting sequential dependencies, large intermediate activations, and tight latency requirements.


draft_name: draft-wang-lisp-ai-agent
title: LISP for AI Agent Networking
idea_type: extension
extracted_at: 2026-03-06T21:14:53.208306+00:00
description:
  Extend LISP protocol with context-aware mapping and agent-centric communication primitives to provide location transparency, mobility, and logical isolation for distributed AI systems across cloud-edge-endpoint environments.


draft_name: draft-williams-netmod-lm-hierarchy-topology
title: YANG Model for Hierarchical LM Coordination
idea_type: architecture
extracted_at: 2026-03-06T21:14:53.216438+00:00
description:
  Define a YANG data model and reference architecture for orchestrating cooperative inference across heterogeneous language models of varying sizes using secure messaging, request escalation, token-based authorization, and pluggable trust models.


draft_name: draft-wu-sidr-aspa-validation-signaling
title: BGP Extended Community for ASPA Validation State
idea_type: extension
extracted_at: 2026-03-06T21:14:53.223057+00:00
description:
  Define a BGP opaque extended community to signal AS_PATH validation results based on ASPA within IBGP, enabling policy-driven route decision-making based on upstream provider authorization validation.


draft_name: draft-xsaopig-nmop-service-flow-modal-mapping
title: ALTO-based Service Flow to Network Resource Mapping
idea_type: architecture
extracted_at: 2026-03-06T21:14:56.997467+00:00
description:
  Framework using ALTO protocol to map service flow characteristics to network modal resources in SDN-based multi-modal computing networks with dynamic optimization.


draft_name: draft-xsaopig-nsttlp-traffic-labeling
title: Network Service Type-Aware Traffic Labeling Protocol
idea_type: protocol
extracted_at: 2026-03-06T21:14:57.004749+00:00
description:
  Protocol mechanism for embedding standardized service type identifiers into packet headers (IPv4/IPv6, MPLS, Ethernet) to enable intelligent traffic recognition and policy-based forwarding.


draft_name: draft-xu-rtgwg-fare-in-sun
title: Fully Adaptive Routing Ethernet for AI Scale-Up Networks
idea_type: extension
extracted_at: 2026-03-06T21:14:57.010821+00:00
description:
  Extension of WECMP load-balancing to support ultra-high-throughput, ultra-low-latency routing for MoE-based LLM inference across hundreds of GPUs in scale-up networks.


draft_name: draft-xu-sidrops-asrank-vulnerabilities
title: ASRank Structural Vulnerability Analysis
idea_type: requirement
extracted_at: 2026-03-06T21:14:57.017531+00:00
description:
  Security analysis identifying structural vulnerabilities in the ASRank algorithm for inferring AS business relationships, demonstrating manipulation risks under adversarial conditions.


draft_name: draft-yan-anima-brski-cle
title: Certificateless BRSKI Enrollment for Constrained IoT
idea_type: protocol
extracted_at: 2026-03-06T21:14:57.024121+00:00
description:
  BRSKI enrollment framework using Key Encapsulation Mechanism instead of certificates for Class 1 constrained devices with quantum-safe algorithm support and lightweight credential issuance.


draft_name: draft-yang-alto-multi-domain
title: Multi-Domain ALTO Service Discovery
idea_type: architecture
extracted_at: 2026-03-06T21:15:01.094511+00:00
description:
  Framework for ALTO clients and servers to discover and communicate across multiple autonomous network domains with different trust boundaries.


draft_name: draft-yang-alto-multi-domain
title: Cross-Domain Path Optimization
idea_type: mechanism
extracted_at: 2026-03-06T21:15:01.094511+00:00
description:
  Mechanism to optimize application traffic paths that span multiple autonomous networks using ALTO information from multiple domains.


draft_name: draft-yang-dynamic-ecn-threshold
title: Dynamic ECN Threshold Coupling
idea_type: mechanism
extracted_at: 2026-03-06T21:15:01.102557+00:00
description:
  Algorithm to dynamically adjust ECN marking thresholds in real-time based on instantaneous buffer allocation to prevent premature or late congestion marking.


draft_name: draft-yang-nmrg-mcp-nm
title: MCP-Based Network Management Plane
idea_type: architecture
extracted_at: 2026-03-06T21:15:01.109873+00:00
description:
  Architecture for applying Model Context Protocol to expose network management operations, capabilities, and AI integration through standardized MCP interfaces.


draft_name: draft-yang-spring-sr-policy-intelligent-routing
title: Quality-Based SR Policy Path Selection
idea_type: mechanism
extracted_at: 2026-03-06T21:15:01.117276+00:00
description:
  Intelligent routing method that selects among SR Policy candidate paths based on network quality metrics in MPLS environments.


draft_name: draft-yang-srv6ops-intelligent-routing
title: Quality-Based SR Policy Path Selection for SRv6
idea_type: mechanism
extracted_at: 2026-03-06T21:15:01.123947+00:00
description:
  Intelligent routing method that selects among SR Policy candidate paths based on network quality metrics in IPv6 Segment Routing environments.


draft_name: draft-yao-tsvwg-cco-requirement-and-analysis
title: Network-Assisted Collective Communication Optimization
idea_type: architecture
extracted_at: 2026-03-06T21:15:03.748927+00:00
description:
  Enable efficient One-to-Many, Many-to-One, and Many-to-Many collective operations in AI clusters by offloading collective acceleration to the network while maintaining RDMA compatibility.


draft_name: draft-yc-ipv6-for-ioa
title: IPv6 Requirements for Internet of Agents
idea_type: requirement
extracted_at: 2026-03-06T21:15:03.755665+00:00
description:
  Define foundational capabilities and evolutionary requirements that IPv6 must provide to support hundreds of billions of intelligent agents at scale.


draft_name: draft-zeng-mcp-network-measurement
title: MCP-based Network Measurement Framework
idea_type: architecture
extracted_at: 2026-03-06T21:15:03.762047+00:00
description:
  Enable AI-assisted, natural language-driven network measurement and fault diagnosis by implementing network devices as MCP servers and controllers as MCP clients.


draft_name: draft-zeng-mcp-network-mgmt
title: MCP Extensions for Network Equipment Management
idea_type: extension
extracted_at: 2026-03-06T21:15:03.767928+00:00
description:
  Define minimal extensions to Model Context Protocol allowing network equipment to act as MCP servers with new capability tokens, tools, resources, and prompts for AI-driven network management.


draft_name: draft-zhang-rtgwg-ai-agents-measurement
title: Measurement Agent Communication Protocol
idea_type: requirement
extracted_at: 2026-03-06T21:15:06.725266+00:00
description:
  Define communication protocol requirements and use cases for autonomous measurement agents deployed on network devices.


draft_name: draft-zhang-rtgwg-ai-agents-troubleshooting
title: Troubleshooting Agent Communication Protocol
idea_type: requirement
extracted_at: 2026-03-06T21:15:06.731729+00:00
description:
  Establish communication protocol requirements and use cases for autonomous troubleshooting agents operating on network devices.


draft_name: draft-zhang-rtgwg-multicast-requirements-gaps-aidc
title: Multicast Optimization for AI Data Centers
idea_type: requirement
extracted_at: 2026-03-06T21:15:06.738520+00:00
description:
  Identify multicast requirements and capability gaps for efficient point-to-multipoint data transmission in large language model training and inference workloads.


draft_name: draft-zhao-anima-automatic-congestion-relief
title: Intelligent Automatic Congestion Relief Mechanism
idea_type: mechanism
extracted_at: 2026-03-06T21:15:06.744975+00:00
description:
  Enable self-healing network responses to fiber optic failures through intelligent traffic analysis and dynamic regulation.


draft_name: draft-zhao-detnet-enhanced-use-cases
title: Deterministic Network Scaling Requirements
idea_type: requirement
extracted_at: 2026-03-06T21:15:06.751657+00:00
description:
  Define use cases and network requirements for scaling deterministic networking to industrial IoT, high-quality video, intelligent computing, and ISAC-enabled factories.


draft_name: draft-zhao-nmrg-ai-agent-for-ndt
title: AI Agent Architecture for Network Digital Twin
idea_type: architecture
extracted_at: 2026-03-06T21:15:09.283692+00:00
description:
  An architecture integrating AI agents with network digital twin technology to enable autonomous network understanding and decision-making.


draft_name: draft-zhu-anima-service-intent
title: Service Intent Model for Autonomic Networks
idea_type: mechanism
extracted_at: 2026-03-06T21:15:09.290478+00:00
description:
  A structured semantic model and format for expressing service-level objectives and constraints across network, compute, and storage resources in autonomic domains.


draft_name: draft-zm-rtgwg-mcp-network-measurement
title: MCP-based Network Measurement Framework
idea_type: architecture
extracted_at: 2026-03-06T21:15:09.298850+00:00
description:
  A framework enabling natural language-driven, AI-assisted network measurement by treating devices as MCP servers and controllers as MCP clients for real-time monitoring and fault diagnosis.


draft_name: draft-zw-nmrg-mcp-network-mgmt
title: MCP Extensions for Network Equipment Management
idea_type: extension
extracted_at: 2026-03-06T21:15:09.304114+00:00
description:
  Extensions to the Model Context Protocol enabling network equipment to act as MCP servers with new capability tokens, tools, resources, and prompts for AI-driven management.


draft_name: draft-zw-opsawg-mcp-network-mgmt
title: MCP Extensions for Network Equipment
idea_type: extension
extracted_at: 2026-03-06T21:15:10.893119+00:00
description:
  Define capability tokens, tools, resources, prompts, and error codes to enable network equipment to operate as MCP servers for AI-driven management.


draft_name: w3c-web-user-agents
title: User Agent Duties Framework
idea_type: requirement
extracted_at: 2026-03-06T21:15:10.899862+00:00
description:
  Establish collective standards and duties that web user agents must uphold to serve their users rather than other constituencies.


# gaps

topic: Multi-Agent Consensus Protocols
category: A2A protocols
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While A2A protocols exist, there's no standardized approach for multiple AI agents to reach consensus on shared decisions or conflicting objectives. This becomes critical when agents must coordinate on resource allocation or policy enforcement across domains.

evidence:
  High overlap in A2A protocols suggests fragmented approaches without consensus mechanisms, critical for autonomous system coordination


topic: Agent Behavioral Verification
category: AI safety/alignment
severity: critical
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No drafts address how to verify that deployed AI agents are actually behaving according to their declared policies or specifications. This creates a gap between policy definition and runtime assurance.

evidence:
  Only 47 drafts on AI safety despite 433 total drafts, and behavioral verification is fundamental to autonomous system trust


topic: Cross-Protocol Agent Migration
category: Agent discovery/reg
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Agents need to migrate between different network protocols, domains, or infrastructure providers while maintaining state and identity. Current drafts focus on registration but not migration continuity.

evidence:
  Agent registration servers exist but no migration protocols, essential for dynamic edge deployments mentioned in technical ideas


topic: Real-Time Agent Rollback Mechanisms
category: Autonomous netops
evidence: 114 autonomous netops drafts but no rollback mechanisms, critical for production network safety
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  When autonomous agents make incorrect decisions, there's no standardized protocol for quickly rolling back their actions across distributed systems. This is critical for network operations and safety.


topic: Agent Resource Accounting and Billing
category: new
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Multi-tenant environments need standardized ways to track and bill for AI agent resource consumption across compute, network, and storage. Current drafts define resources but not accounting mechanisms.

evidence:
  Edge AI placement and resource types defined but no economic models for multi-tenant agent deployments


topic: Federated Agent Learning Privacy
category: Policy/governance
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While federated architectures exist, there's insufficient specification for privacy-preserving agent learning that prevents data leakage between federated participants during model updates.

evidence:
  Federated AI architectures mentioned but private data training requirements suggest unresolved privacy challenges


topic: Agent Capability Negotiation
category: A2A protocols
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  Agents need standardized protocols to negotiate their capabilities and limitations before collaboration. Tool enumeration exists but not dynamic capability negotiation during runtime.

evidence:
  Tool enumeration APIs exist but no dynamic capability negotiation, needed for heterogeneous agent coordination


topic: Cross-Domain Agent Audit Trails
category: Agent identity/auth
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  When agents operate across multiple domains or organizations, there's no standardized format for maintaining audit trails that satisfy different regulatory requirements simultaneously.

evidence:
  Cross-domain identity management exists but audit requirements vary by jurisdiction, creating compliance gaps


topic: Agent Failure Cascade Prevention
category: AI safety/alignment
severity: critical
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No protocols exist to prevent agent failures from cascading across interconnected autonomous systems. This is critical as agent interdependencies increase in production deployments.

evidence:
  Limited AI safety focus (47 drafts) despite high interconnectivity implied by A2A protocols and autonomous netops


topic: Human Override Standardization
category: Human-agent interaction
severity: high
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  While human-agent interaction exists, there's no standardized protocol for human operators to override autonomous agent decisions across different vendor implementations and domains.

evidence:
  Only 33 human-agent interaction drafts suggest limited focus on human control mechanisms essential for safe deployment


topic: Agent Performance Benchmarking
category: new
severity: medium
analyzed_at: 2026-03-04T00:06:32.798946+00:00
description:
  No standardized metrics or protocols exist for comparing agent performance across different implementations, making it difficult to evaluate and select appropriate agents for specific tasks.

evidence:
  AI workload characterization mentioned but no performance comparison frameworks, needed for agent selection and optimization


# generated_drafts

gap_topic: behavior verification
draft_name: draft-ai-agent-behavior-verification-protocol-00
title: Agent Behavior Verification Protocol (ABVP)
status: draft
created_at: 2026-03-04T00:17:40.729113+00:00
abstract:
  Autonomous AI agents operate with increasing independence across network environments, making it critical to verify that their actual behavior matches expected policies and constraints. Existing approaches focus primarily on identity verification or single-point attestation, leaving gaps in continuous behavior monitoring and cross-protocol verification. This document defines the Agent Behavior Verification Protocol (ABVP), which provides standardized mechanisms for capturing, validating, and attesting to agent behavior patterns in real-time. ABVP enables continuous trustworthiness assessment through cryptographic behavior proofs, supports multi-vendor attestation environments, and integrates with existing authorization frameworks. The protocol addresses the fundamental challenge of moving from 'who is this agent?' to 'is this agent behaving as expected?' across diverse operational contexts. ABVP complements existing agent authentication and authorization protocols by providing the missing behavior verification layer essential for autonomous agent deployment at scale.

outline_json:
  {"title": "Agent Behavior Verification Protocol (ABVP)", "abstract": "Autonomous AI agents operate with increasing independence across network environments, making it critical to verify that their actual behavior matches expected policies and constraints. Existing approaches focus primarily on identity verification or single-point attestation, leaving gaps in continuous behavior monitoring and cross-protocol verification. This document defines the Agent Behavior Verification Protocol (ABVP), which provides standardized mechanisms for capturing, validating, and attesting to agent behavior patterns in real-time. ABVP enables continuous trustworthiness assessment through cryptographic behavior proofs, supports multi-vendor attestation environments, and integrates with existing authorization frameworks. The protocol addresses the fundamental challenge of moving from 'who is this agent?' to 'is this agent behaving as expected?' across diverse operational contexts. ABVP complements existing agent authentication and authorization protocols by providing the missing behavior verification layer essential for autonomous agent deployment at scale.", "sections": [{"title": "Introduction", "summary": "Establishes the need for standardized behavior verification in autonomous agent ecosystems. Outlines how ABVP fits within the broader agent protocol landscape and complements existing authentication/authorization mechanisms.", "key_ideas": ["Continuous Trustworthiness Verification", "Behavioral Trustworthiness Assessment", "Dynamic Behavior Authentication"]}, {"title": "Terminology", "summary": "Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.", "key_ideas": ["Verifiable Agent Behavior Attestation", "Cryptographic Proof-Based Autonomy"]}, {"title": "Problem Statement", "summary": "Details the gaps in current agent verification approaches, focusing on the inability to verify ongoing behavior versus static identity. Describes challenges with autonomous agents that modify their behavior over time.", "key_ideas": ["Agent Reasoning Trace Capture", "Real-Time Task Adaptability", "Multi-Vendor TEE Attestation"]}, {"title": "ABVP Architecture and Components", "summary": "Defines the core architectural components including Behavior Attestation Engines, Verification Registries, and Trust Anchor Management. Describes how these components interact to provide continuous behavior verification.", "key_ideas": ["Five Enforcement Pillars with Typed Schemas", "Public Registry Enrollment Mode", "DNS TXT Records for Agent Identity and Keys"]}, {"title": "Behavior Capture and Attestation Mechanisms", "summary": "Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.", "key_ideas": ["TPM Hardware Attestation Headers", "Cryptographic Identity Verification", "Agent Reasoning Trace Capture"]}, {"title": "Protocol Integration and Bindings", "summary": "Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.", "key_ideas": ["AIPREF Vocabulary Protocol Alignment", "Post-Discovery Authorization Handshake", "JOSE DVS Extension"]}, {"title": "Verification Workflows and Enforcement", "summary": "Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.", "key_ideas": ["Delegation Evidence Verification via Token-Bound Certificates", "Public Key Derived HMAC", "Congestion Control Behavior Definitions"]}, {"title": "Security Considerations", "summary": "Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.", "key_ideas": ["Multi-Vendor TEE Attestation", "Cryptographic Proof-Based Autonomy"]}, {"title": "IANA Considerations", "summary": "Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.", "key_ideas": ["Five Enforcement Pillars with Typed Schemas", "AIPREF Vocabulary Protocol Alignment"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 8446", "RFC 5280", "RFC 7519", "RFC 9052", "draft-birkholz-verifiable-agent-conversations", "draft-aylward-aiga-1"], "informative_refs": ["RFC 6749", "RFC 8259", "RFC 9110", "draft-aylward-daap-v2", "draft-chen-agent-decoupled-authorization-model", "draft-berlinai-vera", "draft-chen-ai-agent-auth-new-requirements", "draft-condrey-rats-witnessd-enrollment", "draft-drake-email-tpm-attestation", "draft-bastian-jose-dvs", "draft-bastian-jose-pkdh", "draft-barney-caam", "draft-altanai-aipref-realtime-protocol-bindings"], "terminology": {"Behavior Attestation": "A cryptographically signed assertion about an agent's observed behavior patterns over a specified time period", "Verification Proof": "Cryptographic evidence that demonstrates an agent's compliance with specified behavioral constraints", "Behavior Trace": "A structured record of agent actions, decisions, and reasoning processes that can be cryptographically verified", "Trust Anchor": "A root source of trust for behavior verification, typically backed by hardware attestation or multi-party consensus", "Behavior Policy": "A formal specification of expected agent behavior patterns and constraints", "Verification Registry": "A distributed system for storing and validating behavior attestations and verification proofs"}, "target_wg": "lake", "intended_status": "standards-track"}

sections_json:
  ["The proliferation of autonomous AI agents across network environments has introduced a fundamental challenge in distributed systems: verifying that agents behave according to their intended design and declared policies throughout their operational lifecycle. Traditional authentication and authorization mechanisms, as defined in frameworks like OAuth 2.0 [RFC6749] and established in agent-specific protocols [draft-aylward-aiga-1], primarily focus on identity verification and initial access control decisions. However, these approaches are insufficient for autonomous agents that operate independently over extended periods, modify their behavior based on learning, and interact across multiple administrative domains without continuous human oversight.\n\nCurrent verification approaches leave critical gaps in ongoing behavioral assurance. While existing protocols can answer \"who is this agent?\" through identity attestation, they cannot adequately address \"is this agent behaving as expected?\" during operation. This limitation becomes particularly problematic when agents exhibit Dynamic Behavior Authentication requirements, where the agent's behavioral patterns change over time based on learning algorithms, environmental adaptation, or policy updates. The absence of standardized Behavioral Trustworthiness Assessment mechanisms means that authorization decisions cannot incorporate an agent's actual behavioral history, leading to either overly restrictive policies that limit legitimate agent autonomy or overly permissive policies that create security risks.\n\nThe Agent Behavior Verification Protocol (ABVP) addresses these limitations by providing a standardized framework for Continuous Trustworthiness Verification that extends beyond initial authentication to ongoing behavioral validation. ABVP complements existing agent authentication protocols by introducing behavior attestation mechanisms that capture, verify, and attest to agent behavior patterns using cryptographic proofs. The protocol leverages hardware-backed attestation capabilities, similar to those used in verifiable agent conversations [draft-birkholz-verifiable-agent-conversations], while extending the verification scope from message integrity to comprehensive behavior pattern validation.\n\nABVP integrates with existing authorization frameworks by providing behavior verification inputs that enhance access control decisions. Rather than replacing current agent protocols, ABVP serves as a behavior verification layer that can be bound to existing communication protocols and authorization systems. The protocol enables verification registries to maintain distributed records of agent behavior attestations, allowing multiple parties to contribute to and benefit from behavioral trustworthiness assessments. This approach supports multi-vendor environments where agents from different providers must demonstrate behavioral compliance to shared policies and constraints.\n\nThe key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here. This document assumes familiarity with cryptographic attestation concepts, JSON-based data structures [RFC8259], and existing agent protocol frameworks.", "This document uses terminology from several domains including cryptographic attestation, autonomous agent systems, and distributed verification protocols. The key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.\n\n**Agent**: An autonomous software entity that operates independently to achieve specified goals, potentially across multiple network environments and administrative domains. Agents may modify their behavior over time through learning, adaptation, or policy updates as defined in [draft-aylward-aiga-1].\n\n**Behavior Attestation**: A cryptographically signed assertion about an agent's observed behavior patterns over a specified time period. Behavior attestations provide verifiable evidence of agent actions, decisions, and compliance with established policies, extending the concept of verifiable agent conversations [draft-birkholz-verifiable-agent-conversations] to broader behavioral patterns.\n\n**Behavior Policy**: A formal specification of expected agent behavior patterns and constraints, expressed as machine-readable rules that define acceptable and unacceptable agent actions. Behavior policies serve as the normative baseline against which agent behavior is measured and verified.\n\n**Behavior Trace**: A structured, chronologically ordered record of agent actions, decisions, and reasoning processes that can be cryptographically verified for integrity and authenticity. Behavior traces form the evidentiary foundation for behavior attestations and MUST be tamper-evident through cryptographic linking as specified in [RFC9052].\n\n**Trust Anchor**: A root source of trust for behavior verification, typically backed by hardware attestation mechanisms, trusted execution environments, or multi-party consensus protocols. Trust anchors provide the cryptographic foundation for establishing the authenticity and integrity of behavior attestations, similar to certificate authorities in PKI systems [RFC5280].\n\n**Verification Proof**: Cryptographic evidence that demonstrates an agent's compliance with specified behavioral constraints, generated through zero-knowledge proofs, hash chains, or other cryptographic mechanisms. Verification proofs enable third parties to validate agent behavior without requiring access to sensitive operational details, supporting the cryptographic proof-based autonomy model [draft-berlinai-vera].\n\n**Verification Registry**: A distributed system for storing, indexing, and validating behavior attestations and verification proofs. The registry provides a queryable interface for behavior verification while maintaining appropriate privacy controls and access restrictions based on authorization frameworks such as OAuth 2.0 [RFC6749].\n\n**Behavior Attestation Engine (BAE)**: A trusted component responsible for continuously monitoring agent behavior, generating behavior traces, and producing cryptographically signed behavior attestations. The BAE operates within a trusted execution environment or relies on hardware security modules to ensure the integrity of the attestation process.", "Current agent verification systems primarily focus on establishing \"who\" an agent is through identity and capability attestation, but fail to address the fundamental question of \"how\" an agent behaves over time. Traditional approaches, as described in existing frameworks [RFC6749] and emerging agent protocols [draft-birkholz-verifiable-agent-conversations], concentrate on static verification points such as initial authentication, capability declarations, and single-point attestations. While these mechanisms successfully establish agent identity and initial trustworthiness, they create significant gaps in ongoing behavioral assurance as agents operate with increasing autonomy across distributed network environments.\n\nThe challenge becomes more acute when considering autonomous agents that inherently modify their behavior patterns based on environmental feedback, learning algorithms, or Real-Time Task Adaptability requirements [draft-cui-ai-agent-task]. Existing attestation systems, including advanced approaches like Multi-Vendor TEE Attestation (M-TACE) [draft-aylward-aiga-1], excel at verifying the integrity of agent code and initial state but cannot validate whether an agent's runtime behavior adheres to expected policies after deployment. This creates a verification gap where an agent may pass initial attestation checks while subsequently exhibiting behaviors that violate operational constraints, security policies, or ethical guidelines without detection.\n\nFurthermore, current verification approaches lack standardized mechanisms for continuous behavior monitoring across heterogeneous environments. Agent Reasoning Trace Capture techniques [draft-birkholz-verifiable-agent-conversations] provide valuable insights into decision-making processes but do not establish cryptographic proofs of behavioral compliance that can be verified by third parties. The absence of standardized Behavior Traces and Verification Proofs means that each deployment environment must develop proprietary monitoring solutions, leading to fragmented trust models and limited interoperability between agent ecosystems.\n\nThe temporal dimension of agent behavior verification presents additional challenges that existing protocols do not adequately address. Static verification mechanisms cannot account for the dynamic nature of autonomous agents that adapt their strategies, modify their interaction patterns, or evolve their decision-making processes over operational lifetimes. Without continuous Behavior Attestation capabilities, network operators and service providers cannot maintain confidence in agent trustworthiness beyond initial deployment, creating significant risks for mission-critical applications and cross-organizational agent interactions.\n\nCurrent authorization frameworks also lack integration points for ongoing behavioral verification, focusing instead on capability-based access control determined at authentication time. The absence of standardized Behavior Policy enforcement mechanisms means that agents operating within their assigned capabilities may still exhibit problematic behaviors that violate implicit operational expectations or emergent security requirements. This gap becomes particularly problematic in multi-tenant environments where agents from different organizations interact, requiring mutual assurance of behavioral compliance that extends beyond simple identity verification.\n\nThe scalability challenges of behavior verification compound these issues, as existing approaches do not provide efficient mechanisms for aggregating behavioral evidence across distributed deployments or enabling third-party verification of agent conduct. Without standardized Verification Registry systems and Trust Anchor mechanisms, each verification attempt requires independent evidence collection and validation, creating computational and administrative overhead that limits practical deployment of comprehensive behavior monitoring in large-scale autonomous agent environments.", "The Agent Behavior Verification Protocol (ABVP) architecture consists of three primary components that work together to provide continuous behavior verification for autonomous AI agents. These components establish a comprehensive framework for capturing, validating, and attesting to agent behavior patterns in real-time across diverse operational environments. The architecture is designed to be vendor-neutral, protocol-agnostic, and compatible with existing agent authentication and authorization frameworks as defined in [RFC6749] and referenced in [draft-aylward-aiga-1].\n\nThe Behavior Attestation Engine (BAE) serves as the core component responsible for capturing agent behavior traces and generating cryptographic attestations. Each BAE MUST maintain a secure execution environment that continuously monitors agent actions, decisions, and reasoning processes without interfering with agent operation. The BAE generates behavior traces using structured formats based on [RFC8259] and creates cryptographically signed attestations using mechanisms defined in [RFC9052]. These attestations follow the Five Enforcement Pillars with Typed Schemas pattern, providing comprehensive coverage across authorization enforcement, data handling compliance, operational boundary adherence, communication protocol compliance, and resource utilization monitoring. The BAE SHOULD integrate with hardware-based Trusted Execution Environments (TEEs) when available to provide additional attestation integrity guarantees as specified in existing RATS frameworks.\n\nVerification Registries provide distributed storage and validation services for behavior attestations and verification proofs generated by BAEs. These registries operate using a federated trust model similar to the Public Registry Enrollment Mode, enabling cross-organizational behavior verification without requiring direct trust relationships between all participants. Each registry MUST validate the cryptographic integrity of incoming attestations, verify the authenticity of the attesting BAE, and maintain tamper-evident logs of all verification activities. Verification Registries support both real-time queries for immediate behavior verification and batch processing for historical behavior analysis. The registries expose standardized APIs that allow verifying parties to retrieve behavior attestations, validate verification proofs, and assess agent trustworthiness based on historical behavior patterns.\n\nTrust Anchor Management provides the foundational trust infrastructure that enables the entire ABVP ecosystem to function reliably. Trust anchors serve as root sources of trust for behavior verification and are typically backed by hardware attestation mechanisms, multi-party consensus systems, or established certificate authorities as defined in [RFC5280]. The Trust Anchor Management component MUST provide mechanisms for trust anchor discovery, validation, and lifecycle management including key rotation and revocation. Following the DNS TXT Records approach for agent identity distribution, trust anchor information MAY be published using DNS infrastructure to enable scalable trust anchor discovery across organizational boundaries. Trust Anchor Management also defines the policies and procedures for establishing new trust anchors, managing trust relationships between different ABVP deployments, and handling trust anchor compromise or revocation scenarios.\n\nThe interaction between these components creates a continuous behavior verification loop that provides ongoing assurance of agent trustworthiness. When an agent performs actions, the associated BAE captures these activities as behavior traces and generates signed attestations that are stored in Verification Registries. Verifying parties can query these registries to obtain current and historical behavior attestations, validate them against relevant trust anchors, and make informed decisions about agent trustworthiness. This architecture supports both centralized deployments within single organizations and federated deployments spanning multiple organizations, enabling behavior verification across the full spectrum of autonomous agent operational scenarios. The modular design allows organizations to implement components incrementally while maintaining interoperability with existing agent infrastructure and protocols such as those defined in [draft-birkholz-verifiable-agent-conversations].", "The ABVP behavior capture subsystem defines standardized mechanisms for recording agent actions, decisions, and reasoning processes in a cryptographically verifiable format. Agent implementations MUST generate behavior traces that capture sufficient detail to enable meaningful verification against behavioral policies. These traces MUST include timestamped records of agent actions, input stimuli, decision reasoning (as specified in [draft-birkholz-verifiable-agent-conversations]), and any policy evaluations performed by the agent. The behavior capture mechanism SHOULD be implemented as close to the agent's core reasoning engine as possible to minimize opportunities for tampering or selective reporting.\n\nBehavior traces MUST be structured as JSON objects [RFC8259] containing mandatory fields for agent identity, timestamp, action type, input context, and reasoning chain. The reasoning chain field leverages the agent reasoning trace capture mechanisms defined in [draft-birkholz-verifiable-agent-conversations] to provide transparency into the agent's decision-making process. Each trace entry MUST be signed using the agent's cryptographic identity as established through hardware-backed verification systems [draft-aylward-aiga-1]. Implementations MAY compress or summarize behavior traces for efficiency while maintaining cryptographic integrity through merkle tree structures or similar authenticated data structures.\n\nThe attestation generation process transforms behavior traces into cryptographically signed behavior attestations that can be independently verified by third parties. Attestation engines MUST create attestations in JSON Web Signature (JWS) format [RFC7515] using keys derived from or backed by hardware trust anchors. For implementations with Trusted Platform Module (TPM) support, attestations SHOULD include TPM-backed signatures following patterns similar to those defined for email attestation in [draft-drake-email-tpm-attestation]. The attestation payload MUST include a hash of the complete behavior trace, the evaluation period, applicable behavior policies, and compliance assertions.\n\nHardware attestation integration provides the foundational trust layer for ABVP by anchoring behavior attestations to tamper-resistant hardware. Agents operating on platforms with Trusted Execution Environment (TEE) capabilities MUST generate attestations from within the TEE to ensure behavior trace integrity. The attestation process MUST establish a cryptographic chain of trust from the hardware root of trust through the agent's runtime environment to the behavior attestation itself. This chain enables verifiers to confirm not only that the attestation is authentic but also that the underlying behavior capture mechanism has not been compromised.\n\nAttestation formats MUST support both real-time streaming attestations for continuous verification and batch attestations for periodic compliance reporting. Streaming attestations provide immediate behavior verification but require more computational overhead, while batch attestations enable efficient verification of longer behavior patterns. Implementations SHOULD support attestation aggregation mechanisms that allow multiple related behavior traces to be combined into a single attestation without losing verification granularity. All attestations MUST include sufficient metadata to enable policy evaluation and MUST be resistant to replay attacks through appropriate nonce and timestamp mechanisms as specified in [RFC8446] for cryptographic freshness.", "ABVP is designed to operate as a complementary layer alongside existing agent protocols and authorization frameworks, providing behavior verification capabilities without disrupting established communication patterns. The protocol integrates through standardized extension points and binding mechanisms that allow existing agent infrastructures to incrementally adopt behavior verification. ABVP bindings MUST be implemented in a manner that preserves backward compatibility with agents that do not support behavior verification while providing enhanced trust capabilities for ABVP-enabled environments.\n\nThe protocol defines three primary integration patterns: inline verification where behavior attestations are embedded directly in protocol messages, out-of-band verification where behavior proofs are transmitted through separate channels, and registry-based verification where behavior attestations are stored in distributed verification registries for asynchronous validation. For OAuth 2.0 [RFC6749] and similar authorization frameworks, ABVP extends token-based flows by including behavior verification claims within JWT tokens [RFC7519] or as additional attestation headers. The Behavior Attestation Engine generates verification proofs that reference the agent's recent behavior traces and embeds these within the authorization context, enabling authorization servers to make access decisions based on both identity and behavioral compliance.\n\nIntegration with Trusted Execution Environment (TEE) based agent frameworks leverages hardware attestation capabilities to anchor behavior verification in secure enclaves. ABVP bindings for TEE environments utilize the JOSE DVS extension mechanisms to provide derived verification signatures that combine hardware attestation with behavior proofs. The protocol supports integration with existing verifiable agent conversation frameworks [draft-birkholz-verifiable-agent-conversations] by extending conversation attestation to include behavioral compliance assertions. When integrated with agent discovery protocols, ABVP provides post-discovery authorization handshake capabilities that validate behavioral constraints before permitting tool execution or authority delegation.\n\nProtocol message formats utilize CBOR Web Token (CWT) structures [RFC9052] for compact behavior attestations in bandwidth-constrained environments, while supporting JSON-based attestation formats [RFC8259] for web-oriented integrations. ABVP bindings MUST specify the transport-specific mechanisms for behavior verification, including TLS 1.3 [RFC8446] extension points for embedding behavior proofs in secure channels and X.509 [RFC5280] certificate extensions for long-term behavior attestation storage. The protocol defines standard header fields and message extensions that enable behavior verification across HTTP-based agent communications, WebSocket connections, and message queue systems.\n\nBehavior policy alignment with standardized vocabularies such as AIPREF enables consistent behavior verification across multi-vendor agent environments. ABVP protocol bindings support dynamic policy negotiation where agents and verification entities can establish mutually acceptable behavior constraints and attestation formats during protocol handshake. The integration framework provides hooks for custom behavior verification logic while maintaining interoperability through standardized attestation formats and verification procedures. Implementation guidance specifies how existing agent frameworks can incrementally adopt ABVP capabilities, starting with passive behavior logging and progressing to active behavior enforcement and attestation generation.", "This section defines the operational workflows that enable continuous behavior verification for autonomous agents. ABVP supports three primary verification patterns: real-time verification for immediate trust decisions, batch verification for historical compliance assessment, and dispute resolution for handling verification conflicts. Each workflow integrates with existing authorization frameworks while providing the behavioral assurance layer necessary for autonomous agent operations.\n\nReal-time verification workflows enable immediate assessment of agent behavior during active operations. When an agent initiates actions that require behavioral validation, the Behavior Attestation Engine MUST generate a verification proof that demonstrates compliance with applicable behavior policies. The verification proof SHALL be constructed using Public Key Derived HMAC mechanisms as specified in [draft-bastian-jose-pkdh], allowing verifiers to authenticate behavior attestations using only public key information from the agent's JWS tokens. Resource servers implementing ABVP MUST validate these proofs against registered behavior policies before authorizing agent actions. The real-time workflow supports integration with OAuth 2.0 [RFC6749] access tokens through delegation evidence verification, enabling Resource Servers to verify behavior attestations using AS-attested keys bound to access tokens as described in [draft-chu-oauth-as-attested-user-cert].\n\nBatch verification workflows provide mechanisms for historical behavior analysis and compliance reporting. Verification Registries MUST support batch processing of behavior traces covering extended time periods, enabling policy compliance assessment across multiple operational contexts. The batch verification process SHALL generate aggregated verification proofs that demonstrate sustained compliance with behavior policies over time. Batch workflows MUST implement congestion control mechanisms consistent with vendor-neutral behavior definitions for AI fabric environments to prevent verification processing from impacting operational performance. Verification results MUST be stored in structured formats using JSON [RFC8259] encoding and signed using CBOR Object Signing and Encryption (COSE) [RFC9052] to ensure integrity and non-repudiation.\n\nDispute resolution workflows address scenarios where behavior verification results are contested or inconsistent across multiple verification sources. When verification conflicts arise, the dispute resolution process MUST invoke multi-party attestation mechanisms involving relevant Trust Anchors to establish authoritative behavior assessments. The dispute resolution workflow SHALL generate resolution evidence that includes cryptographic proofs from multiple verification sources and a consensus determination of agent behavior compliance. Enforcement mechanisms MUST support graduated responses to behavior policy violations, ranging from behavioral warnings and restricted operation modes to complete agent authorization revocation. Policy integration points SHALL enable dynamic adjustment of behavior constraints based on operational context, agent trust levels, and historical compliance patterns, ensuring that enforcement actions are proportionate to assessed risks while maintaining operational continuity for compliant autonomous agents.", "The security architecture of ABVP introduces novel attack surfaces and trust model complexities that require careful analysis. The protocol's reliance on continuous behavior monitoring creates potential privacy vectors where sensitive agent reasoning processes and decision patterns become observable to verification entities. Implementers MUST ensure that behavior traces capture only policy-relevant actions while maintaining operational privacy through selective disclosure mechanisms. The cryptographic binding between behavior attestations and verification proofs, as defined in [RFC9052], provides integrity protection but assumes the underlying attestation infrastructure remains uncompromised. When utilizing Multi-Vendor TEE Attestation (M-TACE) as specified in [draft-aylward-aiga-1], implementations SHOULD distribute trust across multiple hardware vendors to mitigate single-vendor compromise scenarios, though this approach increases complexity in trust anchor management and attestation verification workflows.\n\nBehavior spoofing attacks represent a fundamental threat to ABVP's security model, where malicious agents attempt to generate false behavior traces that appear compliant while executing unauthorized actions. The protocol addresses this through cryptographic proof-based autonomy mechanisms that require agents to demonstrate compliance through zero-knowledge proofs before receiving operational privileges. However, the effectiveness of these protections depends critically on the tamper-resistance of the behavior capture mechanisms and the integrity of the Trusted Execution Environment. Implementations MUST validate that TEE attestations conform to the hardware security requirements defined in the relevant trust anchor specifications, and SHOULD implement continuous attestation refresh cycles to detect runtime compromises. The integration with existing authorization frameworks [RFC6749] creates additional attack vectors where compromised authorization tokens could be used to bypass behavior verification requirements.\n\nThe trust model underlying ABVP assumes that Trust Anchors maintain cryptographic integrity and operate according to specified governance policies. This assumption creates systemic risks where compromise of trust anchors could invalidate entire verification domains. Implementations SHOULD implement trust anchor rotation mechanisms and maintain distributed trust models that prevent single points of failure. The protocol's integration with verifiable agent conversations [draft-birkholz-verifiable-agent-conversations] introduces cross-protocol security dependencies where vulnerabilities in conversation verification could compromise behavior attestation integrity. Additionally, the use of JSON Web Tokens [RFC7519] for attestation transport creates standard token-based attack surfaces including replay attacks and token manipulation, which MUST be mitigated through proper timestamp validation, nonce mechanisms, and secure token storage practices as specified in [RFC8446].\n\nTemporal attacks against ABVP involve manipulating the timing of behavior verification to create windows where non-compliant behavior goes undetected. The protocol's real-time verification requirements create performance versus security trade-offs where verification latency could be exploited by sophisticated attackers. Implementations MUST establish maximum verification latencies and implement fail-safe mechanisms that restrict agent capabilities when verification cannot be completed within specified time bounds. The certificate-based trust model [RFC5280] underlying trust anchor validation introduces standard PKI vulnerabilities including certificate chain attacks and revocation checking failures. Privacy concerns extend beyond individual agent behavior to aggregate behavioral patterns that could reveal sensitive operational intelligence about agent deployments. The protocol SHOULD implement differential privacy mechanisms and behavioral aggregation techniques that preserve verification effectiveness while limiting information disclosure to verification entities and external observers monitoring attestation traffic patterns.", "This document requires IANA to establish and maintain several registries to support the standardized deployment of the Agent Behavior Verification Protocol. These registries are essential for ensuring interoperability across different ABVP implementations and preventing conflicts in namespace usage. The registries MUST be publicly accessible and maintained according to the policies specified in this section.\n\nIANA SHALL establish the \"ABVP Behavior Verification Schema Registry\" to manage standardized schemas for behavior attestation formats and verification proof structures. Registration requests MUST include the schema name, version identifier, JSON Schema definition [RFC8259], cryptographic signature requirements, and designated expert contact information. The registry SHALL use the \"Expert Review\" policy as defined in RFC 8126, with designated experts required to verify schema completeness, cryptographic soundness, and compatibility with existing ABVP attestation mechanisms. Schema names MUST follow the format \"abvp-schema-{category}-{name}\" where category indicates the behavior domain (e.g., \"communication\", \"resource-access\", \"decision-making\") and name provides specific identification.\n\nThe \"ABVP Protocol Identifier Registry\" SHALL be established to manage unique identifiers for ABVP protocol bindings and integration points with existing agent protocols. Each registered identifier MUST specify the target protocol integration (such as OAuth 2.0 [RFC6749] flows or TLS 1.3 [RFC8446] handshake extensions), the ABVP message format requirements, and backward compatibility constraints. Registration follows the \"IETF Review\" policy, requiring protocol bindings to demonstrate security analysis and interoperability testing with at least two independent implementations. Protocol identifiers MUST use URI format with the \"urn:ietf:params:abvp:\" prefix to ensure global uniqueness.\n\nIANA SHALL create the \"ABVP Enforcement Pillar Registry\" to standardize the Five Enforcement Pillars framework referenced in this specification, ensuring consistent implementation across verification systems. Each pillar registration MUST include typed schemas that formally define monitoring requirements, enforcement actions, and compliance measurement criteria. The registry MUST maintain version control for pillar definitions and provide clear migration paths when pillar specifications evolve. This registry SHALL also coordinate with the AIPREF Vocabulary Protocol to ensure alignment between behavior verification vocabularies and AI requirement specifications, preventing semantic conflicts in multi-protocol environments.\n\nThe \"ABVP Trust Anchor Registry\" SHALL manage identifiers and metadata for recognized trust anchor sources, including hardware attestation roots, multi-party consensus systems, and certified verification authorities. Trust anchor registrations MUST include cryptographic key information, attestation capability descriptions, supported hardware platforms (such as TPM or TEE implementations), and revocation procedures. The registry SHALL implement the \"Expert Review\" policy with additional requirements for security audit documentation and demonstrated operational history. All registered trust anchors MUST support the cryptographic requirements specified in RFC 9052 for COSE-based attestation formats and provide clear procedures for key rotation and emergency revocation scenarios."]

full_text:
  Internet-Draft                                             lake
  Intended status: standards-track                             March 2026
  Expires: September 05, 2026


           Agent Behavior Verification Protocol (ABVP)
           draft-ai-agent-behavior-verification-protocol-00

  Abstract

     Autonomous AI agents operate with increasing independence across
     network environments, making it critical to verify that their
     actual behavior matches expected policies and constraints.
     Existing approaches focus primarily on identity verification or
     single-point attestation, leaving gaps in continuous behavior
     monitoring and cross-protocol verification. This document defines
     the Agent Behavior Verification Protocol (ABVP), which provides
     standardized mechanisms for capturing, validating, and attesting
     to agent behavior patterns in real-time. ABVP enables continuous
     trustworthiness assessment through cryptographic behavior proofs,
     supports multi-vendor attestation environments, and integrates
     with existing authorization frameworks. The protocol addresses the
     fundamental challenge of moving from 'who is this agent?' to 'is
     this agent behaving as expected?' across diverse operational
     contexts. ABVP complements existing agent authentication and
     authorization protocols by providing the missing behavior
     verification layer essential for autonomous agent deployment at
     scale.

  Status of This Memo

     This Internet-Draft is submitted in full conformance with the
     provisions of BCP 78 and BCP 79.

     This document is intended to have standards-track status.
     Distribution of this memo is unlimited.

  Terminology

     The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
     NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
     "MAY", and "OPTIONAL" in this document are to be interpreted as
     described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
     appear in all capitals, as shown here.

     Behavior Attestation
        A cryptographically signed assertion about an agent's observed
        behavior patterns over a specified time period

     Verification Proof
        Cryptographic evidence that demonstrates an agent's compliance
        with specified behavioral constraints

     Behavior Trace
        A structured record of agent actions, decisions, and reasoning
        processes that can be cryptographically verified

     Trust Anchor
        A root source of trust for behavior verification, typically
        backed by hardware attestation or multi-party consensus

     Behavior Policy
        A formal specification of expected agent behavior patterns and
        constraints

     Verification Registry
        A distributed system for storing and validating behavior
        attestations and verification proofs


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement  ...........................................  5
     4.  ABVP Architecture and Components  ............................  6
     5.  Behavior Capture and Attestation Mechanisms  .................  7
     6.  Protocol Integration and Bindings  ...........................  8
     7.  Verification Workflows and Enforcement  ......................  9
     8.  Security Considerations  .....................................  10
     9.  IANA Considerations  .........................................  11
     10.  References  ..................................................  12

  1.  Introduction

     The proliferation of autonomous AI agents across network
     environments has introduced a fundamental challenge in distributed
     systems: verifying that agents behave according to their intended
     design and declared policies throughout their operational
     lifecycle. Traditional authentication and authorization
     mechanisms, as defined in frameworks like OAuth 2.0 [RFC6749] and
     established in agent-specific protocols [draft-aylward-aiga-1],
     primarily focus on identity verification and initial access
     control decisions. However, these approaches are insufficient for
     autonomous agents that operate independently over extended
     periods, modify their behavior based on learning, and interact
     across multiple administrative domains without continuous human
     oversight.

     Current verification approaches leave critical gaps in ongoing
     behavioral assurance. While existing protocols can answer "who is
     this agent?" through identity attestation, they cannot adequately
     address "is this agent behaving as expected?" during operation.
     This limitation becomes particularly problematic when agents
     exhibit Dynamic Behavior Authentication requirements, where the
     agent's behavioral patterns change over time based on learning
     algorithms, environmental adaptation, or policy updates. The
     absence of standardized Behavioral Trustworthiness Assessment
     mechanisms means that authorization decisions cannot incorporate
     an agent's actual behavioral history, leading to either overly
     restrictive policies that limit legitimate agent autonomy or
     overly permissive policies that create security risks.

     The Agent Behavior Verification Protocol (ABVP) addresses these
     limitations by providing a standardized framework for Continuous
     Trustworthiness Verification that extends beyond initial
     authentication to ongoing behavioral validation. ABVP complements
     existing agent authentication protocols by introducing behavior
     attestation mechanisms that capture, verify, and attest to agent
     behavior patterns using cryptographic proofs. The protocol
     leverages hardware-backed attestation capabilities, similar to
     those used in verifiable agent conversations [draft-birkholz-
     verifiable-agent-conversations], while extending the verification
     scope from message integrity to comprehensive behavior pattern
     validation.

     ABVP integrates with existing authorization frameworks by
     providing behavior verification inputs that enhance access control
     decisions. Rather than replacing current agent protocols, ABVP
     serves as a behavior verification layer that can be bound to
     existing communication protocols and authorization systems. The
     protocol enables verification registries to maintain distributed
     records of agent behavior attestations, allowing multiple parties
     to contribute to and benefit from behavioral trustworthiness
     assessments. This approach supports multi-vendor environments
     where agents from different providers must demonstrate behavioral
     compliance to shared policies and constraints.

     The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
     NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
     "MAY", and "OPTIONAL" in this document are to be interpreted as
     described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
     appear in all capitals, as shown here. This document assumes
     familiarity with cryptographic attestation concepts, JSON-based
     data structures [RFC8259], and existing agent protocol frameworks.

  2.  Terminology

     This document uses terminology from several domains including
     cryptographic attestation, autonomous agent systems, and
     distributed verification protocols. The key words "MUST", "MUST
     NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT",
     "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this
     document are to be interpreted as described in BCP 14 [RFC2119]
     [RFC8174] when, and only when, they appear in all capitals, as
     shown here.

     **Agent**: An autonomous software entity that operates
       independently to achieve specified goals, potentially across
       multiple network environments and administrative domains. Agents
       may modify their behavior over time through learning,
       adaptation, or policy updates as defined in [draft-aylward-
       aiga-1].

     **Behavior Attestation**: A cryptographically signed assertion
       about an agent's observed behavior patterns over a specified
       time period. Behavior attestations provide verifiable evidence
       of agent actions, decisions, and compliance with established
       policies, extending the concept of verifiable agent
       conversations [draft-birkholz-verifiable-agent-conversations] to
       broader behavioral patterns.

     **Behavior Policy**: A formal specification of expected agent
       behavior patterns and constraints, expressed as machine-readable
       rules that define acceptable and unacceptable agent actions.
       Behavior policies serve as the normative baseline against which
       agent behavior is measured and verified.

     **Behavior Trace**: A structured, chronologically ordered record
       of agent actions, decisions, and reasoning processes that can be
       cryptographically verified for integrity and authenticity.
       Behavior traces form the evidentiary foundation for behavior
       attestations and MUST be tamper-evident through cryptographic
       linking as specified in [RFC9052].

     **Trust Anchor**: A root source of trust for behavior
       verification, typically backed by hardware attestation
       mechanisms, trusted execution environments, or multi-party
       consensus protocols. Trust anchors provide the cryptographic
       foundation for establishing the authenticity and integrity of
       behavior attestations, similar to certificate authorities in PKI
       systems [RFC5280].

     **Verification Proof**: Cryptographic evidence that demonstrates
       an agent's compliance with specified behavioral constraints,
       generated through zero-knowledge proofs, hash chains, or other
       cryptographic mechanisms. Verification proofs enable third
       parties to validate agent behavior without requiring access to
       sensitive operational details, supporting the cryptographic
       proof-based autonomy model [draft-berlinai-vera].

     **Verification Registry**: A distributed system for storing,
       indexing, and validating behavior attestations and verification
       proofs. The registry provides a queryable interface for behavior
       verification while maintaining appropriate privacy controls and
       access restrictions based on authorization frameworks such as
       OAuth 2.0 [RFC6749].

     **Behavior Attestation Engine (BAE)**: A trusted component
       responsible for continuously monitoring agent behavior,
       generating behavior traces, and producing cryptographically
       signed behavior attestations. The BAE operates within a trusted
       execution environment or relies on hardware security modules to
       ensure the integrity of the attestation process.

  3.  Problem Statement

     Current agent verification systems primarily focus on establishing
     "who" an agent is through identity and capability attestation, but
     fail to address the fundamental question of "how" an agent behaves
     over time. Traditional approaches, as described in existing
     frameworks [RFC6749] and emerging agent protocols [draft-birkholz-
     verifiable-agent-conversations], concentrate on static
     verification points such as initial authentication, capability
     declarations, and single-point attestations. While these
     mechanisms successfully establish agent identity and initial
     trustworthiness, they create significant gaps in ongoing
     behavioral assurance as agents operate with increasing autonomy
     across distributed network environments.

     The challenge becomes more acute when considering autonomous
     agents that inherently modify their behavior patterns based on
     environmental feedback, learning algorithms, or Real-Time Task
     Adaptability requirements [draft-cui-ai-agent-task]. Existing
     attestation systems, including advanced approaches like Multi-
     Vendor TEE Attestation (M-TACE) [draft-aylward-aiga-1], excel at
     verifying the integrity of agent code and initial state but cannot
     validate whether an agent's runtime behavior adheres to expected
     policies after deployment. This creates a verification gap where
     an agent may pass initial attestation checks while subsequently
     exhibiting behaviors that violate operational constraints,
     security policies, or ethical guidelines without detection.

     Furthermore, current verification approaches lack standardized
     mechanisms for continuous behavior monitoring across heterogeneous
     environments. Agent Reasoning Trace Capture techniques [draft-
     birkholz-verifiable-agent-conversations] provide valuable insights
     into decision-making processes but do not establish cryptographic
     proofs of behavioral compliance that can be verified by third
     parties. The absence of standardized Behavior Traces and
     Verification Proofs means that each deployment environment must
     develop proprietary monitoring solutions, leading to fragmented
     trust models and limited interoperability between agent
     ecosystems.

     The temporal dimension of agent behavior verification presents
     additional challenges that existing protocols do not adequately
     address. Static verification mechanisms cannot account for the
     dynamic nature of autonomous agents that adapt their strategies,
     modify their interaction patterns, or evolve their decision-making
     processes over operational lifetimes. Without continuous Behavior
     Attestation capabilities, network operators and service providers
     cannot maintain confidence in agent trustworthiness beyond initial
     deployment, creating significant risks for mission-critical
     applications and cross-organizational agent interactions.

     Current authorization frameworks also lack integration points for
     ongoing behavioral verification, focusing instead on capability-
     based access control determined at authentication time. The
     absence of standardized Behavior Policy enforcement mechanisms
     means that agents operating within their assigned capabilities may
     still exhibit problematic behaviors that violate implicit
     operational expectations or emergent security requirements. This
     gap becomes particularly problematic in multi-tenant environments
     where agents from different organizations interact, requiring
     mutual assurance of behavioral compliance that extends beyond
     simple identity verification.

     The scalability challenges of behavior verification compound these
     issues, as existing approaches do not provide efficient mechanisms
     for aggregating behavioral evidence across distributed deployments
     or enabling third-party verification of agent conduct. Without
     standardized Verification Registry systems and Trust Anchor
     mechanisms, each verification attempt requires independent
     evidence collection and validation, creating computational and
     administrative overhead that limits practical deployment of
     comprehensive behavior monitoring in large-scale autonomous agent
     environments.

  4.  ABVP Architecture and Components

     The Agent Behavior Verification Protocol (ABVP) architecture
     consists of three primary components that work together to provide
     continuous behavior verification for autonomous AI agents. These
     components establish a comprehensive framework for capturing,
     validating, and attesting to agent behavior patterns in real-time
     across diverse operational environments. The architecture is
     designed to be vendor-neutral, protocol-agnostic, and compatible
     with existing agent authentication and authorization frameworks as
     defined in [RFC6749] and referenced in [draft-aylward-aiga-1].

     The Behavior Attestation Engine (BAE) serves as the core component
     responsible for capturing agent behavior traces and generating
     cryptographic attestations. Each BAE MUST maintain a secure
     execution environment that continuously monitors agent actions,
     decisions, and reasoning processes without interfering with agent
     operation. The BAE generates behavior traces using structured
     formats based on [RFC8259] and creates cryptographically signed
     attestations using mechanisms defined in [RFC9052]. These
     attestations follow the Five Enforcement Pillars with Typed
     Schemas pattern, providing comprehensive coverage across
     authorization enforcement, data handling compliance, operational
     boundary adherence, communication protocol compliance, and
     resource utilization monitoring. The BAE SHOULD integrate with
     hardware-based Trusted Execution Environments (TEEs) when
     available to provide additional attestation integrity guarantees
     as specified in existing RATS frameworks.

     Verification Registries provide distributed storage and validation
     services for behavior attestations and verification proofs
     generated by BAEs. These registries operate using a federated
     trust model similar to the Public Registry Enrollment Mode,
     enabling cross-organizational behavior verification without
     requiring direct trust relationships between all participants.
     Each registry MUST validate the cryptographic integrity of
     incoming attestations, verify the authenticity of the attesting
     BAE, and maintain tamper-evident logs of all verification
     activities. Verification Registries support both real-time queries
     for immediate behavior verification and batch processing for
     historical behavior analysis. The registries expose standardized
     APIs that allow verifying parties to retrieve behavior
     attestations, validate verification proofs, and assess agent
     trustworthiness based on historical behavior patterns.

     Trust Anchor Management provides the foundational trust
     infrastructure that enables the entire ABVP ecosystem to function
     reliably. Trust anchors serve as root sources of trust for
     behavior verification and are typically backed by hardware
     attestation mechanisms, multi-party consensus systems, or
     established certificate authorities as defined in [RFC5280]. The
     Trust Anchor Management component MUST provide mechanisms for
     trust anchor discovery, validation, and lifecycle management
     including key rotation and revocation. Following the DNS TXT
     Records approach for agent identity distribution, trust anchor
     information MAY be published using DNS infrastructure to enable
     scalable trust anchor discovery across organizational boundaries.
     Trust Anchor Management also defines the policies and procedures
     for establishing new trust anchors, managing trust relationships
     between different ABVP deployments, and handling trust anchor
     compromise or revocation scenarios.

     The interaction between these components creates a continuous
     behavior verification loop that provides ongoing assurance of
     agent trustworthiness. When an agent performs actions, the
     associated BAE captures these activities as behavior traces and
     generates signed attestations that are stored in Verification
     Registries. Verifying parties can query these registries to obtain
     current and historical behavior attestations, validate them
     against relevant trust anchors, and make informed decisions about
     agent trustworthiness. This architecture supports both centralized
     deployments within single organizations and federated deployments
     spanning multiple organizations, enabling behavior verification
     across the full spectrum of autonomous agent operational
     scenarios. The modular design allows organizations to implement
     components incrementally while maintaining interoperability with
     existing agent infrastructure and protocols such as those defined
     in [draft-birkholz-verifiable-agent-conversations].

  5.  Behavior Capture and Attestation Mechanisms

     The ABVP behavior capture subsystem defines standardized
     mechanisms for recording agent actions, decisions, and reasoning
     processes in a cryptographically verifiable format. Agent
     implementations MUST generate behavior traces that capture
     sufficient detail to enable meaningful verification against
     behavioral policies. These traces MUST include timestamped records
     of agent actions, input stimuli, decision reasoning (as specified
     in [draft-birkholz-verifiable-agent-conversations]), and any
     policy evaluations performed by the agent. The behavior capture
     mechanism SHOULD be implemented as close to the agent's core
     reasoning engine as possible to minimize opportunities for
     tampering or selective reporting.

     Behavior traces MUST be structured as JSON objects [RFC8259]
     containing mandatory fields for agent identity, timestamp, action
     type, input context, and reasoning chain. The reasoning chain
     field leverages the agent reasoning trace capture mechanisms
     defined in [draft-birkholz-verifiable-agent-conversations] to
     provide transparency into the agent's decision-making process.
     Each trace entry MUST be signed using the agent's cryptographic
     identity as established through hardware-backed verification
     systems [draft-aylward-aiga-1]. Implementations MAY compress or
     summarize behavior traces for efficiency while maintaining
     cryptographic integrity through merkle tree structures or similar
     authenticated data structures.

     The attestation generation process transforms behavior traces into
     cryptographically signed behavior attestations that can be
     independently verified by third parties. Attestation engines MUST
     create attestations in JSON Web Signature (JWS) format [RFC7515]
     using keys derived from or backed by hardware trust anchors. For
     implementations with Trusted Platform Module (TPM) support,
     attestations SHOULD include TPM-backed signatures following
     patterns similar to those defined for email attestation in [draft-
     drake-email-tpm-attestation]. The attestation payload MUST include
     a hash of the complete behavior trace, the evaluation period,
     applicable behavior policies, and compliance assertions.

     Hardware attestation integration provides the foundational trust
     layer for ABVP by anchoring behavior attestations to tamper-
     resistant hardware. Agents operating on platforms with Trusted
     Execution Environment (TEE) capabilities MUST generate
     attestations from within the TEE to ensure behavior trace
     integrity. The attestation process MUST establish a cryptographic
     chain of trust from the hardware root of trust through the agent's
     runtime environment to the behavior attestation itself. This chain
     enables verifiers to confirm not only that the attestation is
     authentic but also that the underlying behavior capture mechanism
     has not been compromised.

     Attestation formats MUST support both real-time streaming
     attestations for continuous verification and batch attestations
     for periodic compliance reporting. Streaming attestations provide
     immediate behavior verification but require more computational
     overhead, while batch attestations enable efficient verification
     of longer behavior patterns. Implementations SHOULD support
     attestation aggregation mechanisms that allow multiple related
     behavior traces to be combined into a single attestation without
     losing verification granularity. All attestations MUST include
     sufficient metadata to enable policy evaluation and MUST be
     resistant to replay attacks through appropriate nonce and
     timestamp mechanisms as specified in [RFC8446] for cryptographic
     freshness.

  6.  Protocol Integration and Bindings

     ABVP is designed to operate as a complementary layer alongside
     existing agent protocols and authorization frameworks, providing
     behavior verification capabilities without disrupting established
     communication patterns. The protocol integrates through
     standardized extension points and binding mechanisms that allow
     existing agent infrastructures to incrementally adopt behavior
     verification. ABVP bindings MUST be implemented in a manner that
     preserves backward compatibility with agents that do not support
     behavior verification while providing enhanced trust capabilities
     for ABVP-enabled environments.

     The protocol defines three primary integration patterns: inline
     verification where behavior attestations are embedded directly in
     protocol messages, out-of-band verification where behavior proofs
     are transmitted through separate channels, and registry-based
     verification where behavior attestations are stored in distributed
     verification registries for asynchronous validation. For OAuth 2.0
     [RFC6749] and similar authorization frameworks, ABVP extends
     token-based flows by including behavior verification claims within
     JWT tokens [RFC7519] or as additional attestation headers. The
     Behavior Attestation Engine generates verification proofs that
     reference the agent's recent behavior traces and embeds these
     within the authorization context, enabling authorization servers
     to make access decisions based on both identity and behavioral
     compliance.

     Integration with Trusted Execution Environment (TEE) based agent
     frameworks leverages hardware attestation capabilities to anchor
     behavior verification in secure enclaves. ABVP bindings for TEE
     environments utilize the JOSE DVS extension mechanisms to provide
     derived verification signatures that combine hardware attestation
     with behavior proofs. The protocol supports integration with
     existing verifiable agent conversation frameworks [draft-birkholz-
     verifiable-agent-conversations] by extending conversation
     attestation to include behavioral compliance assertions. When
     integrated with agent discovery protocols, ABVP provides post-
     discovery authorization handshake capabilities that validate
     behavioral constraints before permitting tool execution or
     authority delegation.

     Protocol message formats utilize CBOR Web Token (CWT) structures
     [RFC9052] for compact behavior attestations in bandwidth-
     constrained environments, while supporting JSON-based attestation
     formats [RFC8259] for web-oriented integrations. ABVP bindings
     MUST specify the transport-specific mechanisms for behavior
     verification, including TLS 1.3 [RFC8446] extension points for
     embedding behavior proofs in secure channels and X.509 [RFC5280]
     certificate extensions for long-term behavior attestation storage.
     The protocol defines standard header fields and message extensions
     that enable behavior verification across HTTP-based agent
     communications, WebSocket connections, and message queue systems.

     Behavior policy alignment with standardized vocabularies such as
     AIPREF enables consistent behavior verification across multi-
     vendor agent environments. ABVP protocol bindings support dynamic
     policy negotiation where agents and verification entities can
     establish mutually acceptable behavior constraints and attestation
     formats during protocol handshake. The integration framework
     provides hooks for custom behavior verification logic while
     maintaining interoperability through standardized attestation
     formats and verification procedures. Implementation guidance
     specifies how existing agent frameworks can incrementally adopt
     ABVP capabilities, starting with passive behavior logging and
     progressing to active behavior enforcement and attestation
     generation.

  7.  Verification Workflows and Enforcement

     This section defines the operational workflows that enable
     continuous behavior verification for autonomous agents. ABVP
     supports three primary verification patterns: real-time
     verification for immediate trust decisions, batch verification for
     historical compliance assessment, and dispute resolution for
     handling verification conflicts. Each workflow integrates with
     existing authorization frameworks while providing the behavioral
     assurance layer necessary for autonomous agent operations.

     Real-time verification workflows enable immediate assessment of
     agent behavior during active operations. When an agent initiates
     actions that require behavioral validation, the Behavior
     Attestation Engine MUST generate a verification proof that
     demonstrates compliance with applicable behavior policies. The
     verification proof SHALL be constructed using Public Key Derived
     HMAC mechanisms as specified in [draft-bastian-jose-pkdh],
     allowing verifiers to authenticate behavior attestations using
     only public key information from the agent's JWS tokens. Resource
     servers implementing ABVP MUST validate these proofs against
     registered behavior policies before authorizing agent actions. The
     real-time workflow supports integration with OAuth 2.0 [RFC6749]
     access tokens through delegation evidence verification, enabling
     Resource Servers to verify behavior attestations using AS-attested
     keys bound to access tokens as described in [draft-chu-oauth-as-
     attested-user-cert].

     Batch verification workflows provide mechanisms for historical
     behavior analysis and compliance reporting. Verification
     Registries MUST support batch processing of behavior traces
     covering extended time periods, enabling policy compliance
     assessment across multiple operational contexts. The batch
     verification process SHALL generate aggregated verification proofs
     that demonstrate sustained compliance with behavior policies over
     time. Batch workflows MUST implement congestion control mechanisms
     consistent with vendor-neutral behavior definitions for AI fabric
     environments to prevent verification processing from impacting
     operational performance. Verification results MUST be stored in
     structured formats using JSON [RFC8259] encoding and signed using
     CBOR Object Signing and Encryption (COSE) [RFC9052] to ensure
     integrity and non-repudiation.

     Dispute resolution workflows address scenarios where behavior
     verification results are contested or inconsistent across multiple
     verification sources. When verification conflicts arise, the
     dispute resolution process MUST invoke multi-party attestation
     mechanisms involving relevant Trust Anchors to establish
     authoritative behavior assessments. The dispute resolution
     workflow SHALL generate resolution evidence that includes
     cryptographic proofs from multiple verification sources and a
     consensus determination of agent behavior compliance. Enforcement
     mechanisms MUST support graduated responses to behavior policy
     violations, ranging from behavioral warnings and restricted
     operation modes to complete agent authorization revocation. Policy
     integration points SHALL enable dynamic adjustment of behavior
     constraints based on operational context, agent trust levels, and
     historical compliance patterns, ensuring that enforcement actions
     are proportionate to assessed risks while maintaining operational
     continuity for compliant autonomous agents.

  8.  Security Considerations

     The security architecture of ABVP introduces novel attack surfaces
     and trust model complexities that require careful analysis. The
     protocol's reliance on continuous behavior monitoring creates
     potential privacy vectors where sensitive agent reasoning
     processes and decision patterns become observable to verification
     entities. Implementers MUST ensure that behavior traces capture
     only policy-relevant actions while maintaining operational privacy
     through selective disclosure mechanisms. The cryptographic binding
     between behavior attestations and verification proofs, as defined
     in [RFC9052], provides integrity protection but assumes the
     underlying attestation infrastructure remains uncompromised. When
     utilizing Multi-Vendor TEE Attestation (M-TACE) as specified in
     [draft-aylward-aiga-1], implementations SHOULD distribute trust
     across multiple hardware vendors to mitigate single-vendor
     compromise scenarios, though this approach increases complexity in
     trust anchor management and attestation verification workflows.

     Behavior spoofing attacks represent a fundamental threat to ABVP's
     security model, where malicious agents attempt to generate false
     behavior traces that appear compliant while executing unauthorized
     actions. The protocol addresses this through cryptographic proof-
     based autonomy mechanisms that require agents to demonstrate
     compliance through zero-knowledge proofs before receiving
     operational privileges. However, the effectiveness of these
     protections depends critically on the tamper-resistance of the
     behavior capture mechanisms and the integrity of the Trusted
     Execution Environment. Implementations MUST validate that TEE
     attestations conform to the hardware security requirements defined
     in the relevant trust anchor specifications, and SHOULD implement
     continuous attestation refresh cycles to detect runtime
     compromises. The integration with existing authorization
     frameworks [RFC6749] creates additional attack vectors where
     compromised authorization tokens could be used to bypass behavior
     verification requirements.

     The trust model underlying ABVP assumes that Trust Anchors
     maintain cryptographic integrity and operate according to
     specified governance policies. This assumption creates systemic
     risks where compromise of trust anchors could invalidate entire
     verification domains. Implementations SHOULD implement trust
     anchor rotation mechanisms and maintain distributed trust models
     that prevent single points of failure. The protocol's integration
     with verifiable agent conversations [draft-birkholz-verifiable-
     agent-conversations] introduces cross-protocol security
     dependencies where vulnerabilities in conversation verification
     could compromise behavior attestation integrity. Additionally, the
     use of JSON Web Tokens [RFC7519] for attestation transport creates
     standard token-based attack surfaces including replay attacks and
     token manipulation, which MUST be mitigated through proper
     timestamp validation, nonce mechanisms, and secure token storage
     practices as specified in [RFC8446].

     Temporal attacks against ABVP involve manipulating the timing of
     behavior verification to create windows where non-compliant
     behavior goes undetected. The protocol's real-time verification
     requirements create performance versus security trade-offs where
     verification latency could be exploited by sophisticated
     attackers. Implementations MUST establish maximum verification
     latencies and implement fail-safe mechanisms that restrict agent
     capabilities when verification cannot be completed within
     specified time bounds. The certificate-based trust model [RFC5280]
     underlying trust anchor validation introduces standard PKI
     vulnerabilities including certificate chain attacks and revocation
     checking failures. Privacy concerns extend beyond individual agent
     behavior to aggregate behavioral patterns that could reveal
     sensitive operational intelligence about agent deployments. The
     protocol SHOULD implement differential privacy mechanisms and
     behavioral aggregation techniques that preserve verification
     effectiveness while limiting information disclosure to
     verification entities and external observers monitoring
     attestation traffic patterns.

  9.  IANA Considerations

     This document requires IANA to establish and maintain several
     registries to support the standardized deployment of the Agent
     Behavior Verification Protocol. These registries are essential for
     ensuring interoperability across different ABVP implementations
     and preventing conflicts in namespace usage. The registries MUST
     be publicly accessible and maintained according to the policies
     specified in this section.

     IANA SHALL establish the "ABVP Behavior Verification Schema
     Registry" to manage standardized schemas for behavior attestation
     formats and verification proof structures. Registration requests
     MUST include the schema name, version identifier, JSON Schema
     definition [RFC8259], cryptographic signature requirements, and
     designated expert contact information. The registry SHALL use the
     "Expert Review" policy as defined in RFC 8126, with designated
     experts required to verify schema completeness, cryptographic
     soundness, and compatibility with existing ABVP attestation
     mechanisms. Schema names MUST follow the format "abvp-
     schema-{category}-{name}" where category indicates the behavior
     domain (e.g., "communication", "resource-access", "decision-
     making") and name provides specific identification.

     The "ABVP Protocol Identifier Registry" SHALL be established to
     manage unique identifiers for ABVP protocol bindings and
     integration points with existing agent protocols. Each registered
     identifier MUST specify the target protocol integration (such as
     OAuth 2.0 [RFC6749] flows or TLS 1.3 [RFC8446] handshake
     extensions), the ABVP message format requirements, and backward
     compatibility constraints. Registration follows the "IETF Review"
     policy, requiring protocol bindings to demonstrate security
     analysis and interoperability testing with at least two
     independent implementations. Protocol identifiers MUST use URI
     format with the "urn:ietf:params:abvp:" prefix to ensure global
     uniqueness.

     IANA SHALL create the "ABVP Enforcement Pillar Registry" to
     standardize the Five Enforcement Pillars framework referenced in
     this specification, ensuring consistent implementation across
     verification systems. Each pillar registration MUST include typed
     schemas that formally define monitoring requirements, enforcement
     actions, and compliance measurement criteria. The registry MUST
     maintain version control for pillar definitions and provide clear
     migration paths when pillar specifications evolve. This registry
     SHALL also coordinate with the AIPREF Vocabulary Protocol to
     ensure alignment between behavior verification vocabularies and AI
     requirement specifications, preventing semantic conflicts in
     multi-protocol environments.

     The "ABVP Trust Anchor Registry" SHALL manage identifiers and
     metadata for recognized trust anchor sources, including hardware
     attestation roots, multi-party consensus systems, and certified
     verification authorities. Trust anchor registrations MUST include
     cryptographic key information, attestation capability
     descriptions, supported hardware platforms (such as TPM or TEE
     implementations), and revocation procedures. The registry SHALL
     implement the "Expert Review" policy with additional requirements
     for security audit documentation and demonstrated operational
     history. All registered trust anchors MUST support the
     cryptographic requirements specified in RFC 9052 for COSE-based
     attestation formats and provide clear procedures for key rotation
     and emergency revocation scenarios.

  10.  References

  10.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8446]
           RFC 8446

     [RFC 5280]
           RFC 5280

     [RFC 7519]
           RFC 7519

     [RFC 9052]
           RFC 9052

     [draft-birkholz-verifiable-agent-conversations]
           draft-birkholz-verifiable-agent-conversations

     [draft-aylward-aiga-1]
           draft-aylward-aiga-1

  10.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 8259]
           RFC 8259

     [RFC 9110]
           RFC 9110

     [draft-aylward-daap-v2]
           draft-aylward-daap-v2

     [draft-chen-agent-decoupled-authorization-model]
           draft-chen-agent-decoupled-authorization-model

     [draft-berlinai-vera]
           draft-berlinai-vera

     [draft-chen-ai-agent-auth-new-requirements]
           draft-chen-ai-agent-auth-new-requirements

     [draft-condrey-rats-witnessd-enrollment]
           draft-condrey-rats-witnessd-enrollment

     [draft-drake-email-tpm-attestation]
           draft-drake-email-tpm-attestation

     [draft-bastian-jose-dvs]
           draft-bastian-jose-dvs

     [draft-bastian-jose-pkdh]
           draft-bastian-jose-pkdh

     [draft-barney-caam]
           draft-barney-caam

     [draft-altanai-aipref-realtime-protocol-bindings]
           draft-altanai-aipref-realtime-protocol-bindings


  Author's Address

     Generated by IETF Draft Analyzer
     2026-03-04

rating_json:
  {"s": "ABVP proposes a standardized protocol for continuous cryptographic verification of autonomous AI agent behavior against expected policies in real-time. It positions itself as a complementary layer to existing agent authentication, addressing the gap between identity verification and actual behavioral trustworthiness across network environments.", "n": 4, "nn": "The focus on continuous behavioral attestation rather than point-in-time identity verification is relatively novel, though behavior monitoring concepts exist in adjacent domains (runtime security, policy enforcement).", "m": 1, "mn": "Early stage draft with no implementation references, no protocol details specified, no formal schema or message definitions, and lacks concrete examples of 'cryptographic behavior proofs' mechanisms.", "o": 3, "on": "Moderate overlap with agent authentication/authorization frameworks and policy governance approaches; behavioral monitoring overlaps with existing runtime security and DevOps attestation practices.", "mo": 2, "mon": "Timely topic given AI agent deployment concerns, but abstract framing and lack of technical substance limit perceived momentum; appears pre-WG maturity.", "r": 4, "rn": "Highly relevant given increased autonomous agent deployments, but relevance is contingent on whether real operational gaps exist that current attestation and authorization stacks don't address.", "c": ["A2A protocols", "AI safety/alignment", "Agent identity/auth", "Policy/governance", "Autonomous netops"]}


gap_topic: Agent Ecosystem Model
draft_name: draft-ai-agent-learning-protocol-00
title: Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments
family_name: agent-ecosystem
family_role: AEM
status: draft
created_at: 2026-03-04T00:25:19.526439+00:00
abstract:
  Federated learning enables distributed AI agents to collaboratively improve models without centralizing training data. However, existing federated architectures lack comprehensive privacy guarantees for multi-tenant environments where agents from different organizations must learn together while preventing data leakage. This specification defines the Privacy-Preserving Agent Learning Protocol (PPALP) that combines differential privacy, secure aggregation, and zero-knowledge proofs to enable federated agent learning with formal privacy guarantees. PPALP provides mechanisms for privacy-preserving model updates, tenant isolation enforcement, and cryptographic verification of privacy compliance. The protocol supports heterogeneous agent types while maintaining backward compatibility with existing federated learning frameworks. This work addresses critical privacy gaps in federated agent architectures and provides implementable solutions for secure cross-organizational AI collaboration.

outline_json:
  {"title": "Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments", "abstract": "Federated learning enables distributed AI agents to collaboratively improve models without centralizing training data. However, existing federated architectures lack comprehensive privacy guarantees for multi-tenant environments where agents from different organizations must learn together while preventing data leakage. This specification defines the Privacy-Preserving Agent Learning Protocol (PPALP) that combines differential privacy, secure aggregation, and zero-knowledge proofs to enable federated agent learning with formal privacy guarantees. PPALP provides mechanisms for privacy-preserving model updates, tenant isolation enforcement, and cryptographic verification of privacy compliance. The protocol supports heterogeneous agent types while maintaining backward compatibility with existing federated learning frameworks. This work addresses critical privacy gaps in federated agent architectures and provides implementable solutions for secure cross-organizational AI collaboration.", "sections": [{"title": "Introduction", "summary": "Motivates the need for privacy-preserving federated agent learning and positions this work within the broader AI agent standardization landscape. Outlines the key challenges of multi-tenant federated learning and the protocol's contribution to addressing them.", "key_ideas": ["Federated Agent Learning Privacy gap", "Multi-tenant privacy challenges", "Protocol overview"]}, {"title": "Terminology", "summary": "Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.", "key_ideas": ["Privacy-preserving terminology", "Federated learning concepts", "Multi-tenant definitions"]}, {"title": "Problem Statement", "summary": "Formally defines the privacy leakage problem in federated agent learning and quantifies the threat model. Identifies specific attack vectors including gradient inversion, membership inference, and cross-tenant data reconstruction.", "key_ideas": ["AI Agent Structured Threat Model", "Data leakage attack vectors", "Multi-tenant threat analysis"]}, {"title": "Privacy-Preserving Agent Learning Architecture", "summary": "Defines the overall system architecture combining differential privacy, secure aggregation, and zero-knowledge proofs. Specifies roles for federated coordinators, tenant gateways, and privacy compliance verifiers.", "key_ideas": ["Privacy-Preserving Reference Architecture", "Agent Gateway Control Plane", "Federated Policy Enforcement"]}, {"title": "Differential Privacy for Agent Model Updates", "summary": "Specifies the differential privacy mechanisms for agent model parameter updates including noise calibration, privacy budget allocation, and composition theorems. Defines privacy accounting across multiple learning rounds.", "key_ideas": ["Differential Privacy for Agent Models", "Privacy budget management", "Formal privacy guarantees"]}, {"title": "Secure Multi-Tenant Federated Aggregation", "summary": "Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.", "key_ideas": ["Multi-Tenant Federated Averaging", "Cryptographic aggregation", "ZKP-based Agent Attestation"]}, {"title": "Privacy Compliance and Verification", "summary": "Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.", "key_ideas": ["Privacy-Respecting Capability Attestation", "Cryptographic Erasure Compliance", "SD-JWT Privacy-Preserving Trust Proof"]}, {"title": "Protocol Message Formats and Flows", "summary": "Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.", "key_ideas": ["Protocol message specifications", "API definitions", "Communication flows"]}, {"title": "Security Considerations", "summary": "Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.", "key_ideas": ["Distributed Agent Communication Security Model", "Attack resistance analysis", "Key management"]}, {"title": "IANA Considerations", "summary": "Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.", "key_ideas": ["Protocol registrations", "Error code allocations"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 8259", "RFC 7519", "RFC 8446", "draft-kale-agntcy-federated-privacy"], "informative_refs": ["RFC 6749", "RFC 9110", "RFC 5280", "draft-berlinai-vera", "draft-huang-acme-scalable-agent-enrollment", "draft-messous-eat-ai", "draft-li-dmsc-mcps-agw", "draft-gaikwad-aps-profile", "draft-drake-email-tpm-attestation", "draft-huang-rats-agentic-eat-cap-attest"], "terminology": {"Privacy Budget": "A quantitative measure of the privacy cost incurred by releasing information about a dataset through differential privacy mechanisms", "Tenant Isolation": "The property that agent learning activities of one organizational tenant cannot infer private information about another tenant's data or models", "Secure Aggregation": "A cryptographic protocol that allows computation of aggregate statistics over distributed data without revealing individual contributions", "Gradient Inversion Attack": "An attack where an adversary attempts to reconstruct training data from shared gradient information in federated learning", "Privacy Compliance Verifier": "A system component that cryptographically verifies adherence to differential privacy guarantees and tenant isolation requirements", "Federated Coordinator": "The central entity responsible for orchestrating privacy-preserving model aggregation across multiple tenants in federated learning"}, "target_wg": "lake", "intended_status": "standards-track"}

sections_json:
  ["The rapid deployment of AI agents across organizational boundaries has created an urgent need for collaborative learning mechanisms that preserve privacy while enabling knowledge sharing. Traditional centralized machine learning approaches require data consolidation, which violates privacy regulations and organizational policies in multi-tenant environments where competing organizations must collaborate. Federated learning addresses data centralization concerns but existing protocols lack comprehensive privacy guarantees necessary for cross-organizational AI agent deployments. Current federated learning frameworks are vulnerable to gradient inversion attacks [draft-kale-agntcy-federated-privacy], membership inference attacks, and cross-tenant data reconstruction, making them unsuitable for privacy-sensitive multi-tenant agent learning scenarios.\n\nMulti-tenant federated agent learning presents unique challenges beyond traditional federated learning contexts. AI agents often process behavioral data, decision patterns, and strategic information that require stronger privacy protections than conventional machine learning datasets. The heterogeneous nature of agent types across organizations complicates model aggregation while maintaining meaningful learning outcomes. Existing privacy-preserving techniques such as differential privacy and secure aggregation, when applied independently, fail to provide comprehensive protection against sophisticated attacks targeting federated agent systems. Furthermore, regulatory compliance requirements such as GDPR's right to erasure and data processing transparency mandates necessitate cryptographic verification mechanisms that current federated learning protocols do not provide.\n\nThis specification introduces the Privacy-Preserving Agent Learning Protocol (PPALP) to address critical privacy gaps in federated agent architectures while enabling secure cross-organizational AI collaboration. PPALP combines differential privacy mechanisms specifically calibrated for agent model parameters, secure multi-party computation for cross-tenant aggregation, and zero-knowledge proofs for cryptographic verification of privacy compliance. The protocol builds upon the Privacy-Preserving Reference Architecture defined in [draft-kale-agntcy-federated-privacy] and extends federated averaging mechanisms to support heterogeneous agent types with formal privacy guarantees. PPALP integrates with existing federated learning frameworks through standardized APIs while providing backward compatibility for legacy deployments.\n\nThe protocol's contribution to AI agent standardization includes formal privacy accounting mechanisms that track privacy budget consumption across multiple learning rounds, tenant isolation enforcement that prevents cross-organizational information leakage, and cryptographic audit trails that enable regulatory compliance verification. PPALP addresses the critical need for standardized privacy-preserving protocols in federated agent learning by providing implementable solutions that balance collaborative learning benefits with stringent privacy requirements. The specification defines concrete message formats, communication flows, and security mechanisms necessary for production deployment of privacy-preserving federated agent learning systems in multi-tenant environments where traditional approaches fail to provide adequate protection.", "This document defines terminology for privacy-preserving federated learning protocols in multi-tenant environments. The key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.\n\n**Agent**: An autonomous software entity capable of learning and adapting its behavior through machine learning mechanisms, as defined in [draft-berlinai-vera]. In the context of this specification, agents participate in federated learning to improve their models collaboratively.\n\n**Federated Agent Learning**: A distributed machine learning paradigm where multiple AI agents collaboratively train shared models without centralizing their training data. Each agent maintains local data while contributing to global model improvement through privacy-preserving parameter sharing mechanisms.\n\n**Privacy Budget**: A quantitative measure of the privacy cost incurred by releasing information about a dataset through differential privacy mechanisms. The privacy budget, typically denoted as epsilon (\u03b5), bounds the maximum privacy loss across all learning iterations and MUST be carefully managed to maintain formal privacy guarantees throughout the federated learning process.\n\n**Differential Privacy**: A mathematical framework that provides formal privacy guarantees by adding calibrated noise to computation outputs. In federated agent learning, differential privacy mechanisms ensure that the participation of any individual data point cannot be inferred from the released model parameters or gradients.\n\n**Tenant**: An organizational entity participating in federated learning with its own collection of agents, data, and privacy requirements. Tenants represent distinct administrative domains that require isolation guarantees while participating in collaborative learning protocols.\n\n**Tenant Isolation**: The property that agent learning activities of one organizational tenant cannot infer private information about another tenant's data or models. Tenant isolation MUST be maintained through cryptographic mechanisms and differential privacy guarantees to prevent cross-organizational data leakage.\n\n**Secure Aggregation**: A cryptographic protocol that allows computation of aggregate statistics over distributed data without revealing individual contributions. Secure aggregation protocols typically employ techniques such as homomorphic encryption or secure multiparty computation to ensure that only the aggregated result is revealed to participants.\n\n**Federated Coordinator**: The central entity responsible for orchestrating privacy-preserving model aggregation across multiple tenants in federated learning. The coordinator manages communication flows, enforces privacy policies, and coordinates the secure aggregation process while maintaining tenant isolation guarantees.\n\n**Privacy Compliance Verifier**: A system component that cryptographically verifies adherence to differential privacy guarantees and tenant isolation requirements. The verifier employs zero-knowledge proofs and audit mechanisms to ensure that all participants comply with established privacy policies without revealing sensitive information about their contributions.\n\n**Gradient Inversion Attack**: An attack where an adversary attempts to reconstruct training data from shared gradient information in federated learning. Such attacks exploit the information leakage inherent in gradient sharing and represent a primary threat that privacy-preserving protocols MUST defend against.\n\n**Membership Inference Attack**: An attack technique where an adversary attempts to determine whether a specific data point was used in training a machine learning model. In federated settings, these attacks can be used to infer information about individual agents' training data across tenant boundaries.\n\n**Privacy Accounting**: The process of tracking and managing privacy budget consumption across multiple learning rounds and operations. Privacy accounting mechanisms MUST ensure that the cumulative privacy loss remains within acceptable bounds throughout the entire federated learning process.\n\n**Homomorphic Encryption**: A cryptographic technique that allows computation on encrypted data without decrypting it first. In the context of secure aggregation, homomorphic encryption enables the computation of aggregate model parameters while keeping individual contributions encrypted.\n\n**Zero-Knowledge Proof**: A cryptographic protocol that allows one party to prove knowledge of information to another party without revealing the information itself. Zero-knowledge proofs are used in privacy compliance verification to demonstrate adherence to privacy requirements without exposing sensitive details about the verification process.", "Federated agent learning in multi-tenant environments faces fundamental privacy leakage risks that existing protocols fail to adequately address. The core problem arises when AI agents from different organizations (tenants) participate in collaborative learning while sharing gradient updates or model parameters that inadvertently reveal sensitive information about their private training data. Unlike traditional federated learning scenarios with homogeneous participants, multi-tenant environments amplify privacy risks due to potentially adversarial relationships between tenants and the heterogeneous nature of agent architectures across organizational boundaries.\n\nThe threat model for privacy-preserving federated agent learning encompasses three primary attack vectors that adversaries MAY exploit to compromise tenant data privacy. First, gradient inversion attacks allow malicious participants to reconstruct approximations of private training samples from shared gradient information, particularly effective when batch sizes are small or when attackers can observe multiple training rounds. Second, membership inference attacks enable adversaries to determine whether specific data points were used in another tenant's training dataset by analyzing model behavior or parameter updates. Third, cross-tenant data reconstruction attacks leverage correlations between multiple tenants' shared information to infer sensitive attributes about datasets belonging to other organizational participants, violating tenant isolation requirements.\n\nThe privacy leakage problem is quantified through formal privacy metrics that MUST account for composition across multiple learning rounds and tenant interactions. Let \u03b5 represent the privacy budget expended per tenant per learning round, and \u03b4 represent the probability of privacy violation. The cumulative privacy cost grows with each parameter sharing event, and without proper privacy accounting mechanisms, the total privacy loss (\u03b5_total, \u03b4_total) MAY exceed acceptable thresholds defined by organizational privacy policies. Traditional differential privacy mechanisms designed for single-organization scenarios provide insufficient guarantees in multi-tenant federated settings where privacy budgets must be allocated across tenant boundaries and learning rounds while maintaining utility for collaborative model improvement.\n\nBuilding upon the AI Agent Structured Threat Model framework defined in [draft-berlinai-vera], federated agent learning environments face additional risks including compromised privacy compliance verifiers that MAY provide false attestations of differential privacy adherence, tenant gateway failures that could leak cross-tenant information, and coordination attacks where multiple tenants collude to circumvent privacy protections. The threat model assumes honest-but-curious federated coordinators who follow protocol specifications but MAY attempt to infer private information from aggregated data, and potentially malicious tenants who MAY deviate from prescribed privacy-preserving mechanisms to gain competitive advantages through unauthorized data access.\n\nThe problem statement also addresses the challenge of maintaining privacy guarantees while supporting heterogeneous agent architectures with varying computational capabilities, privacy requirements, and trust assumptions. Agents operating under different privacy regulations (such as GDPR, CCPA, or sector-specific requirements) MUST be able to participate in federated learning while maintaining compliance with their respective privacy obligations. The protocol MUST provide mechanisms for cryptographic verification of privacy compliance, audit trails for regulatory reporting, and graceful degradation when privacy budgets are exhausted, ensuring that collaborative learning can continue while respecting individual tenant privacy constraints and maintaining the overall utility of the federated learning process.", "This section defines the Privacy-Preserving Agent Learning Protocol (PPALP) architecture, which integrates differential privacy, secure aggregation, and zero-knowledge proofs to enable federated learning across organizational boundaries while maintaining formal privacy guarantees. The architecture extends the Privacy-Preserving Reference Architecture framework [draft-kale-agntcy-federated-privacy] to specifically address agent learning scenarios in multi-tenant environments. PPALP employs a layered approach where privacy mechanisms are enforced at multiple architectural levels to provide defense-in-depth protection against information leakage.\n\nThe PPALP architecture defines four primary component roles that collectively ensure privacy-preserving federated learning operations. The Federated Coordinator serves as the central orchestration entity responsible for managing global model state, coordinating aggregation rounds, and maintaining privacy budget allocations across all participating tenants. Tenant Gateways act as intermediary control plane entities, extending the Agent Gateway Control Plane concept [draft-li-dmsc-mcps-agw] to enforce tenant-specific privacy policies and manage agent authentication within organizational boundaries. Privacy Compliance Verifiers operate as independent auditing entities that cryptographically verify adherence to differential privacy guarantees and tenant isolation requirements through zero-knowledge proofs. Participating Agents represent the distributed learning entities that contribute model updates while consuming privacy budget allocated by their respective tenants.\n\nThe architectural design enforces strict tenant isolation through cryptographic boundaries and policy enforcement mechanisms. Each tenant maintains an isolated privacy budget that cannot be influenced by other tenants' learning activities, ensuring that cross-tenant privacy guarantees remain intact regardless of adversarial behavior by other participants. Tenant Gateways implement the Federated Policy Enforcement framework [draft-cui-dmsc-agent-cdi] to harmonize local privacy policies with global federated learning requirements while preventing policy conflicts that could compromise privacy guarantees. All inter-component communications MUST use TLS 1.3 [RFC8446] with mutual authentication based on X.509 certificates [RFC5280] to prevent network-level attacks.\n\nThe protocol supports heterogeneous agent types through a standardized privacy-preserving model update interface that abstracts underlying model architectures while ensuring consistent privacy accounting. Agents MUST register their learning capabilities and privacy requirements with their tenant's gateway, which negotiates appropriate differential privacy parameters with the Federated Coordinator. The architecture maintains backward compatibility with existing federated learning frameworks by implementing standard aggregation interfaces that can be secured through the PPALP privacy mechanisms without requiring modifications to existing agent implementations.\n\nPrivacy accounting occurs at multiple architectural layers to provide comprehensive protection against composition attacks and privacy budget exhaustion. The Federated Coordinator maintains global privacy budget state and implements composition theorems to track cumulative privacy expenditure across learning rounds. Tenant Gateways enforce local privacy policies and MAY implement additional privacy amplification techniques such as subsampling to reduce privacy costs for their constituent agents. Privacy Compliance Verifiers continuously monitor privacy budget consumption and generate cryptographic proofs of compliance that can be audited by external parties without revealing sensitive learning parameters or model information.", "This section specifies the differential privacy mechanisms that MUST be applied to agent model parameter updates to provide formal privacy guarantees in multi-tenant federated learning environments. All participating agents MUST implement differential privacy mechanisms that satisfy \u03b5-differential privacy for a pre-negotiated privacy parameter \u03b5, where smaller values of \u03b5 provide stronger privacy guarantees but may reduce model utility. The differential privacy implementation MUST use the Gaussian mechanism for continuous-valued parameters and the exponential mechanism for discrete-valued selections, calibrated to the global sensitivity of the learning algorithm as defined in [draft-kale-agntcy-federated-privacy].\n\nPrivacy budget allocation MUST be managed across multiple learning rounds to prevent privacy depletion over time. Each tenant MUST maintain a privacy budget tracker that accounts for the cumulative privacy cost across all federated learning rounds, where the total privacy cost is bounded by composition theorems. The system MUST implement advanced composition using the moments accountant method or R\u00e9nyi differential privacy to achieve tighter privacy bounds than basic composition would allow. Privacy budgets SHOULD be allocated proportionally to the expected utility gain from each learning round, with mechanisms for tenants to reserve budget for future high-priority learning tasks.\n\nNoise calibration for model parameter updates MUST be performed based on the L2 sensitivity of the gradient computation, which is bounded through gradient clipping techniques. Before applying differential privacy noise, gradients MUST be clipped to a maximum norm C, where C is negotiated during protocol initialization based on the trade-off between privacy, utility, and convergence requirements. The Gaussian noise added to each parameter update MUST have variance \u03c3\u00b2 = 2C\u00b2ln(1.25/\u03b4)/\u03b5\u00b2 for (\u03b5,\u03b4)-differential privacy, where \u03b4 represents the probability of privacy failure and MUST be set to be cryptographically negligible (typically \u03b4 \u2264 10\u207b\u2076).\n\nThe protocol MUST maintain detailed privacy accounting across learning rounds using a standardized privacy ledger format that records the privacy cost of each operation. This ledger MUST track per-tenant privacy expenditure, cross-tenant privacy interactions, and cumulative privacy debt according to the composition theorems. The privacy accounting system MUST provide cryptographic proofs of privacy budget compliance that can be verified by Privacy Compliance Verifiers as specified in Section 7. When a tenant's privacy budget is exhausted, the system MUST either terminate that tenant's participation or implement privacy budget renewal mechanisms with appropriate cooling-off periods to prevent privacy budget laundering attacks.\n\nThe differential privacy implementation MUST be compatible with the secure aggregation protocols defined in Section 6, ensuring that privacy guarantees are maintained even when combined with cryptographic operations. The noise addition process MUST occur before any cryptographic encoding for secure aggregation, and the protocol MUST verify that the combination of differential privacy and secure aggregation does not introduce additional privacy vulnerabilities through cryptographic side channels or timing attacks. All differential privacy parameters, including \u03b5, \u03b4, and clipping bounds, MUST be negotiated during the protocol handshake phase and cryptographically committed to prevent manipulation during the learning process.", "This section defines the cryptographic protocols that enable secure aggregation of model parameters across tenant boundaries while preserving privacy guarantees established in Section 5. The secure aggregation protocol MUST ensure that no individual tenant can learn information about other tenants' model updates beyond what is revealed by the final aggregated result. The protocol combines homomorphic encryption with secure multiparty computation techniques to achieve this property while maintaining computational efficiency suitable for practical federated learning deployments.\n\nThe Multi-Tenant Federated Averaging mechanism serves as the foundation for secure parameter aggregation. Each tenant's Privacy Compliance Verifier MUST encrypt their differentially-private model updates using a threshold homomorphic encryption scheme before transmission to the Federated Coordinator. The encryption MUST use a (t,n)-threshold scheme where t represents the minimum number of tenants required for decryption and n represents the total number of participating tenants. This ensures that no single tenant or coordinator can decrypt individual updates, while still enabling computation over encrypted parameters. The homomorphic encryption scheme SHOULD support both addition and limited multiplication operations to accommodate various neural network architectures commonly used in agent learning systems.\n\nThe secure multiparty computation protocol proceeds in three phases: commitment, computation, and verification. During the commitment phase, each tenant generates a cryptographic commitment to their encrypted model update using a computationally-hiding and perfectly-binding commitment scheme. The Federated Coordinator collects all commitments and broadcasts them to participating tenants for verification. In the computation phase, tenants collaboratively execute a secure aggregation protocol that computes the weighted average of model parameters without revealing individual contributions. The protocol MUST be secure against up to t-1 malicious participants and SHOULD provide robustness against participant dropout through verifiable secret sharing techniques.\n\nVerification mechanisms ensure correctness of the aggregation process and detect potential manipulation attempts. Each participating tenant MUST verify the integrity of the aggregation using zero-knowledge proofs that demonstrate correct execution of the secure computation protocol. The ZKP-based Agent Attestation mechanism enables tenants to prove their compliance with the differential privacy requirements specified in Section 5 without revealing their actual noise parameters or model updates. These proofs MUST be verified by the Federated Coordinator and recorded in an immutable audit trail as specified in Section 7. Additionally, the protocol includes range proofs to verify that model parameter updates fall within expected bounds, preventing gradient manipulation attacks that could compromise model convergence or extract private information.\n\nThe aggregation protocol MUST support heterogeneous agent types by normalizing model parameters to a common representation before encryption and aggregation. Tenants with different model architectures encode their updates using a standardized parameter mapping that preserves semantic meaning while enabling secure computation. The protocol SHOULD maintain backward compatibility with existing federated learning frameworks by providing adapter layers that translate between PPALP message formats defined in Section 8 and legacy federated learning APIs. Performance optimizations including parameter compression, selective aggregation of model layers, and asynchronous update mechanisms MAY be implemented provided they do not compromise the privacy guarantees established by the differential privacy and secure aggregation components.", "Privacy compliance in federated agent learning environments requires cryptographic mechanisms that provide verifiable proof of adherence to differential privacy guarantees while maintaining audit capabilities for regulatory requirements. This section defines the Privacy Compliance and Verification framework that enables tenants to demonstrate privacy compliance without revealing sensitive implementation details or compromising the privacy of their learning processes. The framework combines zero-knowledge proofs, selective disclosure attestations, and cryptographic erasure mechanisms to provide comprehensive privacy verification capabilities.\n\nThe Privacy Compliance Verifier MUST implement zero-knowledge proof protocols that allow federated agents to demonstrate differential privacy adherence without revealing actual noise values, data distributions, or model parameters. Following the SD-JWT Privacy-Preserving Trust Proof pattern from [draft-drake-email-tpm-attestation], the verifier SHALL use Selective Disclosure JWT mechanisms [RFC7519] to enable privacy-preserving attestation of compliance properties. Each participating agent MUST generate cryptographic proofs that demonstrate: (1) proper calibration of differential privacy noise according to the allocated privacy budget, (2) correct implementation of the Gaussian mechanism or equivalent noise generation, and (3) adherence to composition theorems across multiple learning rounds. These proofs MUST be constructed using non-interactive zero-knowledge proof systems such as zk-SNARKs or zk-STARKs that provide succinctness and efficient verification.\n\nAudit trail mechanisms MUST maintain cryptographically verifiable logs of all privacy-relevant operations while preserving the confidentiality of sensitive learning data. The audit system SHALL record privacy budget expenditures, aggregation operations, and tenant isolation boundary enforcement without logging actual model parameters or gradient values. Each audit entry MUST include a timestamp, tenant identifier, privacy budget allocation, and cryptographic hash of the differential privacy parameters used. The audit trail MUST support selective disclosure to authorized auditors while preventing unauthorized access to privacy-sensitive operational details. Privacy budget accounting MUST be maintained across all learning rounds with cryptographic commitments that prevent double-spending of privacy allocations.\n\nCryptographic erasure capabilities MUST be implemented to address regulatory requirements for data deletion and agent memory cleanup as specified in [draft-gaikwad-aps-profile]. The system SHALL support cryptographic erasure of agent memory associated with specific user or agent identities through key destruction mechanisms that render associated encrypted data permanently inaccessible. When a deletion request is received, the Privacy Compliance Verifier MUST coordinate the erasure of all federated learning artifacts associated with the specified identity, including gradient histories, model update records, and privacy budget allocations. The erasure process MUST generate cryptographic proofs of deletion that demonstrate the irreversible destruction of the requested data across all participating federated nodes.\n\nPrivacy compliance verification MUST support Privacy-Respecting Capability Attestation mechanisms that enable verification of agent privacy capabilities without revealing implementation-specific details. Following patterns from [draft-huang-rats-agentic-eat-cap-attest], agents MUST be able to attest to their differential privacy implementation capabilities, secure aggregation support, and tenant isolation enforcement without disclosing proprietary algorithms or security configurations. The attestation process SHALL use hardware-backed trust anchors where available, with fallback to software-based attestation for environments without trusted execution capabilities. All privacy compliance proofs and attestations MUST be timestamped and include validity periods to prevent replay attacks and ensure freshness of compliance claims.", "This section defines the concrete message formats, communication flows, and API definitions for the Privacy-Preserving Agent Learning Protocol (PPALP). All message exchanges MUST use JSON format as specified in [RFC8259] with UTF-8 encoding. Protocol implementations MUST support TLS 1.3 [RFC8446] for transport security and MUST authenticate using OAuth 2.0 [RFC6749] bearer tokens or X.509 certificates [RFC5280]. The protocol defines four primary message categories: initialization messages for establishing privacy parameters, update messages for transmitting differentially private model parameters, aggregation messages for secure multi-tenant computation, and verification messages for privacy compliance validation.\n\nThe protocol message structure follows a common envelope format with mandatory header fields. Each message MUST include a \"version\" field specifying the PPALP protocol version, a \"type\" field indicating the message category, a \"tenant_id\" field for tenant isolation enforcement, and a \"timestamp\" field using ISO 8601 format. Messages involving privacy budget allocation MUST include a \"privacy_budget\" object containing epsilon and delta values as defined in Section 5. Security-critical messages such as model updates and aggregation requests MUST include a \"signature\" field containing a JWS signature [RFC7519] for message authentication and integrity verification.\n\nThe federated learning session begins with a Privacy Parameter Negotiation flow initiated by the Federated Coordinator. The coordinator sends a \"privacy_init\" message specifying the global privacy budget, differential privacy mechanism, and aggregation method. Each Tenant Gateway responds with a \"privacy_accept\" message confirming participation and local privacy constraints. Model update flows use \"model_update\" messages containing encrypted differentially private gradients, privacy proof attestations, and secure aggregation shares. The Federated Coordinator processes these through \"aggregation_request\" and \"aggregation_response\" messages that implement the secure multi-tenant aggregation protocol defined in Section 6.\n\nPrivacy compliance verification occurs through dedicated message flows involving the Privacy Compliance Verifier component. Agents MUST send \"privacy_proof\" messages containing zero-knowledge proofs of differential privacy adherence for each model update. The verifier responds with \"compliance_status\" messages indicating verification success or failure with specific error codes. Audit trail generation uses \"audit_log\" messages that record privacy budget consumption, aggregation participation, and compliance verification events. All audit messages MUST be cryptographically signed and include tamper-evident sequence numbers for regulatory compliance.\n\nThe protocol defines standardized error handling through HTTP status codes [RFC9110] and structured error responses. Privacy budget exhaustion MUST return HTTP 429 with a \"privacy_budget_exceeded\" error code and remaining budget information. Tenant isolation violations MUST return HTTP 403 with detailed violation descriptions for security monitoring. Implementation-specific extensions MAY add custom message fields using the \"extensions\" object within the message envelope, provided they do not compromise privacy guarantees or tenant isolation requirements. Reference implementations MUST provide JSON Schema validation files and OpenAPI specifications for all message formats to ensure interoperability across different PPALP implementations.\n\nExample message flows demonstrate typical protocol interactions including session establishment, multi-round federated learning with privacy budget management, and graceful session termination with privacy compliance verification. The appendix includes complete JSON Schema definitions for all message types, sample message exchanges showing proper privacy parameter negotiation, and error handling examples covering common failure scenarios such as aggregation timeouts and privacy proof validation failures.", "PPALP addresses multiple security threats present in multi-tenant federated learning environments through defense-in-depth mechanisms. The protocol MUST implement protections against gradient inversion attacks [RFC8446] by ensuring differential privacy noise addition occurs before any parameter sharing, with privacy budgets calculated according to composition theorems defined in Section 5. Membership inference attacks are mitigated through tenant isolation enforcement, where each tenant's Privacy Compliance Verifier cryptographically verifies that cross-tenant information leakage remains below specified epsilon-delta bounds. Model poisoning attacks are addressed through secure aggregation protocols that include Byzantine-fault-tolerant mechanisms and zero-knowledge proofs of honest participation.\n\nKey management in PPALP follows a hierarchical trust model where the Federated Coordinator maintains root cryptographic credentials, while each tenant operates independent key hierarchies for their agent populations. Tenant Gateways MUST establish mutually authenticated channels using TLS 1.3 [RFC8446] with certificate pinning, and rotate encryption keys according to the privacy budget consumption rate to ensure forward secrecy. The protocol supports both pre-shared keys for closed consortium deployments and public key infrastructure integration [RFC5280] for open multi-tenant scenarios. Cross-tenant cryptographic isolation MUST be maintained such that compromise of one tenant's keys cannot affect the privacy guarantees of other tenants' learning processes.\n\nOperational security considerations for multi-tenant deployments include audit logging of all privacy budget expenditures, secure deletion mechanisms for ephemeral cryptographic material, and side-channel attack mitigation. The protocol MUST log all differential privacy parameter selections, aggregation events, and privacy compliance verification results to enable post-hoc security analysis while ensuring these logs themselves do not leak private information. Network-level protections SHOULD include traffic analysis resistance through padding and timing obfuscation, particularly for Agent Gateway intercommunication as specified in [draft-han-rtgwg-agent-gateway-intercomm-framework]. Deployments MUST implement secure boot and attestation mechanisms for Privacy Compliance Verifiers to prevent tampering with privacy enforcement logic.\n\nThe protocol provides resistance to collusion attacks through cryptographic commitment schemes and verifiable privacy budget accounting across multiple learning rounds. When tenant subsets attempt to correlate their local model updates to infer information about non-colluding tenants, the secure aggregation protocol ensures that only differentially private aggregate statistics are revealed. However, the protocol assumes honest-but-curious adversaries and MAY NOT provide protection against active adversaries who deviate from the protocol specification. Deployments requiring protection against malicious adversaries SHOULD implement additional verification mechanisms and consider reduced privacy parameters to account for adaptive attacks over multiple learning epochs.", "This document defines new protocol elements that require registration with the Internet Assigned Numbers Authority (IANA) to ensure interoperability and avoid conflicts with existing protocols. The Privacy-Preserving Agent Learning Protocol (PPALP) introduces novel message types, error codes, and protocol identifiers that extend existing federated learning and privacy-preserving computation frameworks as defined in [draft-kale-agntcy-federated-privacy].\n\nIANA SHALL establish a new registry titled \"Privacy-Preserving Agent Learning Protocol (PPALP) Parameters\" under the \"AI Agent Protocol Parameters\" registry group. This registry SHALL contain three sub-registries: \"PPALP Message Types\", \"PPALP Error Codes\", and \"PPALP Privacy Compliance Identifiers\". The PPALP Message Types registry SHALL include entries for differential privacy update messages (DPUPDATE), secure aggregation requests (SECAGG_REQ), secure aggregation responses (SECAGG_RESP), privacy budget allocation messages (PRIVBUDGET), tenant isolation verification messages (TENANT_VERIFY), and zero-knowledge proof submission messages (ZKP_SUBMIT). Each message type SHALL be assigned a unique 16-bit identifier following the allocation policies specified in [RFC8259] for JSON-based protocol extensions.\n\nThe PPALP Error Codes registry SHALL define error conditions specific to privacy-preserving federated learning operations. Required error codes include PRIVACY_BUDGET_EXCEEDED (4001), TENANT_ISOLATION_VIOLATION (4002), DIFFERENTIAL_PRIVACY_VERIFICATION_FAILED (4003), SECURE_AGGREGATION_TIMEOUT (4004), INSUFFICIENT_PRIVACY_PARAMETERS (4005), and ZKP_VERIFICATION_FAILED (4006). These error codes extend the HTTP status code space as defined in [RFC9110] and SHALL follow the expert review allocation policy with designated experts appointed from the privacy-preserving computation and federated learning communities.\n\nThe PPALP Privacy Compliance Identifiers registry SHALL contain standardized identifiers for privacy mechanisms, verification schemes, and compliance frameworks used within the protocol. This registry SHALL include identifiers for differential privacy mechanisms (DP_GAUSSIAN, DP_LAPLACE), secure aggregation protocols (SECAGG_BFV, SECAGG_CKKS), and zero-knowledge proof systems (ZKP_BULLETPROOFS, ZKP_PLONK). Registration policies for this registry SHALL follow the Specification Required policy as defined in [RFC2119], with specifications requiring formal privacy analysis and security proofs. Updates to these registries SHALL be coordinated with the broader AI agent protocol standardization efforts referenced in [draft-berlinai-vera] to ensure consistency across related privacy-preserving agent communication protocols."]

full_text:
  Internet-Draft                                                      lake
  Intended status: standards-track                              March 2026
  Expires: September 05, 2026


           Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments
           draft-agent-ecosystem-agent-learning-protocol-00

  Abstract

     Federated learning enables distributed AI agents to
     collaboratively improve models without centralizing training data.
     However, existing federated architectures lack comprehensive
     privacy guarantees for multi-tenant environments where agents from
     different organizations must learn together while preventing data
     leakage. This specification defines the Privacy-Preserving Agent
     Learning Protocol (PPALP) that combines differential privacy,
     secure aggregation, and zero-knowledge proofs to enable federated
     agent learning with formal privacy guarantees. PPALP provides
     mechanisms for privacy-preserving model updates, tenant isolation
     enforcement, and cryptographic verification of privacy compliance.
     The protocol supports heterogeneous agent types while maintaining
     backward compatibility with existing federated learning
     frameworks. This work addresses critical privacy gaps in federated
     agent architectures and provides implementable solutions for
     secure cross-organizational AI collaboration.

  Status of This Memo

     This Internet-Draft is submitted in full conformance with the
     provisions of BCP 78 and BCP 79.

     This document is intended to have standards-track status.
     Distribution of this memo is unlimited.

  Terminology

     The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
     NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
     "MAY", and "OPTIONAL" in this document are to be interpreted as
     described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
     appear in all capitals, as shown here.

     Privacy Budget
        A quantitative measure of the privacy cost incurred by
        releasing information about a dataset through differential
        privacy mechanisms

     Tenant Isolation
        The property that agent learning activities of one
        organizational tenant cannot infer private information about
        another tenant's data or models

     Secure Aggregation
        A cryptographic protocol that allows computation of aggregate
        statistics over distributed data without revealing individual
        contributions

     Gradient Inversion Attack
        An attack where an adversary attempts to reconstruct training
        data from shared gradient information in federated learning

     Privacy Compliance Verifier
        A system component that cryptographically verifies adherence to
        differential privacy guarantees and tenant isolation
        requirements

     Federated Coordinator
        The central entity responsible for orchestrating privacy-
        preserving model aggregation across multiple tenants in
        federated learning


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement  ...........................................  5
     4.  Privacy-Preserving Agent Learning Architecture  ..............  6
     5.  Differential Privacy for Agent Model Updates  ................  7
     6.  Secure Multi-Tenant Federated Aggregation  ...................  8
     7.  Privacy Compliance and Verification  .........................  9
     8.  Protocol Message Formats and Flows  ..........................  10
     9.  Security Considerations  .....................................  11
     10.  IANA Considerations  .........................................  12
     11.  References  ..................................................  13

  1.  Introduction

     The rapid deployment of AI agents across organizational boundaries
     has created an urgent need for collaborative learning mechanisms
     that preserve privacy while enabling knowledge sharing.
     Traditional centralized machine learning approaches require data
     consolidation, which violates privacy regulations and
     organizational policies in multi-tenant environments where
     competing organizations must collaborate. Federated learning
     addresses data centralization concerns but existing protocols lack
     comprehensive privacy guarantees necessary for cross-
     organizational AI agent deployments. Current federated learning
     frameworks are vulnerable to gradient inversion attacks [draft-
     kale-agntcy-federated-privacy], membership inference attacks, and
     cross-tenant data reconstruction, making them unsuitable for
     privacy-sensitive multi-tenant agent learning scenarios.

     Multi-tenant federated agent learning presents unique challenges
     beyond traditional federated learning contexts. AI agents often
     process behavioral data, decision patterns, and strategic
     information that require stronger privacy protections than
     conventional machine learning datasets. The heterogeneous nature
     of agent types across organizations complicates model aggregation
     while maintaining meaningful learning outcomes. Existing privacy-
     preserving techniques such as differential privacy and secure
     aggregation, when applied independently, fail to provide
     comprehensive protection against sophisticated attacks targeting
     federated agent systems. Furthermore, regulatory compliance
     requirements such as GDPR's right to erasure and data processing
     transparency mandates necessitate cryptographic verification
     mechanisms that current federated learning protocols do not
     provide.

     This specification introduces the Privacy-Preserving Agent
     Learning Protocol (PPALP) to address critical privacy gaps in
     federated agent architectures while enabling secure cross-
     organizational AI collaboration. PPALP combines differential
     privacy mechanisms specifically calibrated for agent model
     parameters, secure multi-party computation for cross-tenant
     aggregation, and zero-knowledge proofs for cryptographic
     verification of privacy compliance. The protocol builds upon the
     Privacy-Preserving Reference Architecture defined in [draft-kale-
     agntcy-federated-privacy] and extends federated averaging
     mechanisms to support heterogeneous agent types with formal
     privacy guarantees. PPALP integrates with existing federated
     learning frameworks through standardized APIs while providing
     backward compatibility for legacy deployments.

     The protocol's contribution to AI agent standardization includes
     formal privacy accounting mechanisms that track privacy budget
     consumption across multiple learning rounds, tenant isolation
     enforcement that prevents cross-organizational information
     leakage, and cryptographic audit trails that enable regulatory
     compliance verification. PPALP addresses the critical need for
     standardized privacy-preserving protocols in federated agent
     learning by providing implementable solutions that balance
     collaborative learning benefits with stringent privacy
     requirements. The specification defines concrete message formats,
     communication flows, and security mechanisms necessary for
     production deployment of privacy-preserving federated agent
     learning systems in multi-tenant environments where traditional
     approaches fail to provide adequate protection.

  2.  Terminology

     This document defines terminology for privacy-preserving federated
     learning protocols in multi-tenant environments. The key words
     "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD",
     "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
     "OPTIONAL" in this document are to be interpreted as described in
     BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
     capitals, as shown here.

     Agent: An autonomous software entity capable of learning and
     adapting its behavior through machine learning mechanisms, as
     defined in [draft-berlinai-vera]. In the context of this
     specification, agents participate in federated learning to improve
     their models collaboratively.

     Federated Agent Learning: A distributed machine learning paradigm
     where multiple AI agents collaboratively train shared models
     without centralizing their training data. Each agent maintains
     local data while contributing to global model improvement through
     privacy-preserving parameter sharing mechanisms.

     Privacy Budget: A quantitative measure of the privacy cost
     incurred by releasing information about a dataset through
     differential privacy mechanisms. The privacy budget, typically
     denoted as epsilon (ε), bounds the maximum privacy loss across all
     learning iterations and MUST be carefully managed to maintain
     formal privacy guarantees throughout the federated learning
     process.

     Differential Privacy: A mathematical framework that provides
     formal privacy guarantees by adding calibrated noise to
     computation outputs. In federated agent learning, differential
     privacy mechanisms ensure that the participation of any individual
     data point cannot be inferred from the released model parameters
     or gradients.

     Tenant: An organizational entity participating in federated
     learning with its own collection of agents, data, and privacy
     requirements. Tenants represent distinct administrative domains
     that require isolation guarantees while participating in
     collaborative learning protocols.

     Tenant Isolation: The property that agent learning activities of
     one organizational tenant cannot infer private information about
     another tenant's data or models. Tenant isolation MUST be
     maintained through cryptographic mechanisms and differential
     privacy guarantees to prevent cross-organizational data leakage.

     Secure Aggregation: A cryptographic protocol that allows
     computation of aggregate statistics over distributed data without
     revealing individual contributions. Secure aggregation protocols
     typically employ techniques such as homomorphic encryption or
     secure multiparty computation to ensure that only the aggregated
     result is revealed to participants.

     Federated Coordinator: The central entity responsible for
     orchestrating privacy-preserving model aggregation across multiple
     tenants in federated learning. The coordinator manages
     communication flows, enforces privacy policies, and coordinates
     the secure aggregation process while maintaining tenant isolation
     guarantees.

     Privacy Compliance Verifier: A system component that
     cryptographically verifies adherence to differential privacy
     guarantees and tenant isolation requirements. The verifier employs
     zero-knowledge proofs and audit mechanisms to ensure that all
     participants comply with established privacy policies without
     revealing sensitive information about their contributions.

     Gradient Inversion Attack: An attack where an adversary attempts
     to reconstruct training data from shared gradient information in
     federated learning. Such attacks exploit the information leakage
     inherent in gradient sharing and represent a primary threat that
     privacy-preserving protocols MUST defend against.

     Membership Inference Attack: An attack technique where an
     adversary attempts to determine whether a specific data point was
     used in training a machine learning model. In federated settings,
     these attacks can be used to infer information about individual
     agents' training data across tenant boundaries.

     Privacy Accounting: The process of tracking and managing privacy
     budget consumption across multiple learning rounds and operations.
     Privacy accounting mechanisms MUST ensure that the cumulative
     privacy loss remains within acceptable bounds throughout the
     entire federated learning process.

     Homomorphic Encryption: A cryptographic technique that allows
     computation on encrypted data without decrypting it first. In the
     context of secure aggregation, homomorphic encryption enables the
     computation of aggregate model parameters while keeping individual
     contributions encrypted.

     Zero-Knowledge Proof: A cryptographic protocol that allows one
     party to prove knowledge of information to another party without
     revealing the information itself. Zero-knowledge proofs are used
     in privacy compliance verification to demonstrate adherence to
     privacy requirements without exposing sensitive details about the
     verification process.

  3.  Problem Statement

     Federated agent learning in multi-tenant environments faces
     fundamental privacy leakage risks that existing protocols fail to
     adequately address. The core problem arises when AI agents from
     different organizations (tenants) participate in collaborative
     learning while sharing gradient updates or model parameters that
     inadvertently reveal sensitive information about their private
     training data. Unlike traditional federated learning scenarios
     with homogeneous participants, multi-tenant environments amplify
     privacy risks due to potentially adversarial relationships between
     tenants and the heterogeneous nature of agent architectures across
     organizational boundaries.

     The threat model for privacy-preserving federated agent learning
     encompasses three primary attack vectors that adversaries MAY
     exploit to compromise tenant data privacy. First, gradient
     inversion attacks allow malicious participants to reconstruct
     approximations of private training samples from shared gradient
     information, particularly effective when batch sizes are small or
     when attackers can observe multiple training rounds. Second,
     membership inference attacks enable adversaries to determine
     whether specific data points were used in another tenant's
     training dataset by analyzing model behavior or parameter updates.
     Third, cross-tenant data reconstruction attacks leverage
     correlations between multiple tenants' shared information to infer
     sensitive attributes about datasets belonging to other
     organizational participants, violating tenant isolation
     requirements.

     The privacy leakage problem is quantified through formal privacy
     metrics that MUST account for composition across multiple learning
     rounds and tenant interactions. Let ε represent the privacy budget
     expended per tenant per learning round, and δ represent the
     probability of privacy violation. The cumulative privacy cost
     grows with each parameter sharing event, and without proper
     privacy accounting mechanisms, the total privacy loss (εtotal,
     δtotal) MAY exceed acceptable thresholds defined by organizational
     privacy policies. Traditional differential privacy mechanisms
     designed for single-organization scenarios provide insufficient
     guarantees in multi-tenant federated settings where privacy
     budgets must be allocated across tenant boundaries and learning
     rounds while maintaining utility for collaborative model
     improvement.

     Building upon the AI Agent Structured Threat Model framework
     defined in [draft-berlinai-vera], federated agent learning
     environments face additional risks including compromised privacy
     compliance verifiers that MAY provide false attestations of
     differential privacy adherence, tenant gateway failures that could
     leak cross-tenant information, and coordination attacks where
     multiple tenants collude to circumvent privacy protections. The
     threat model assumes honest-but-curious federated coordinators who
     follow protocol specifications but MAY attempt to infer private
     information from aggregated data, and potentially malicious
     tenants who MAY deviate from prescribed privacy-preserving
     mechanisms to gain competitive advantages through unauthorized
     data access.

     The problem statement also addresses the challenge of maintaining
     privacy guarantees while supporting heterogeneous agent
     architectures with varying computational capabilities, privacy
     requirements, and trust assumptions. Agents operating under
     different privacy regulations (such as GDPR, CCPA, or sector-
     specific requirements) MUST be able to participate in federated
     learning while maintaining compliance with their respective
     privacy obligations. The protocol MUST provide mechanisms for
     cryptographic verification of privacy compliance, audit trails for
     regulatory reporting, and graceful degradation when privacy
     budgets are exhausted, ensuring that collaborative learning can
     continue while respecting individual tenant privacy constraints
     and maintaining the overall utility of the federated learning
     process.

  4.  Privacy-Preserving Agent Learning Architecture

     This section defines the Privacy-Preserving Agent Learning
     Protocol (PPALP) architecture, which integrates differential
     privacy, secure aggregation, and zero-knowledge proofs to enable
     federated learning across organizational boundaries while
     maintaining formal privacy guarantees. The architecture extends
     the Privacy-Preserving Reference Architecture framework [draft-
     kale-agntcy-federated-privacy] to specifically address agent
     learning scenarios in multi-tenant environments. PPALP employs a
     layered approach where privacy mechanisms are enforced at multiple
     architectural levels to provide defense-in-depth protection
     against information leakage.

     The PPALP architecture defines four primary component roles that
     collectively ensure privacy-preserving federated learning
     operations. The Federated Coordinator serves as the central
     orchestration entity responsible for managing global model state,
     coordinating aggregation rounds, and maintaining privacy budget
     allocations across all participating tenants. Tenant Gateways act
     as intermediary control plane entities, extending the Agent
     Gateway Control Plane concept [draft-li-dmsc-mcps-agw] to enforce
     tenant-specific privacy policies and manage agent authentication
     within organizational boundaries. Privacy Compliance Verifiers
     operate as independent auditing entities that cryptographically
     verify adherence to differential privacy guarantees and tenant
     isolation requirements through zero-knowledge proofs.
     Participating Agents represent the distributed learning entities
     that contribute model updates while consuming privacy budget
     allocated by their respective tenants.

     The architectural design enforces strict tenant isolation through
     cryptographic boundaries and policy enforcement mechanisms. Each
     tenant maintains an isolated privacy budget that cannot be
     influenced by other tenants' learning activities, ensuring that
     cross-tenant privacy guarantees remain intact regardless of
     adversarial behavior by other participants. Tenant Gateways
     implement the Federated Policy Enforcement framework [draft-cui-
     dmsc-agent-cdi] to harmonize local privacy policies with global
     federated learning requirements while preventing policy conflicts
     that could compromise privacy guarantees. All inter-component
     communications MUST use TLS 1.3 [RFC8446] with mutual
     authentication based on X.509 certificates [RFC5280] to prevent
     network-level attacks.

     The protocol supports heterogeneous agent types through a
     standardized privacy-preserving model update interface that
     abstracts underlying model architectures while ensuring consistent
     privacy accounting. Agents MUST register their learning
     capabilities and privacy requirements with their tenant's gateway,
     which negotiates appropriate differential privacy parameters with
     the Federated Coordinator. The architecture maintains backward
     compatibility with existing federated learning frameworks by
     implementing standard aggregation interfaces that can be secured
     through the PPALP privacy mechanisms without requiring
     modifications to existing agent implementations.

     Privacy accounting occurs at multiple architectural layers to
     provide comprehensive protection against composition attacks and
     privacy budget exhaustion. The Federated Coordinator maintains
     global privacy budget state and implements composition theorems to
     track cumulative privacy expenditure across learning rounds.
     Tenant Gateways enforce local privacy policies and MAY implement
     additional privacy amplification techniques such as subsampling to
     reduce privacy costs for their constituent agents. Privacy
     Compliance Verifiers continuously monitor privacy budget
     consumption and generate cryptographic proofs of compliance that
     can be audited by external parties without revealing sensitive
     learning parameters or model information.

  5.  Differential Privacy for Agent Model Updates

     This section specifies the differential privacy mechanisms that
     MUST be applied to agent model parameter updates to provide formal
     privacy guarantees in multi-tenant federated learning
     environments. All participating agents MUST implement differential
     privacy mechanisms that satisfy ε-differential privacy for a pre-
     negotiated privacy parameter ε, where smaller values of ε provide
     stronger privacy guarantees but may reduce model utility. The
     differential privacy implementation MUST use the Gaussian
     mechanism for continuous-valued parameters and the exponential
     mechanism for discrete-valued selections, calibrated to the global
     sensitivity of the learning algorithm as defined in [draft-kale-
     agntcy-federated-privacy].

     Privacy budget allocation MUST be managed across multiple learning
     rounds to prevent privacy depletion over time. Each tenant MUST
     maintain a privacy budget tracker that accounts for the cumulative
     privacy cost across all federated learning rounds, where the total
     privacy cost is bounded by composition theorems. The system MUST
     implement advanced composition using the moments accountant method
     or Rényi differential privacy to achieve tighter privacy bounds
     than basic composition would allow. Privacy budgets SHOULD be
     allocated proportionally to the expected utility gain from each
     learning round, with mechanisms for tenants to reserve budget for
     future high-priority learning tasks.

     Noise calibration for model parameter updates MUST be performed
     based on the L2 sensitivity of the gradient computation, which is
     bounded through gradient clipping techniques. Before applying
     differential privacy noise, gradients MUST be clipped to a maximum
     norm C, where C is negotiated during protocol initialization based
     on the trade-off between privacy, utility, and convergence
     requirements. The Gaussian noise added to each parameter update
     MUST have variance σ² = 2C²ln(1.25/δ)/ε² for (ε,δ)-differential
     privacy, where δ represents the probability of privacy failure and
     MUST be set to be cryptographically negligible (typically δ ≤
     10⁻⁶).

     The protocol MUST maintain detailed privacy accounting across
     learning rounds using a standardized privacy ledger format that
     records the privacy cost of each operation. This ledger MUST track
     per-tenant privacy expenditure, cross-tenant privacy interactions,
     and cumulative privacy debt according to the composition theorems.
     The privacy accounting system MUST provide cryptographic proofs of
     privacy budget compliance that can be verified by Privacy
     Compliance Verifiers as specified in Section 7. When a tenant's
     privacy budget is exhausted, the system MUST either terminate that
     tenant's participation or implement privacy budget renewal
     mechanisms with appropriate cooling-off periods to prevent privacy
     budget laundering attacks.

     The differential privacy implementation MUST be compatible with
     the secure aggregation protocols defined in Section 6, ensuring
     that privacy guarantees are maintained even when combined with
     cryptographic operations. The noise addition process MUST occur
     before any cryptographic encoding for secure aggregation, and the
     protocol MUST verify that the combination of differential privacy
     and secure aggregation does not introduce additional privacy
     vulnerabilities through cryptographic side channels or timing
     attacks. All differential privacy parameters, including ε, δ, and
     clipping bounds, MUST be negotiated during the protocol handshake
     phase and cryptographically committed to prevent manipulation
     during the learning process.

  6.  Secure Multi-Tenant Federated Aggregation

     This section defines the cryptographic protocols that enable
     secure aggregation of model parameters across tenant boundaries
     while preserving privacy guarantees established in Section 5. The
     secure aggregation protocol MUST ensure that no individual tenant
     can learn information about other tenants' model updates beyond
     what is revealed by the final aggregated result. The protocol
     combines homomorphic encryption with secure multiparty computation
     techniques to achieve this property while maintaining
     computational efficiency suitable for practical federated learning
     deployments.

     The Multi-Tenant Federated Averaging mechanism serves as the
     foundation for secure parameter aggregation. Each tenant's Privacy
     Compliance Verifier MUST encrypt their differentially-private
     model updates using a threshold homomorphic encryption scheme
     before transmission to the Federated Coordinator. The encryption
     MUST use a (t,n)-threshold scheme where t represents the minimum
     number of tenants required for decryption and n represents the
     total number of participating tenants. This ensures that no single
     tenant or coordinator can decrypt individual updates, while still
     enabling computation over encrypted parameters. The homomorphic
     encryption scheme SHOULD support both addition and limited
     multiplication operations to accommodate various neural network
     architectures commonly used in agent learning systems.

     The secure multiparty computation protocol proceeds in three
     phases: commitment, computation, and verification. During the
     commitment phase, each tenant generates a cryptographic commitment
     to their encrypted model update using a computationally-hiding and
     perfectly-binding commitment scheme. The Federated Coordinator
     collects all commitments and broadcasts them to participating
     tenants for verification. In the computation phase, tenants
     collaboratively execute a secure aggregation protocol that
     computes the weighted average of model parameters without
     revealing individual contributions. The protocol MUST be secure
     against up to t-1 malicious participants and SHOULD provide
     robustness against participant dropout through verifiable secret
     sharing techniques.

     Verification mechanisms ensure correctness of the aggregation
     process and detect potential manipulation attempts. Each
     participating tenant MUST verify the integrity of the aggregation
     using zero-knowledge proofs that demonstrate correct execution of
     the secure computation protocol. The ZKP-based Agent Attestation
     mechanism enables tenants to prove their compliance with the
     differential privacy requirements specified in Section 5 without
     revealing their actual noise parameters or model updates. These
     proofs MUST be verified by the Federated Coordinator and recorded
     in an immutable audit trail as specified in Section 7.
     Additionally, the protocol includes range proofs to verify that
     model parameter updates fall within expected bounds, preventing
     gradient manipulation attacks that could compromise model
     convergence or extract private information.

     The aggregation protocol MUST support heterogeneous agent types by
     normalizing model parameters to a common representation before
     encryption and aggregation. Tenants with different model
     architectures encode their updates using a standardized parameter
     mapping that preserves semantic meaning while enabling secure
     computation. The protocol SHOULD maintain backward compatibility
     with existing federated learning frameworks by providing adapter
     layers that translate between PPALP message formats defined in
     Section 8 and legacy federated learning APIs. Performance
     optimizations including parameter compression, selective
     aggregation of model layers, and asynchronous update mechanisms
     MAY be implemented provided they do not compromise the privacy
     guarantees established by the differential privacy and secure
     aggregation components.

  7.  Privacy Compliance and Verification

     Privacy compliance in federated agent learning environments
     requires cryptographic mechanisms that provide verifiable proof of
     adherence to differential privacy guarantees while maintaining
     audit capabilities for regulatory requirements. This section
     defines the Privacy Compliance and Verification framework that
     enables tenants to demonstrate privacy compliance without
     revealing sensitive implementation details or compromising the
     privacy of their learning processes. The framework combines zero-
     knowledge proofs, selective disclosure attestations, and
     cryptographic erasure mechanisms to provide comprehensive privacy
     verification capabilities.

     The Privacy Compliance Verifier MUST implement zero-knowledge
     proof protocols that allow federated agents to demonstrate
     differential privacy adherence without revealing actual noise
     values, data distributions, or model parameters. Following the SD-
     JWT Privacy-Preserving Trust Proof pattern from [draft-drake-
     email-tpm-attestation], the verifier SHALL use Selective
     Disclosure JWT mechanisms [RFC7519] to enable privacy-preserving
     attestation of compliance properties. Each participating agent
     MUST generate cryptographic proofs that demonstrate: (1) proper
     calibration of differential privacy noise according to the
     allocated privacy budget, (2) correct implementation of the
     Gaussian mechanism or equivalent noise generation, and (3)
     adherence to composition theorems across multiple learning rounds.
     These proofs MUST be constructed using non-interactive zero-
     knowledge proof systems such as zk-SNARKs or zk-STARKs that
     provide succinctness and efficient verification.

     Audit trail mechanisms MUST maintain cryptographically verifiable
     logs of all privacy-relevant operations while preserving the
     confidentiality of sensitive learning data. The audit system SHALL
     record privacy budget expenditures, aggregation operations, and
     tenant isolation boundary enforcement without logging actual model
     parameters or gradient values. Each audit entry MUST include a
     timestamp, tenant identifier, privacy budget allocation, and
     cryptographic hash of the differential privacy parameters used.
     The audit trail MUST support selective disclosure to authorized
     auditors while preventing unauthorized access to privacy-sensitive
     operational details. Privacy budget accounting MUST be maintained
     across all learning rounds with cryptographic commitments that
     prevent double-spending of privacy allocations.

     Cryptographic erasure capabilities MUST be implemented to address
     regulatory requirements for data deletion and agent memory cleanup
     as specified in [draft-gaikwad-aps-profile]. The system SHALL
     support cryptographic erasure of agent memory associated with
     specific user or agent identities through key destruction
     mechanisms that render associated encrypted data permanently
     inaccessible. When a deletion request is received, the Privacy
     Compliance Verifier MUST coordinate the erasure of all federated
     learning artifacts associated with the specified identity,
     including gradient histories, model update records, and privacy
     budget allocations. The erasure process MUST generate
     cryptographic proofs of deletion that demonstrate the irreversible
     destruction of the requested data across all participating
     federated nodes.

     Privacy compliance verification MUST support Privacy-Respecting
     Capability Attestation mechanisms that enable verification of
     agent privacy capabilities without revealing implementation-
     specific details. Following patterns from [draft-huang-rats-
     agentic-eat-cap-attest], agents MUST be able to attest to their
     differential privacy implementation capabilities, secure
     aggregation support, and tenant isolation enforcement without
     disclosing proprietary algorithms or security configurations. The
     attestation process SHALL use hardware-backed trust anchors where
     available, with fallback to software-based attestation for
     environments without trusted execution capabilities. All privacy
     compliance proofs and attestations MUST be timestamped and include
     validity periods to prevent replay attacks and ensure freshness of
     compliance claims.

  8.  Protocol Message Formats and Flows

     This section defines the concrete message formats, communication
     flows, and API definitions for the Privacy-Preserving Agent
     Learning Protocol (PPALP). All message exchanges MUST use JSON
     format as specified in [RFC8259] with UTF-8 encoding. Protocol
     implementations MUST support TLS 1.3 [RFC8446] for transport
     security and MUST authenticate using OAuth 2.0 [RFC6749] bearer
     tokens or X.509 certificates [RFC5280]. The protocol defines four
     primary message categories: initialization messages for
     establishing privacy parameters, update messages for transmitting
     differentially private model parameters, aggregation messages for
     secure multi-tenant computation, and verification messages for
     privacy compliance validation.

     The protocol message structure follows a common envelope format
     with mandatory header fields. Each message MUST include a
     "version" field specifying the PPALP protocol version, a "type"
     field indicating the message category, a "tenantid" field for
     tenant isolation enforcement, and a "timestamp" field using ISO
     8601 format. Messages involving privacy budget allocation MUST
     include a "privacybudget" object containing epsilon and delta
     values as defined in Section 5. Security-critical messages such as
     model updates and aggregation requests MUST include a "signature"
     field containing a JWS signature [RFC7519] for message
     authentication and integrity verification.

     The federated learning session begins with a Privacy Parameter
     Negotiation flow initiated by the Federated Coordinator. The
     coordinator sends a "privacyinit" message specifying the global
     privacy budget, differential privacy mechanism, and aggregation
     method. Each Tenant Gateway responds with a "privacyaccept"
     message confirming participation and local privacy constraints.
     Model update flows use "modelupdate" messages containing encrypted
     differentially private gradients, privacy proof attestations, and
     secure aggregation shares. The Federated Coordinator processes
     these through "aggregationrequest" and "aggregation_response"
     messages that implement the secure multi-tenant aggregation
     protocol defined in Section 6.

     Privacy compliance verification occurs through dedicated message
     flows involving the Privacy Compliance Verifier component. Agents
     MUST send "privacyproof" messages containing zero-knowledge proofs
     of differential privacy adherence for each model update. The
     verifier responds with "compliancestatus" messages indicating
     verification success or failure with specific error codes. Audit
     trail generation uses "audit_log" messages that record privacy
     budget consumption, aggregation participation, and compliance
     verification events. All audit messages MUST be cryptographically
     signed and include tamper-evident sequence numbers for regulatory
     compliance.

     The protocol defines standardized error handling through HTTP
     status codes [RFC9110] and structured error responses. Privacy
     budget exhaustion MUST return HTTP 429 with a
     "privacybudgetexceeded" error code and remaining budget
     information. Tenant isolation violations MUST return HTTP 403 with
     detailed violation descriptions for security monitoring.
     Implementation-specific extensions MAY add custom message fields
     using the "extensions" object within the message envelope,
     provided they do not compromise privacy guarantees or tenant
     isolation requirements. Reference implementations MUST provide
     JSON Schema validation files and OpenAPI specifications for all
     message formats to ensure interoperability across different PPALP
     implementations.

     Example message flows demonstrate typical protocol interactions
     including session establishment, multi-round federated learning
     with privacy budget management, and graceful session termination
     with privacy compliance verification. The appendix includes
     complete JSON Schema definitions for all message types, sample
     message exchanges showing proper privacy parameter negotiation,
     and error handling examples covering common failure scenarios such
     as aggregation timeouts and privacy proof validation failures.

  9.  Security Considerations

     PPALP addresses multiple security threats present in multi-tenant
     federated learning environments through defense-in-depth
     mechanisms. The protocol MUST implement protections against
     gradient inversion attacks [RFC8446] by ensuring differential
     privacy noise addition occurs before any parameter sharing, with
     privacy budgets calculated according to composition theorems
     defined in Section 5. Membership inference attacks are mitigated
     through tenant isolation enforcement, where each tenant's Privacy
     Compliance Verifier cryptographically verifies that cross-tenant
     information leakage remains below specified epsilon-delta bounds.
     Model poisoning attacks are addressed through secure aggregation
     protocols that include Byzantine-fault-tolerant mechanisms and
     zero-knowledge proofs of honest participation.

     Key management in PPALP follows a hierarchical trust model where
     the Federated Coordinator maintains root cryptographic
     credentials, while each tenant operates independent key
     hierarchies for their agent populations. Tenant Gateways MUST
     establish mutually authenticated channels using TLS 1.3 [RFC8446]
     with certificate pinning, and rotate encryption keys according to
     the privacy budget consumption rate to ensure forward secrecy. The
     protocol supports both pre-shared keys for closed consortium
     deployments and public key infrastructure integration [RFC5280]
     for open multi-tenant scenarios. Cross-tenant cryptographic
     isolation MUST be maintained such that compromise of one tenant's
     keys cannot affect the privacy guarantees of other tenants'
     learning processes.

     Operational security considerations for multi-tenant deployments
     include audit logging of all privacy budget expenditures, secure
     deletion mechanisms for ephemeral cryptographic material, and
     side-channel attack mitigation. The protocol MUST log all
     differential privacy parameter selections, aggregation events, and
     privacy compliance verification results to enable post-hoc
     security analysis while ensuring these logs themselves do not leak
     private information. Network-level protections SHOULD include
     traffic analysis resistance through padding and timing
     obfuscation, particularly for Agent Gateway intercommunication as
     specified in [draft-han-rtgwg-agent-gateway-intercomm-framework].
     Deployments MUST implement secure boot and attestation mechanisms
     for Privacy Compliance Verifiers to prevent tampering with privacy
     enforcement logic.

     The protocol provides resistance to collusion attacks through
     cryptographic commitment schemes and verifiable privacy budget
     accounting across multiple learning rounds. When tenant subsets
     attempt to correlate their local model updates to infer
     information about non-colluding tenants, the secure aggregation
     protocol ensures that only differentially private aggregate
     statistics are revealed. However, the protocol assumes honest-but-
     curious adversaries and MAY NOT provide protection against active
     adversaries who deviate from the protocol specification.
     Deployments requiring protection against malicious adversaries
     SHOULD implement additional verification mechanisms and consider
     reduced privacy parameters to account for adaptive attacks over
     multiple learning epochs.

  10.  IANA Considerations

     This document defines new protocol elements that require
     registration with the Internet Assigned Numbers Authority (IANA)
     to ensure interoperability and avoid conflicts with existing
     protocols. The Privacy-Preserving Agent Learning Protocol (PPALP)
     introduces novel message types, error codes, and protocol
     identifiers that extend existing federated learning and privacy-
     preserving computation frameworks as defined in [draft-kale-
     agntcy-federated-privacy].

     IANA SHALL establish a new registry titled "Privacy-Preserving
     Agent Learning Protocol (PPALP) Parameters" under the "AI Agent
     Protocol Parameters" registry group. This registry SHALL contain
     three sub-registries: "PPALP Message Types", "PPALP Error Codes",
     and "PPALP Privacy Compliance Identifiers". The PPALP Message
     Types registry SHALL include entries for differential privacy
     update messages (DPUPDATE), secure aggregation requests
     (SECAGGREQ), secure aggregation responses (SECAGGRESP), privacy
     budget allocation messages (PRIVBUDGET), tenant isolation
     verification messages (TENANTVERIFY), and zero-knowledge proof
     submission messages (ZKPSUBMIT). Each message type SHALL be
     assigned a unique 16-bit identifier following the allocation
     policies specified in [RFC8259] for JSON-based protocol
     extensions.

     The PPALP Error Codes registry SHALL define error conditions
     specific to privacy-preserving federated learning operations.
     Required error codes include PRIVACYBUDGETEXCEEDED (4001),
     TENANTISOLATIONVIOLATION (4002),
     DIFFERENTIALPRIVACYVERIFICATIONFAILED (4003),
     SECUREAGGREGATIONTIMEOUT (4004), INSUFFICIENTPRIVACYPARAMETERS
     (4005), and ZKPVERIFICATION_FAILED (4006). These error codes
     extend the HTTP status code space as defined in [RFC9110] and
     SHALL follow the expert review allocation policy with designated
     experts appointed from the privacy-preserving computation and
     federated learning communities.

     The PPALP Privacy Compliance Identifiers registry SHALL contain
     standardized identifiers for privacy mechanisms, verification
     schemes, and compliance frameworks used within the protocol. This
     registry SHALL include identifiers for differential privacy
     mechanisms (DPGAUSSIAN, DPLAPLACE), secure aggregation protocols
     (SECAGGBFV, SECAGGCKKS), and zero-knowledge proof systems
     (ZKPBULLETPROOFS, ZKPPLONK). Registration policies for this
     registry SHALL follow the Specification Required policy as defined
     in [RFC2119], with specifications requiring formal privacy
     analysis and security proofs. Updates to these registries SHALL be
     coordinated with the broader AI agent protocol standardization
     efforts referenced in [draft-berlinai-vera] to ensure consistency
     across related privacy-preserving agent communication protocols.

  11.  References

  11.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8259]
           RFC 8259

     [RFC 7519]
           RFC 7519

     [RFC 8446]
           RFC 8446

     [draft-kale-agntcy-federated-privacy]
           draft-kale-agntcy-federated-privacy

  11.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 9110]
           RFC 9110

     [RFC 5280]
           RFC 5280

     [draft-berlinai-vera]
           draft-berlinai-vera

     [draft-huang-acme-scalable-agent-enrollment]
           draft-huang-acme-scalable-agent-enrollment

     [draft-messous-eat-ai]
           draft-messous-eat-ai

     [draft-li-dmsc-mcps-agw]
           draft-li-dmsc-mcps-agw

     [draft-gaikwad-aps-profile]
           draft-gaikwad-aps-profile

     [draft-drake-email-tpm-attestation]
           draft-drake-email-tpm-attestation

     [draft-huang-rats-agentic-eat-cap-attest]
           draft-huang-rats-agentic-eat-cap-attest


  Author's Address

     Generated by IETF Draft Analyzer
     Family: agent-ecosystem
     2026-03-04

rating_json:
  {"s": "Proposes PPALP, a federated learning protocol combining differential privacy, secure aggregation, and zero-knowledge proofs for privacy-preserving collaborative AI agent training across multi-tenant environments. Addresses data leakage risks in cross-organizational federated learning with formal privacy guarantees and tenant isolation mechanisms.", "n": 2, "nn": "Combines established privacy techniques (differential privacy, secure aggregation, ZKPs) in federated learning context; incremental rather than foundational contribution to known privacy-preserving ML approaches.", "m": 2, "mn": "Early draft (00) from March 2026; lacks detailed protocol specifications, implementation guidance, security proofs, and empirical validation; no evidence of working implementations or deployment experience.", "o": 4, "on": "Significant overlap with existing federated learning privacy literature (FedAvg variants, DP-SGD, secure aggregation protocols); combination is relatively straightforward application of known techniques.", "mo": 1, "mon": "Single draft version with no revision history; no indicators of author engagement, community discussion, or implementation adoption; appears dormant or nascent.", "r": 3, "rn": "Addresses genuine privacy concerns in federated agent systems relevant to multi-tenant scenarios; however, scope and applicability to agent protocols specifically remains unclear; may be misaligned with IETF core mission.", "c": ["Policy/governance", "Agent identity/auth", "Data formats/interop", "Other AI/agent"]}


gap_topic: Agent Task DAG
draft_name: draft-ai-agent-task-a-00
title: Agent Task DAG: A Framework for Directed Acyclic Graph Execution in Multi-Agent Systems
family_name: agent-ecosystem
family_role: ATD
status: draft
created_at: 2026-03-04T00:28:23.726180+00:00
abstract:
  As AI agent systems become increasingly complex, there is a growing need for structured approaches to orchestrate multi-step tasks across multiple autonomous agents. This document defines the Agent Task DAG (Directed Acyclic Graph) framework, which provides a standardized approach for representing, executing, and managing complex workflows in multi-agent environments. The framework addresses key challenges including task decomposition, dependency management, parallel execution, failure recovery, and human oversight integration. By building upon existing agent authorization profiles and task negotiation protocols, this specification enables agents to coordinate complex workflows while maintaining security, auditability, and the ability to incorporate human-in-the-loop decision points. The framework supports both fast execution in trusted environments and rigorous verification in regulated contexts through configurable assurance profiles.

outline_json:
  {"title": "Agent Task DAG: A Framework for Directed Acyclic Graph Execution in Multi-Agent Systems", "abstract": "As AI agent systems become increasingly complex, there is a growing need for structured approaches to orchestrate multi-step tasks across multiple autonomous agents. This document defines the Agent Task DAG (Directed Acyclic Graph) framework, which provides a standardized approach for representing, executing, and managing complex workflows in multi-agent environments. The framework addresses key challenges including task decomposition, dependency management, parallel execution, failure recovery, and human oversight integration. By building upon existing agent authorization profiles and task negotiation protocols, this specification enables agents to coordinate complex workflows while maintaining security, auditability, and the ability to incorporate human-in-the-loop decision points. The framework supports both fast execution in trusted environments and rigorous verification in regulated contexts through configurable assurance profiles.", "sections": [{"title": "Introduction", "summary": "Introduces the need for structured task orchestration in multi-agent systems and positions the Agent Task DAG framework within the broader AI agent ecosystem. Outlines the relationship to existing protocols and the scope of this specification.", "key_ideas": ["Multi-agent coordination challenges", "DAG execution benefits", "Integration with existing agent protocols"]}, {"title": "Terminology", "summary": "Defines key terms used throughout the specification including Task Node, Agent Task DAG, Execution Context, and Checkpoint. Establishes consistent vocabulary building on existing agent protocol terminology.", "key_ideas": ["Task Node definition", "DAG execution states", "Agent capability bindings"]}, {"title": "Problem Statement", "summary": "Describes the limitations of current agent task coordination approaches and the specific challenges that DAG-based execution addresses. Identifies requirements for scalable, fault-tolerant multi-agent workflows.", "key_ideas": ["Current orchestration limitations", "Dependency management challenges", "Human oversight integration needs"]}, {"title": "Agent Task DAG Framework", "summary": "Defines the core DAG data model including node types, edge relationships, and execution semantics. Specifies how tasks are represented, dependencies are managed, and execution context is maintained.", "key_ideas": ["DAG data structure", "Node execution semantics", "Context propagation"]}, {"title": "Task Execution Protocol", "summary": "Describes the protocol for executing DAG workflows including task scheduling, parallel execution, and coordination between agents. Defines message formats and state synchronization mechanisms.", "key_ideas": ["Execution orchestration", "Agent task negotiation integration", "State synchronization"]}, {"title": "Checkpoint and Recovery Mechanisms", "summary": "Specifies checkpoint creation, rollback procedures, and failure recovery strategies for DAG execution. Ensures workflow resilience and enables human intervention points.", "key_ideas": ["Checkpoint semantics", "Rollback procedures", "Failure recovery strategies"]}, {"title": "Integration with Existing Agent Protocols", "summary": "Details how the DAG framework integrates with existing agent authorization profiles, discovery protocols, and communication mechanisms. Provides protocol-agnostic binding approaches.", "key_ideas": ["AAP integration", "Discovery protocol binding", "Transport layer abstraction"]}, {"title": "Security Considerations", "summary": "Addresses security aspects of DAG execution including task authorization, context isolation, and audit trail requirements. Considers threats specific to multi-agent workflow execution.", "key_ideas": ["Task-level authorization", "Context security", "Audit requirements"]}, {"title": "IANA Considerations", "summary": "Specifies any new registries or protocol identifiers needed for the Agent Task DAG framework.", "key_ideas": ["Protocol identifiers", "Message type registrations"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 8259", "RFC 7519", "draft-aap-oauth-profile", "draft-cui-ai-agent-task", "draft-guy-bary-stamp-protocol"], "informative_refs": ["RFC 6749", "RFC 9110", "draft-chang-agent-context-interaction", "draft-liu-dmsc-acps-arc", "draft-rosenberg-aiproto-framework", "draft-song-oauth-ai-agent-collaborate-authz", "draft-mao-rtgwg-apn-framework-for-ioa", "draft-nandakumar-ai-agent-moq-transport"], "terminology": {"Agent Task DAG": "A directed acyclic graph representing a complex workflow where nodes represent individual tasks and edges represent dependencies between tasks", "Task Node": "An individual unit of work within a DAG that can be executed by one or more agents", "Execution Context": "The runtime environment and state information associated with DAG execution, including agent assignments, intermediate results, and checkpoint data", "Checkpoint": "A persistent snapshot of DAG execution state that enables rollback and recovery operations", "Task Binding": "The association of a task node with specific agent capabilities or agent instances", "DAG Coordinator": "An agent or system component responsible for orchestrating the execution of a complete DAG workflow"}, "target_wg": "anima", "intended_status": "standards-track"}

sections_json:
  ["The increasing sophistication of AI agent systems has created a demand for structured approaches to orchestrate complex, multi-step tasks across autonomous agents. While individual agents have become capable of handling sophisticated reasoning and execution tasks, real-world applications often require coordinating multiple agents to complete workflows that involve parallel processing, sequential dependencies, and dynamic task allocation. Current approaches to multi-agent coordination typically rely on ad-hoc communication patterns or simple request-response chains, which lack the expressiveness and reliability needed for complex enterprise and research applications.\n\nThis document defines the Agent Task DAG (Directed Acyclic Graph) framework, which provides a standardized approach for representing, executing, and managing complex workflows in multi-agent environments. The framework builds upon existing agent protocols, particularly the Agent Authorization Profile [draft-aap-oauth-profile] for security and authorization, and agent task coordination mechanisms [draft-cui-ai-agent-task] for basic task execution. By representing workflows as directed acyclic graphs, the framework enables explicit modeling of task dependencies, parallel execution opportunities, and conditional branching while maintaining guarantees about workflow termination and consistency.\n\nThe Agent Task DAG framework addresses several critical challenges in multi-agent systems: task decomposition and dependency management, efficient parallel execution across heterogeneous agents, robust failure recovery and rollback mechanisms, and integration of human oversight at critical decision points. The framework leverages structured claims for agent context [draft-aap-oauth-profile] to enable context-aware task assignment and supports agent context distribution mechanisms [draft-chang-agent-context-interaction] to maintain coherent state across complex multi-round workflows. This approach ensures that agents can coordinate effectively while maintaining security boundaries and audit trails required in enterprise environments.\n\nThe specification is designed to be protocol-agnostic and can operate over various transport mechanisms including HTTP [RFC9110], message queuing systems, and specialized agent communication protocols. The framework integrates with existing OAuth 2.0 [RFC6749] and JWT [RFC7519] infrastructure through the Agent Authorization Profile, enabling seamless deployment in environments that already support agent authentication and authorization. The DAG representation follows JSON [RFC8259] encoding standards to ensure broad compatibility and easy integration with existing agent development frameworks.\n\nThis document focuses specifically on the DAG execution framework and does not address broader questions of agent discovery, capability matching, or task marketplace mechanisms, which are covered by complementary specifications. The framework assumes the existence of agent authorization infrastructure and builds upon established patterns for agent-to-agent communication while providing the additional structure needed for complex workflow coordination.", "The key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.\n\nThis specification builds upon terminology established in the Agent Authorization Profile [draft-aap-oauth-profile], AI Agent Task specifications [draft-cui-ai-agent-task], and Agent Context Interaction mechanisms [draft-chang-agent-context-interaction]. The following terms are defined for use throughout this document:\n\n**Agent Task DAG**: A directed acyclic graph data structure representing a complex multi-step workflow where nodes correspond to individual tasks and directed edges represent dependency relationships between tasks. The DAG enforces execution ordering constraints while enabling parallel execution of independent task branches. Each DAG maintains metadata including creation time, ownership, and execution policies that govern how the workflow may be executed across multiple agents.\n\n**Task Node**: An individual unit of work within an Agent Task DAG that encapsulates a specific operation to be performed by one or more AI agents. Each task node contains task specifications, input/output schemas, execution constraints, and binding requirements that determine which agents are capable of executing the task. Task nodes maintain state information including execution status, assigned agents, and result data as defined in [draft-cui-ai-agent-task].\n\n**Execution Context**: The runtime environment and associated state information that governs the execution of an Agent Task DAG. The execution context includes agent assignments, intermediate task results, security credentials, operational constraints from Agent Authorization Profiles [draft-aap-oauth-profile], and distributed context information as specified in [draft-chang-agent-context-interaction]. The execution context ensures consistency and provides necessary information for task coordination across multiple agents.\n\n**Checkpoint**: A persistent, immutable snapshot of Agent Task DAG execution state captured at a specific point in time. Checkpoints contain the complete execution context, task completion status, intermediate results, and sufficient metadata to enable rollback and recovery operations. Checkpoints serve as recovery points for failure scenarios and decision points for human-in-the-loop interventions.\n\n**Task Binding**: The process and resulting association between a task node and specific agent capabilities or agent instances that will execute the task. Task binding considers agent authorization profiles, capability matching, resource availability, and security constraints. The binding process may be performed statically during DAG planning or dynamically during execution based on runtime conditions.\n\n**DAG Coordinator**: An agent or system component responsible for orchestrating the complete lifecycle of Agent Task DAG execution. The DAG Coordinator manages task scheduling, monitors execution progress, handles inter-agent communication, enforces security policies, and coordinates checkpoint and recovery operations. The coordinator maintains the authoritative view of DAG execution state and serves as the primary interface for human oversight and intervention.", "Current approaches to multi-agent task coordination suffer from several fundamental limitations that impede the development of robust, scalable autonomous systems. Existing coordination mechanisms typically rely on ad-hoc communication patterns, simple request-response protocols, or basic workflow engines that were not designed for the dynamic, autonomous nature of AI agents. While protocols like those defined in [draft-cui-ai-agent-task] provide foundations for individual task execution, they lack standardized approaches for managing complex workflows involving multiple interdependent tasks across heterogeneous agent populations. The Agent Authorization Profile [draft-aap-oauth-profile] establishes important primitives for agent identity and authorization, but does not address the orchestration challenges that arise when multiple authorized agents must coordinate to complete complex, multi-step objectives.\n\nThe complexity of real-world AI agent applications demands structured approaches to task decomposition and dependency management that current protocols do not adequately address. Agents operating in domains such as scientific research, business process automation, or infrastructure management often require workflows where tasks have intricate dependencies, may execute in parallel when possible, and must handle partial failures gracefully. Without standardized mechanisms for representing these relationships, agent systems resort to brittle, custom coordination logic that is difficult to audit, debug, or modify. The lack of formal workflow representation also prevents effective human oversight integration, as stakeholders cannot easily understand or intervene in complex multi-agent processes.\n\nAgent Context Distribution mechanisms [draft-chang-agent-context-interaction] have demonstrated that context sharing among agents significantly impacts execution success rates, but current approaches do not provide systematic ways to manage context propagation through complex workflows. In multi-step processes, intermediate results from one task often serve as inputs to downstream tasks, creating context dependencies that must be carefully managed to ensure workflow integrity. Existing protocols lack standardized approaches for maintaining execution context across task boundaries, leading to information loss, redundant computation, and coordination failures that compromise overall system reliability.\n\nFault tolerance and recovery represent critical gaps in current multi-agent coordination approaches. Real-world agent systems must handle various failure modes including agent unavailability, task timeouts, resource constraints, and partial execution failures. Without systematic checkpoint and recovery mechanisms, workflows often must restart completely when any component fails, leading to inefficient resource utilization and poor user experience. The absence of standardized rollback capabilities also complicates human intervention scenarios, where domain experts may need to modify workflow parameters or task assignments based on intermediate results or changing requirements.\n\nScalability challenges emerge when current coordination approaches encounter workflows with dozens or hundreds of interdependent tasks distributed across multiple agent instances. Simple centralized coordination quickly becomes a bottleneck, while fully decentralized approaches struggle with consistency and deadlock prevention. The lack of standardized protocols for parallel task execution, resource allocation, and progress monitoring prevents agent systems from efficiently utilizing available computational resources. Additionally, without formal workflow representation, it becomes difficult to optimize task scheduling, predict resource requirements, or provide meaningful progress indicators to human stakeholders.\n\nThese limitations necessitate a framework that provides: structured representation of complex workflows with explicit dependency management; standardized protocols for parallel execution and agent coordination; systematic checkpoint and recovery mechanisms that enable fault tolerance and human intervention; integration with existing agent authorization and context distribution mechanisms; and scalable execution patterns that can accommodate workflows ranging from simple sequential processes to complex parallel computations involving multiple agent populations.", "This section defines the core data model and execution semantics for the Agent Task DAG framework. The framework provides a structured approach for representing complex multi-agent workflows as directed acyclic graphs, where individual tasks are modeled as nodes and dependencies between tasks are represented as edges. The data model builds upon existing agent protocol foundations while introducing specific constructs needed for distributed workflow orchestration.\n\n## 4.1. DAG Data Model\n\nAn Agent Task DAG MUST be represented as a JSON object [RFC8259] that contains the complete specification of a workflow. The DAG structure consists of three primary components: metadata describing the overall workflow, a collection of task nodes representing individual units of work, and dependency relationships that define execution ordering constraints. Each DAG MUST include a unique identifier, version information, and execution parameters that govern how the workflow should be processed.\n\nTask nodes within the DAG represent atomic units of work that can be executed by autonomous agents. Each task node MUST specify its execution requirements, including required agent capabilities, input and output data schemas, and execution constraints such as timeouts or resource limits. Task nodes SHOULD reference standardized task types as defined in [draft-cui-ai-agent-task] where applicable, enabling interoperability across different agent implementations. The task specification MUST include sufficient information for agents to determine their capability to execute the task and negotiate execution parameters.\n\nDependency relationships between task nodes are expressed through edge definitions that establish partial ordering constraints over the DAG. Each edge MUST specify source and target task nodes, with the semantic meaning that the target task cannot begin execution until the source task has completed successfully. Edges MAY include conditional execution logic, allowing for branching workflows based on the results of predecessor tasks. The framework supports both data dependencies, where output from one task serves as input to another, and control dependencies, where task ordering is required for correctness without direct data flow.\n\n## 4.2. Execution Context Management\n\nThe Execution Context provides the runtime environment for DAG processing and maintains state information throughout workflow execution. The execution context MUST track the current state of each task node, intermediate results produced during execution, and metadata about agent assignments for each task. Context information SHOULD be distributed among participating agents using the mechanisms defined in [draft-chang-agent-context-interaction] to ensure consistent state visibility across the multi-agent system.\n\nAgent binding within the execution context associates task nodes with specific agent instances or agent capability requirements. The framework supports both static binding, where task assignments are predetermined before execution begins, and dynamic binding, where task assignments are resolved at runtime based on agent availability and capability matching. When integrated with Agent Authorization Profiles [draft-aap-oauth-profile], the execution context MUST validate that assigned agents possess the necessary authorization claims to execute their bound tasks.\n\nCheckpoint creation within the execution context enables persistent state management and recovery capabilities. The framework MUST support checkpoint creation at configurable intervals, capturing the complete state of DAG execution including task completion status, intermediate results, and current agent assignments. Checkpoints SHOULD be created automatically before task nodes that are marked as requiring human oversight, enabling rollback to known-good states when human intervention modifies the workflow execution path.\n\n## 4.3. Task Execution Semantics\n\nTask execution within the DAG framework follows a coordination model where a DAG Coordinator orchestrates workflow progress while individual agents execute assigned tasks autonomously. The coordinator MUST maintain the global view of DAG state and determine when task dependencies have been satisfied, enabling parallel execution of independent task branches. Task scheduling MUST respect dependency constraints while maximizing parallel execution opportunities to optimize overall workflow completion time.\n\nThe framework defines specific execution states for task nodes including pending, ready, executing, completed, failed, and skipped. State transitions MUST be coordinated through the DAG Coordinator to ensure consistency across the distributed system. When a task transitions to the ready state, the coordinator SHOULD initiate agent assignment and task negotiation protocols to begin execution. Failed tasks MAY trigger rollback procedures or alternate execution paths depending on the configured failure handling policies.\n\nIntegration with existing agent protocols occurs through standardized interfaces that abstract the underlying communication mechanisms. The framework MUST support protocol-agnostic bindings that allow integration with different agent discovery, authorization, and communication protocols. Task execution requests SHOULD include structured claims as defined in [draft-aap-oauth-profile] when agent authorization is required, ensuring that security and audit requirements are maintained throughout the distributed workflow execution.", "The Agent Task DAG execution protocol defines a standardized approach for coordinating the execution of complex workflows across multiple autonomous agents. The protocol builds upon existing agent communication mechanisms and authorization frameworks, particularly the Agent Authorization Profile [draft-aap-oauth-profile], to enable secure and auditable workflow execution. The execution model supports both centralized coordination through a designated DAG Coordinator and distributed execution patterns where agents negotiate task assignments dynamically.\n\nThe execution protocol operates through a series of well-defined phases: initialization, task scheduling, parallel execution, and completion verification. During initialization, the DAG Coordinator validates the workflow structure, resolves task bindings to available agents, and establishes the execution context. Task scheduling follows topological ordering of the DAG, with the coordinator identifying executable tasks (those with satisfied dependencies) and dispatching them to appropriate agents. The protocol supports parallel execution of independent tasks while maintaining strict dependency ordering through state synchronization mechanisms.\n\nAgent coordination during DAG execution relies on structured message exchanges that convey task assignments, status updates, and result propagation. Task assignment messages MUST include the complete task specification, execution context parameters, and any required authorization tokens following the Agent Authorization Profile format [draft-aap-oauth-profile]. Agents respond with acceptance confirmations that include estimated execution time and resource requirements. Status update messages provide real-time execution progress and MUST be sent at configurable intervals to enable failure detection and recovery operations.\n\nState synchronization across the multi-agent system is achieved through a combination of checkpoint mechanisms and distributed context sharing. The DAG Coordinator maintains the authoritative execution state, including task completion status, intermediate results, and dependency satisfaction tracking. Agent Context Distribution mechanisms [draft-chang-agent-context-interaction] are employed to efficiently share relevant context information among participating agents, reducing redundant data transfer while ensuring each agent has access to necessary execution context. Intermediate results from completed tasks are propagated to dependent tasks through structured result messages that preserve data lineage and enable audit trail construction.\n\nThe protocol defines specific message formats for each phase of execution, using JSON [RFC8259] structures that can be embedded within existing agent communication protocols. Task execution requests include fields for task identification, input parameters, execution constraints, and callback endpoints for status reporting. Result messages contain structured output data, execution metadata, and quality indicators that enable downstream tasks to validate input requirements. Error and exception messages provide detailed failure information including error codes, diagnostic data, and suggested recovery actions.\n\nParallel execution coordination addresses the challenges of resource contention and optimal scheduling across heterogeneous agent capabilities. The protocol supports both push-based task assignment, where the coordinator actively distributes work, and pull-based execution, where agents request tasks based on their availability and capabilities. Load balancing mechanisms consider agent capacity, current workload, and task affinity when making scheduling decisions. The protocol also defines procedures for dynamic rescheduling when agents become unavailable or when execution time estimates prove inaccurate, ensuring workflow completion despite individual agent failures.", "The Agent Task DAG framework MUST provide robust checkpoint and recovery mechanisms to ensure workflow resilience and enable graceful handling of failures, interruptions, and human intervention points. Checkpoints represent persistent snapshots of the DAG execution state at specific points in the workflow, capturing sufficient information to resume execution from that point or rollback to a previous stable state. The framework defines three types of checkpoints: automatic checkpoints created at predefined intervals or task completion boundaries, explicit checkpoints requested by agents or human operators, and recovery checkpoints generated immediately before high-risk operations that may require rollback.\n\nCheckpoint creation MUST capture the complete execution context as defined in Section 4, including the current state of all task nodes, intermediate results, agent assignments, and security context derived from Agent Authorization Profiles [draft-aap-oauth-profile]. Each checkpoint MUST include a unique identifier, timestamp, DAG version, execution state hash, and references to any external resources or agent context information as specified in [draft-chang-agent-context-interaction]. The checkpoint data structure SHOULD be serialized using JSON [RFC8259] with optional compression for large state objects, and MUST be digitally signed to ensure integrity and authenticity. Checkpoints MAY be stored in distributed storage systems to ensure availability across multiple DAG Coordinators.\n\nThe rollback procedure enables the DAG execution to revert to a previous checkpoint when failures occur or human intervention requires undoing completed work. When a rollback is initiated, the DAG Coordinator MUST notify all participating agents of the rollback operation, invalidate any results produced after the target checkpoint, and restore the execution context to the checkpoint state. Agents MUST acknowledge the rollback operation and may need to perform agent-specific cleanup operations such as releasing resources or notifying external systems. The rollback operation MUST preserve audit trails by maintaining records of both the original execution and the rollback event, ensuring compliance with security and regulatory requirements.\n\nFailure recovery strategies operate at multiple levels within the DAG execution framework, from individual task failures to complete coordinator failures. For task-level failures, the framework supports automatic retry with exponential backoff, task reassignment to alternative agents with compatible capabilities, and conditional continuation where dependent tasks may proceed with degraded inputs. When coordinator failures occur, recovery mechanisms leverage distributed checkpoints and coordinator election protocols to restore execution state on alternative infrastructure. The framework MUST support human-in-the-loop recovery scenarios where automated recovery is insufficient, providing interfaces for human operators to inspect checkpoint states, approve recovery actions, and inject corrective context information.\n\nThe checkpoint and recovery mechanisms MUST integrate with the agent authorization framework to ensure that recovery operations maintain appropriate security boundaries and access controls. Recovery operations SHOULD verify that participating agents still possess valid authorization profiles and may require re-authentication if significant time has elapsed since checkpoint creation. The framework MUST provide configurable retention policies for checkpoints, balancing storage efficiency with recovery requirements, and MUST support secure deletion of checkpoint data containing sensitive information when retention periods expire or workflows complete successfully.", "This section describes how the Agent Task DAG framework integrates with existing agent authorization, discovery, and communication protocols to provide a comprehensive multi-agent workflow execution environment. The framework is designed to be protocol-agnostic while providing specific bindings for commonly used agent protocols, enabling organizations to adopt DAG-based workflows within their existing agent infrastructure.\n\nThe DAG framework builds upon the Agent Authorization Profile (AAP) [draft-aap-oauth-profile] to establish secure task execution contexts. When a DAG Coordinator initiates workflow execution, it MUST obtain appropriate authorization tokens for each participating agent using the structured claims defined in AAP. The task context claim within the agent's JWT token includes the DAG identifier, task node assignments, and operational constraints specific to the workflow. This approach ensures that agents can verify their authorization to execute specific tasks within the broader workflow context while maintaining the delegation chains and human oversight requirements established in their authorization profiles.\n\nAgent discovery and capability matching for DAG execution leverages existing agent discovery protocols while extending them with DAG-specific metadata. Agents participating in DAG workflows SHOULD advertise their capabilities using structured capability descriptors that include supported task types, execution constraints, and checkpoint compatibility. The DAG Coordinator uses this information during the task binding process to assign task nodes to appropriate agents. When multiple agents are capable of executing a particular task type, the coordinator MAY use load balancing, geographic distribution, or other selection criteria to optimize workflow execution.\n\nContext distribution among agents executing DAG workflows follows the mechanisms defined in [draft-chang-agent-context-interaction], with specific extensions for DAG execution state management. The execution context for a DAG workflow includes the complete graph structure, current execution state, intermediate task results, and checkpoint metadata. Agents MUST receive sufficient context to execute their assigned tasks while minimizing the distribution of sensitive information to unauthorized agents. The framework supports both push-based context distribution, where the DAG Coordinator sends relevant context to agents before task execution, and pull-based approaches where agents request specific context elements as needed.\n\nThe framework provides protocol bindings for common agent communication patterns including HTTP-based REST APIs [RFC9110], message queuing systems, and real-time communication protocols. Each binding specifies how DAG execution messages are encoded, how task results are reported, and how checkpoint operations are coordinated across the distributed agent environment. Protocol-specific considerations such as connection management, retry mechanisms, and error handling are addressed within each binding specification. For HTTP-based bindings, the framework defines standardized endpoints for task execution, status reporting, and checkpoint operations that can be implemented by any agent supporting the DAG execution protocol.\n\nIntegration with existing agent task protocols [draft-cui-ai-agent-task] is achieved through task node adapters that translate between DAG task specifications and protocol-specific task representations. These adapters handle differences in task parameterization, result formatting, and execution semantics while preserving the dependency relationships and execution guarantees required by the DAG framework. The framework also supports integration with audit and compliance systems through standardized logging interfaces that capture task execution events, authorization decisions, and checkpoint operations in formats compatible with existing security and compliance tools.", "The Agent Task DAG framework introduces unique security challenges that extend beyond traditional single-agent systems. Multi-agent workflows create expanded attack surfaces through inter-agent communication channels, shared execution contexts, and distributed state management. Malicious actors may attempt to inject unauthorized tasks into DAG structures, manipulate task dependencies to create privilege escalation paths, or exploit checkpoint mechanisms to gain persistent access to workflow state. The distributed nature of DAG execution also amplifies risks related to agent impersonation, context poisoning, and unauthorized workflow modification during execution.\n\nTask authorization within DAG workflows MUST leverage the Agent Authorization Profile [draft-aap-oauth-profile] to establish fine-grained permissions for each task node. Each task node SHOULD include authorization requirements that specify which agent capabilities, delegation chains, and operational constraints are required for execution. The DAG Coordinator MUST verify that assigned agents possess valid JWT tokens with appropriate structured claims before initiating task execution. When tasks involve sensitive operations or access to protected resources, implementations SHOULD require fresh token validation rather than relying on cached authorization state. Multi-step workflows that span extended time periods MUST implement token refresh mechanisms to maintain security throughout DAG execution.\n\nContext isolation represents a critical security boundary in multi-agent DAG systems. Execution contexts MUST be isolated between different DAG instances to prevent information leakage and unauthorized access to intermediate results. Implementations SHOULD use cryptographic techniques to protect context data in transit and at rest, particularly when context distribution mechanisms [draft-chang-agent-context-interaction] are employed across network boundaries. Task nodes that handle sensitive data MUST implement appropriate data classification and handling controls, ensuring that context information is only accessible to authorized agents within the workflow. The framework SHOULD support configurable context sharing policies that allow administrators to define which context elements can be shared between tasks and which must remain isolated.\n\nAudit trail requirements for DAG execution are more complex than single-agent scenarios due to the distributed and potentially parallel nature of task execution. Implementations MUST maintain comprehensive logs that capture DAG initiation, task assignments, agent authorizations, execution outcomes, and any human intervention points. Audit records SHOULD include cryptographic signatures or integrity mechanisms to prevent tampering and support forensic analysis. The checkpoint and recovery mechanisms introduce additional logging requirements, as rollback operations and failure recovery attempts MUST be fully auditable. Organizations operating in regulated environments MAY require enhanced audit capabilities that provide real-time monitoring of DAG execution state and automated alerts for security policy violations.\n\nThe integration of human oversight points within DAG workflows creates additional security considerations around authentication, authorization, and workflow integrity. Human operators MUST be properly authenticated before approving task continuations or modifying workflow parameters. The framework SHOULD support multi-factor authentication and role-based access controls for human intervention points. Implementations MUST ensure that human approval requirements cannot be bypassed through agent coordination or DAG manipulation. When human operators modify workflow parameters or approve exceptional conditions, these actions MUST be cryptographically signed and integrated into the workflow's audit trail to maintain end-to-end accountability.", "This document introduces several new protocol elements and identifiers that require IANA registration to ensure global uniqueness and interoperability across implementations. The Agent Task DAG framework extends existing agent communication protocols with new message types, node classifications, and execution state identifiers that must be standardized for consistent implementation.\n\nThe specification requires the establishment of a new \"Agent Task DAG Parameters\" registry to manage the various identifiers used within the framework. This registry MUST include sub-registries for DAG node types, edge relationship types, execution states, checkpoint types, and recovery action identifiers. Each sub-registry MUST follow the \"Specification Required\" registration policy as defined in [RFC8126], with designated experts reviewing submissions for technical correctness and consistency with the overall framework architecture. The registry MUST also accommodate extensions that integrate with existing agent authorization profiles as defined in [draft-aap-oauth-profile].\n\nA new \"application/vnd.ietf.agent-task-dag+json\" media type registration is REQUIRED for DAG workflow documents. This media type MUST reference this specification and follow the JSON format requirements specified in [RFC8259]. The media type enables proper content negotiation when agents exchange DAG definitions and execution state information. Additionally, new URI schemes \"agent-dag:\" and \"agent-task:\" are proposed for identifying DAG instances and individual task nodes respectively, requiring registration in the \"Uniform Resource Identifier (URI) Schemes\" registry maintained by IANA.\n\nThe framework introduces new JWT claim names for representing DAG execution context and task bindings within agent authorization tokens, extending the structured claims mechanism defined in [draft-aap-oauth-profile]. These claim names MUST be registered in the \"JSON Web Token Claims\" registry established by [RFC7519]. The new claims include \"dag_id\", \"task_node\", \"execution_context\", \"checkpoint_ref\", and \"recovery_state\", each with specific semantic meanings within the DAG execution protocol. Registration of these claims ensures consistent interpretation across different agent implementations and authorization servers.\n\nFinally, new HTTP header fields \"DAG-Execution-ID\" and \"DAG-Checkpoint\" are introduced for coordination between agents during DAG execution. These headers MUST be registered in the \"Hypertext Transfer Protocol (HTTP) Field Name Registry\" as defined in [RFC9110]. The headers enable stateless coordination mechanisms and support the checkpoint and recovery procedures specified in this framework, while maintaining compatibility with existing HTTP-based agent communication protocols."]

full_text:
  Internet-Draft                                                     anima
  Intended status: standards-track                              March 2026
  Expires: September 05, 2026


           Agent Task DAG: A Framework for Directed Acyclic Graph Execution in Multi-Agent Systems
           draft-agent-ecosystem-agent-task-a-00

  Abstract

     As AI agent systems become increasingly complex, there is a
     growing need for structured approaches to orchestrate multi-step
     tasks across multiple autonomous agents. This document defines the
     Agent Task DAG (Directed Acyclic Graph) framework, which provides
     a standardized approach for representing, executing, and managing
     complex workflows in multi-agent environments. The framework
     addresses key challenges including task decomposition, dependency
     management, parallel execution, failure recovery, and human
     oversight integration. By building upon existing agent
     authorization profiles and task negotiation protocols, this
     specification enables agents to coordinate complex workflows while
     maintaining security, auditability, and the ability to incorporate
     human-in-the-loop decision points. The framework supports both
     fast execution in trusted environments and rigorous verification
     in regulated contexts through configurable assurance profiles.

  Status of This Memo

     This Internet-Draft is submitted in full conformance with the
     provisions of BCP 78 and BCP 79.

     This document is intended to have standards-track status.
     Distribution of this memo is unlimited.

  Terminology

     The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
     NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
     "MAY", and "OPTIONAL" in this document are to be interpreted as
     described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
     appear in all capitals, as shown here.

     Agent Task DAG
        A directed acyclic graph representing a complex workflow where
        nodes represent individual tasks and edges represent
        dependencies between tasks

     Task Node
        An individual unit of work within a DAG that can be executed by
        one or more agents

     Execution Context
        The runtime environment and state information associated with
        DAG execution, including agent assignments, intermediate
        results, and checkpoint data

     Checkpoint
        A persistent snapshot of DAG execution state that enables
        rollback and recovery operations

     Task Binding
        The association of a task node with specific agent capabilities
        or agent instances

     DAG Coordinator
        An agent or system component responsible for orchestrating the
        execution of a complete DAG workflow


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement  ...........................................  5
     4.  Agent Task DAG Framework  ....................................  6
     5.  Task Execution Protocol  .....................................  7
     6.  Checkpoint and Recovery Mechanisms  ..........................  8
     7.  Integration with Existing Agent Protocols  ...................  9
     8.  Security Considerations  .....................................  10
     9.  IANA Considerations  .........................................  11
     10.  References  ..................................................  12

  1.  Introduction

     The increasing sophistication of AI agent systems has created a
     demand for structured approaches to orchestrate complex, multi-
     step tasks across autonomous agents. While individual agents have
     become capable of handling sophisticated reasoning and execution
     tasks, real-world applications often require coordinating multiple
     agents to complete workflows that involve parallel processing,
     sequential dependencies, and dynamic task allocation. Current
     approaches to multi-agent coordination typically rely on ad-hoc
     communication patterns or simple request-response chains, which
     lack the expressiveness and reliability needed for complex
     enterprise and research applications.

     This document defines the Agent Task DAG (Directed Acyclic Graph)
     framework, which provides a standardized approach for
     representing, executing, and managing complex workflows in multi-
     agent environments. The framework builds upon existing agent
     protocols, particularly the Agent Authorization Profile [draft-
     aap-oauth-profile] for security and authorization, and agent task
     coordination mechanisms [draft-cui-ai-agent-task] for basic task
     execution. By representing workflows as directed acyclic graphs,
     the framework enables explicit modeling of task dependencies,
     parallel execution opportunities, and conditional branching while
     maintaining guarantees about workflow termination and consistency.

     The Agent Task DAG framework addresses several critical challenges
     in multi-agent systems: task decomposition and dependency
     management, efficient parallel execution across heterogeneous
     agents, robust failure recovery and rollback mechanisms, and
     integration of human oversight at critical decision points. The
     framework leverages structured claims for agent context [draft-
     aap-oauth-profile] to enable context-aware task assignment and
     supports agent context distribution mechanisms [draft-chang-agent-
     context-interaction] to maintain coherent state across complex
     multi-round workflows. This approach ensures that agents can
     coordinate effectively while maintaining security boundaries and
     audit trails required in enterprise environments.

     The specification is designed to be protocol-agnostic and can
     operate over various transport mechanisms including HTTP
     [RFC9110], message queuing systems, and specialized agent
     communication protocols. The framework integrates with existing
     OAuth 2.0 [RFC6749] and JWT [RFC7519] infrastructure through the
     Agent Authorization Profile, enabling seamless deployment in
     environments that already support agent authentication and
     authorization. The DAG representation follows JSON [RFC8259]
     encoding standards to ensure broad compatibility and easy
     integration with existing agent development frameworks.

     This document focuses specifically on the DAG execution framework
     and does not address broader questions of agent discovery,
     capability matching, or task marketplace mechanisms, which are
     covered by complementary specifications. The framework assumes the
     existence of agent authorization infrastructure and builds upon
     established patterns for agent-to-agent communication while
     providing the additional structure needed for complex workflow
     coordination.

  2.  Terminology

     The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL
     NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED",
     "MAY", and "OPTIONAL" in this document are to be interpreted as
     described in BCP 14 [RFC2119] [RFC8174] when, and only when, they
     appear in all capitals, as shown here.

     This specification builds upon terminology established in the
     Agent Authorization Profile [draft-aap-oauth-profile], AI Agent
     Task specifications [draft-cui-ai-agent-task], and Agent Context
     Interaction mechanisms [draft-chang-agent-context-interaction].
     The following terms are defined for use throughout this document:

     Agent Task DAG: A directed acyclic graph data structure
     representing a complex multi-step workflow where nodes correspond
     to individual tasks and directed edges represent dependency
     relationships between tasks. The DAG enforces execution ordering
     constraints while enabling parallel execution of independent task
     branches. Each DAG maintains metadata including creation time,
     ownership, and execution policies that govern how the workflow may
     be executed across multiple agents.

     Task Node: An individual unit of work within an Agent Task DAG
     that encapsulates a specific operation to be performed by one or
     more AI agents. Each task node contains task specifications,
     input/output schemas, execution constraints, and binding
     requirements that determine which agents are capable of executing
     the task. Task nodes maintain state information including
     execution status, assigned agents, and result data as defined in
     [draft-cui-ai-agent-task].

     Execution Context: The runtime environment and associated state
     information that governs the execution of an Agent Task DAG. The
     execution context includes agent assignments, intermediate task
     results, security credentials, operational constraints from Agent
     Authorization Profiles [draft-aap-oauth-profile], and distributed
     context information as specified in [draft-chang-agent-context-
     interaction]. The execution context ensures consistency and
     provides necessary information for task coordination across
     multiple agents.

     Checkpoint: A persistent, immutable snapshot of Agent Task DAG
     execution state captured at a specific point in time. Checkpoints
     contain the complete execution context, task completion status,
     intermediate results, and sufficient metadata to enable rollback
     and recovery operations. Checkpoints serve as recovery points for
     failure scenarios and decision points for human-in-the-loop
     interventions.

     Task Binding: The process and resulting association between a task
     node and specific agent capabilities or agent instances that will
     execute the task. Task binding considers agent authorization
     profiles, capability matching, resource availability, and security
     constraints. The binding process may be performed statically
     during DAG planning or dynamically during execution based on
     runtime conditions.

     DAG Coordinator: An agent or system component responsible for
     orchestrating the complete lifecycle of Agent Task DAG execution.
     The DAG Coordinator manages task scheduling, monitors execution
     progress, handles inter-agent communication, enforces security
     policies, and coordinates checkpoint and recovery operations. The
     coordinator maintains the authoritative view of DAG execution
     state and serves as the primary interface for human oversight and
     intervention.

  3.  Problem Statement

     Current approaches to multi-agent task coordination suffer from
     several fundamental limitations that impede the development of
     robust, scalable autonomous systems. Existing coordination
     mechanisms typically rely on ad-hoc communication patterns, simple
     request-response protocols, or basic workflow engines that were
     not designed for the dynamic, autonomous nature of AI agents.
     While protocols like those defined in [draft-cui-ai-agent-task]
     provide foundations for individual task execution, they lack
     standardized approaches for managing complex workflows involving
     multiple interdependent tasks across heterogeneous agent
     populations. The Agent Authorization Profile [draft-aap-oauth-
     profile] establishes important primitives for agent identity and
     authorization, but does not address the orchestration challenges
     that arise when multiple authorized agents must coordinate to
     complete complex, multi-step objectives.

     The complexity of real-world AI agent applications demands
     structured approaches to task decomposition and dependency
     management that current protocols do not adequately address.
     Agents operating in domains such as scientific research, business
     process automation, or infrastructure management often require
     workflows where tasks have intricate dependencies, may execute in
     parallel when possible, and must handle partial failures
     gracefully. Without standardized mechanisms for representing these
     relationships, agent systems resort to brittle, custom
     coordination logic that is difficult to audit, debug, or modify.
     The lack of formal workflow representation also prevents effective
     human oversight integration, as stakeholders cannot easily
     understand or intervene in complex multi-agent processes.

     Agent Context Distribution mechanisms [draft-chang-agent-context-
     interaction] have demonstrated that context sharing among agents
     significantly impacts execution success rates, but current
     approaches do not provide systematic ways to manage context
     propagation through complex workflows. In multi-step processes,
     intermediate results from one task often serve as inputs to
     downstream tasks, creating context dependencies that must be
     carefully managed to ensure workflow integrity. Existing protocols
     lack standardized approaches for maintaining execution context
     across task boundaries, leading to information loss, redundant
     computation, and coordination failures that compromise overall
     system reliability.

     Fault tolerance and recovery represent critical gaps in current
     multi-agent coordination approaches. Real-world agent systems must
     handle various failure modes including agent unavailability, task
     timeouts, resource constraints, and partial execution failures.
     Without systematic checkpoint and recovery mechanisms, workflows
     often must restart completely when any component fails, leading to
     inefficient resource utilization and poor user experience. The
     absence of standardized rollback capabilities also complicates
     human intervention scenarios, where domain experts may need to
     modify workflow parameters or task assignments based on
     intermediate results or changing requirements.

     Scalability challenges emerge when current coordination approaches
     encounter workflows with dozens or hundreds of interdependent
     tasks distributed across multiple agent instances. Simple
     centralized coordination quickly becomes a bottleneck, while fully
     decentralized approaches struggle with consistency and deadlock
     prevention. The lack of standardized protocols for parallel task
     execution, resource allocation, and progress monitoring prevents
     agent systems from efficiently utilizing available computational
     resources. Additionally, without formal workflow representation,
     it becomes difficult to optimize task scheduling, predict resource
     requirements, or provide meaningful progress indicators to human
     stakeholders.

     These limitations necessitate a framework that provides:
     structured representation of complex workflows with explicit
     dependency management; standardized protocols for parallel
     execution and agent coordination; systematic checkpoint and
     recovery mechanisms that enable fault tolerance and human
     intervention; integration with existing agent authorization and
     context distribution mechanisms; and scalable execution patterns
     that can accommodate workflows ranging from simple sequential
     processes to complex parallel computations involving multiple
     agent populations.

  4.  Agent Task DAG Framework

     This section defines the core data model and execution semantics
     for the Agent Task DAG framework. The framework provides a
     structured approach for representing complex multi-agent workflows
     as directed acyclic graphs, where individual tasks are modeled as
     nodes and dependencies between tasks are represented as edges. The
     data model builds upon existing agent protocol foundations while
     introducing specific constructs needed for distributed workflow
     orchestration.

     4.1. DAG Data Model

     An Agent Task DAG MUST be represented as a JSON object [RFC8259]
     that contains the complete specification of a workflow. The DAG
     structure consists of three primary components: metadata
     describing the overall workflow, a collection of task nodes
     representing individual units of work, and dependency
     relationships that define execution ordering constraints. Each DAG
     MUST include a unique identifier, version information, and
     execution parameters that govern how the workflow should be
     processed.

     Task nodes within the DAG represent atomic units of work that can
     be executed by autonomous agents. Each task node MUST specify its
     execution requirements, including required agent capabilities,
     input and output data schemas, and execution constraints such as
     timeouts or resource limits. Task nodes SHOULD reference
     standardized task types as defined in [draft-cui-ai-agent-task]
     where applicable, enabling interoperability across different agent
     implementations. The task specification MUST include sufficient
     information for agents to determine their capability to execute
     the task and negotiate execution parameters.

     Dependency relationships between task nodes are expressed through
     edge definitions that establish partial ordering constraints over
     the DAG. Each edge MUST specify source and target task nodes, with
     the semantic meaning that the target task cannot begin execution
     until the source task has completed successfully. Edges MAY
     include conditional execution logic, allowing for branching
     workflows based on the results of predecessor tasks. The framework
     supports both data dependencies, where output from one task serves
     as input to another, and control dependencies, where task ordering
     is required for correctness without direct data flow.

     4.2. Execution Context Management

     The Execution Context provides the runtime environment for DAG
     processing and maintains state information throughout workflow
     execution. The execution context MUST track the current state of
     each task node, intermediate results produced during execution,
     and metadata about agent assignments for each task. Context
     information SHOULD be distributed among participating agents using
     the mechanisms defined in [draft-chang-agent-context-interaction]
     to ensure consistent state visibility across the multi-agent
     system.

     Agent binding within the execution context associates task nodes
     with specific agent instances or agent capability requirements.
     The framework supports both static binding, where task assignments
     are predetermined before execution begins, and dynamic binding,
     where task assignments are resolved at runtime based on agent
     availability and capability matching. When integrated with Agent
     Authorization Profiles [draft-aap-oauth-profile], the execution
     context MUST validate that assigned agents possess the necessary
     authorization claims to execute their bound tasks.

     Checkpoint creation within the execution context enables
     persistent state management and recovery capabilities. The
     framework MUST support checkpoint creation at configurable
     intervals, capturing the complete state of DAG execution including
     task completion status, intermediate results, and current agent
     assignments. Checkpoints SHOULD be created automatically before
     task nodes that are marked as requiring human oversight, enabling
     rollback to known-good states when human intervention modifies the
     workflow execution path.

     4.3. Task Execution Semantics

     Task execution within the DAG framework follows a coordination
     model where a DAG Coordinator orchestrates workflow progress while
     individual agents execute assigned tasks autonomously. The
     coordinator MUST maintain the global view of DAG state and
     determine when task dependencies have been satisfied, enabling
     parallel execution of independent task branches. Task scheduling
     MUST respect dependency constraints while maximizing parallel
     execution opportunities to optimize overall workflow completion
     time.

     The framework defines specific execution states for task nodes
     including pending, ready, executing, completed, failed, and
     skipped. State transitions MUST be coordinated through the DAG
     Coordinator to ensure consistency across the distributed system.
     When a task transitions to the ready state, the coordinator SHOULD
     initiate agent assignment and task negotiation protocols to begin
     execution. Failed tasks MAY trigger rollback procedures or
     alternate execution paths depending on the configured failure
     handling policies.

     Integration with existing agent protocols occurs through
     standardized interfaces that abstract the underlying communication
     mechanisms. The framework MUST support protocol-agnostic bindings
     that allow integration with different agent discovery,
     authorization, and communication protocols. Task execution
     requests SHOULD include structured claims as defined in [draft-
     aap-oauth-profile] when agent authorization is required, ensuring
     that security and audit requirements are maintained throughout the
     distributed workflow execution.

  5.  Task Execution Protocol

     The Agent Task DAG execution protocol defines a standardized
     approach for coordinating the execution of complex workflows
     across multiple autonomous agents. The protocol builds upon
     existing agent communication mechanisms and authorization
     frameworks, particularly the Agent Authorization Profile [draft-
     aap-oauth-profile], to enable secure and auditable workflow
     execution. The execution model supports both centralized
     coordination through a designated DAG Coordinator and distributed
     execution patterns where agents negotiate task assignments
     dynamically.

     The execution protocol operates through a series of well-defined
     phases: initialization, task scheduling, parallel execution, and
     completion verification. During initialization, the DAG
     Coordinator validates the workflow structure, resolves task
     bindings to available agents, and establishes the execution
     context. Task scheduling follows topological ordering of the DAG,
     with the coordinator identifying executable tasks (those with
     satisfied dependencies) and dispatching them to appropriate
     agents. The protocol supports parallel execution of independent
     tasks while maintaining strict dependency ordering through state
     synchronization mechanisms.

     Agent coordination during DAG execution relies on structured
     message exchanges that convey task assignments, status updates,
     and result propagation. Task assignment messages MUST include the
     complete task specification, execution context parameters, and any
     required authorization tokens following the Agent Authorization
     Profile format [draft-aap-oauth-profile]. Agents respond with
     acceptance confirmations that include estimated execution time and
     resource requirements. Status update messages provide real-time
     execution progress and MUST be sent at configurable intervals to
     enable failure detection and recovery operations.

     State synchronization across the multi-agent system is achieved
     through a combination of checkpoint mechanisms and distributed
     context sharing. The DAG Coordinator maintains the authoritative
     execution state, including task completion status, intermediate
     results, and dependency satisfaction tracking. Agent Context
     Distribution mechanisms [draft-chang-agent-context-interaction]
     are employed to efficiently share relevant context information
     among participating agents, reducing redundant data transfer while
     ensuring each agent has access to necessary execution context.
     Intermediate results from completed tasks are propagated to
     dependent tasks through structured result messages that preserve
     data lineage and enable audit trail construction.

     The protocol defines specific message formats for each phase of
     execution, using JSON [RFC8259] structures that can be embedded
     within existing agent communication protocols. Task execution
     requests include fields for task identification, input parameters,
     execution constraints, and callback endpoints for status
     reporting. Result messages contain structured output data,
     execution metadata, and quality indicators that enable downstream
     tasks to validate input requirements. Error and exception messages
     provide detailed failure information including error codes,
     diagnostic data, and suggested recovery actions.

     Parallel execution coordination addresses the challenges of
     resource contention and optimal scheduling across heterogeneous
     agent capabilities. The protocol supports both push-based task
     assignment, where the coordinator actively distributes work, and
     pull-based execution, where agents request tasks based on their
     availability and capabilities. Load balancing mechanisms consider
     agent capacity, current workload, and task affinity when making
     scheduling decisions. The protocol also defines procedures for
     dynamic rescheduling when agents become unavailable or when
     execution time estimates prove inaccurate, ensuring workflow
     completion despite individual agent failures.

  6.  Checkpoint and Recovery Mechanisms

     The Agent Task DAG framework MUST provide robust checkpoint and
     recovery mechanisms to ensure workflow resilience and enable
     graceful handling of failures, interruptions, and human
     intervention points. Checkpoints represent persistent snapshots of
     the DAG execution state at specific points in the workflow,
     capturing sufficient information to resume execution from that
     point or rollback to a previous stable state. The framework
     defines three types of checkpoints: automatic checkpoints created
     at predefined intervals or task completion boundaries, explicit
     checkpoints requested by agents or human operators, and recovery
     checkpoints generated immediately before high-risk operations that
     may require rollback.

     Checkpoint creation MUST capture the complete execution context as
     defined in Section 4, including the current state of all task
     nodes, intermediate results, agent assignments, and security
     context derived from Agent Authorization Profiles [draft-aap-
     oauth-profile]. Each checkpoint MUST include a unique identifier,
     timestamp, DAG version, execution state hash, and references to
     any external resources or agent context information as specified
     in [draft-chang-agent-context-interaction]. The checkpoint data
     structure SHOULD be serialized using JSON [RFC8259] with optional
     compression for large state objects, and MUST be digitally signed
     to ensure integrity and authenticity. Checkpoints MAY be stored in
     distributed storage systems to ensure availability across multiple
     DAG Coordinators.

     The rollback procedure enables the DAG execution to revert to a
     previous checkpoint when failures occur or human intervention
     requires undoing completed work. When a rollback is initiated, the
     DAG Coordinator MUST notify all participating agents of the
     rollback operation, invalidate any results produced after the
     target checkpoint, and restore the execution context to the
     checkpoint state. Agents MUST acknowledge the rollback operation
     and may need to perform agent-specific cleanup operations such as
     releasing resources or notifying external systems. The rollback
     operation MUST preserve audit trails by maintaining records of
     both the original execution and the rollback event, ensuring
     compliance with security and regulatory requirements.

     Failure recovery strategies operate at multiple levels within the
     DAG execution framework, from individual task failures to complete
     coordinator failures. For task-level failures, the framework
     supports automatic retry with exponential backoff, task
     reassignment to alternative agents with compatible capabilities,
     and conditional continuation where dependent tasks may proceed
     with degraded inputs. When coordinator failures occur, recovery
     mechanisms leverage distributed checkpoints and coordinator
     election protocols to restore execution state on alternative
     infrastructure. The framework MUST support human-in-the-loop
     recovery scenarios where automated recovery is insufficient,
     providing interfaces for human operators to inspect checkpoint
     states, approve recovery actions, and inject corrective context
     information.

     The checkpoint and recovery mechanisms MUST integrate with the
     agent authorization framework to ensure that recovery operations
     maintain appropriate security boundaries and access controls.
     Recovery operations SHOULD verify that participating agents still
     possess valid authorization profiles and may require re-
     authentication if significant time has elapsed since checkpoint
     creation. The framework MUST provide configurable retention
     policies for checkpoints, balancing storage efficiency with
     recovery requirements, and MUST support secure deletion of
     checkpoint data containing sensitive information when retention
     periods expire or workflows complete successfully.

  7.  Integration with Existing Agent Protocols

     This section describes how the Agent Task DAG framework integrates
     with existing agent authorization, discovery, and communication
     protocols to provide a comprehensive multi-agent workflow
     execution environment. The framework is designed to be protocol-
     agnostic while providing specific bindings for commonly used agent
     protocols, enabling organizations to adopt DAG-based workflows
     within their existing agent infrastructure.

     The DAG framework builds upon the Agent Authorization Profile
     (AAP) [draft-aap-oauth-profile] to establish secure task execution
     contexts. When a DAG Coordinator initiates workflow execution, it
     MUST obtain appropriate authorization tokens for each
     participating agent using the structured claims defined in AAP.
     The task context claim within the agent's JWT token includes the
     DAG identifier, task node assignments, and operational constraints
     specific to the workflow. This approach ensures that agents can
     verify their authorization to execute specific tasks within the
     broader workflow context while maintaining the delegation chains
     and human oversight requirements established in their
     authorization profiles.

     Agent discovery and capability matching for DAG execution
     leverages existing agent discovery protocols while extending them
     with DAG-specific metadata. Agents participating in DAG workflows
     SHOULD advertise their capabilities using structured capability
     descriptors that include supported task types, execution
     constraints, and checkpoint compatibility. The DAG Coordinator
     uses this information during the task binding process to assign
     task nodes to appropriate agents. When multiple agents are capable
     of executing a particular task type, the coordinator MAY use load
     balancing, geographic distribution, or other selection criteria to
     optimize workflow execution.

     Context distribution among agents executing DAG workflows follows
     the mechanisms defined in [draft-chang-agent-context-interaction],
     with specific extensions for DAG execution state management. The
     execution context for a DAG workflow includes the complete graph
     structure, current execution state, intermediate task results, and
     checkpoint metadata. Agents MUST receive sufficient context to
     execute their assigned tasks while minimizing the distribution of
     sensitive information to unauthorized agents. The framework
     supports both push-based context distribution, where the DAG
     Coordinator sends relevant context to agents before task
     execution, and pull-based approaches where agents request specific
     context elements as needed.

     The framework provides protocol bindings for common agent
     communication patterns including HTTP-based REST APIs [RFC9110],
     message queuing systems, and real-time communication protocols.
     Each binding specifies how DAG execution messages are encoded, how
     task results are reported, and how checkpoint operations are
     coordinated across the distributed agent environment. Protocol-
     specific considerations such as connection management, retry
     mechanisms, and error handling are addressed within each binding
     specification. For HTTP-based bindings, the framework defines
     standardized endpoints for task execution, status reporting, and
     checkpoint operations that can be implemented by any agent
     supporting the DAG execution protocol.

     Integration with existing agent task protocols [draft-cui-ai-
     agent-task] is achieved through task node adapters that translate
     between DAG task specifications and protocol-specific task
     representations. These adapters handle differences in task
     parameterization, result formatting, and execution semantics while
     preserving the dependency relationships and execution guarantees
     required by the DAG framework. The framework also supports
     integration with audit and compliance systems through standardized
     logging interfaces that capture task execution events,
     authorization decisions, and checkpoint operations in formats
     compatible with existing security and compliance tools.

  8.  Security Considerations

     The Agent Task DAG framework introduces unique security challenges
     that extend beyond traditional single-agent systems. Multi-agent
     workflows create expanded attack surfaces through inter-agent
     communication channels, shared execution contexts, and distributed
     state management. Malicious actors may attempt to inject
     unauthorized tasks into DAG structures, manipulate task
     dependencies to create privilege escalation paths, or exploit
     checkpoint mechanisms to gain persistent access to workflow state.
     The distributed nature of DAG execution also amplifies risks
     related to agent impersonation, context poisoning, and
     unauthorized workflow modification during execution.

     Task authorization within DAG workflows MUST leverage the Agent
     Authorization Profile [draft-aap-oauth-profile] to establish fine-
     grained permissions for each task node. Each task node SHOULD
     include authorization requirements that specify which agent
     capabilities, delegation chains, and operational constraints are
     required for execution. The DAG Coordinator MUST verify that
     assigned agents possess valid JWT tokens with appropriate
     structured claims before initiating task execution. When tasks
     involve sensitive operations or access to protected resources,
     implementations SHOULD require fresh token validation rather than
     relying on cached authorization state. Multi-step workflows that
     span extended time periods MUST implement token refresh mechanisms
     to maintain security throughout DAG execution.

     Context isolation represents a critical security boundary in
     multi-agent DAG systems. Execution contexts MUST be isolated
     between different DAG instances to prevent information leakage and
     unauthorized access to intermediate results. Implementations
     SHOULD use cryptographic techniques to protect context data in
     transit and at rest, particularly when context distribution
     mechanisms [draft-chang-agent-context-interaction] are employed
     across network boundaries. Task nodes that handle sensitive data
     MUST implement appropriate data classification and handling
     controls, ensuring that context information is only accessible to
     authorized agents within the workflow. The framework SHOULD
     support configurable context sharing policies that allow
     administrators to define which context elements can be shared
     between tasks and which must remain isolated.

     Audit trail requirements for DAG execution are more complex than
     single-agent scenarios due to the distributed and potentially
     parallel nature of task execution. Implementations MUST maintain
     comprehensive logs that capture DAG initiation, task assignments,
     agent authorizations, execution outcomes, and any human
     intervention points. Audit records SHOULD include cryptographic
     signatures or integrity mechanisms to prevent tampering and
     support forensic analysis. The checkpoint and recovery mechanisms
     introduce additional logging requirements, as rollback operations
     and failure recovery attempts MUST be fully auditable.
     Organizations operating in regulated environments MAY require
     enhanced audit capabilities that provide real-time monitoring of
     DAG execution state and automated alerts for security policy
     violations.

     The integration of human oversight points within DAG workflows
     creates additional security considerations around authentication,
     authorization, and workflow integrity. Human operators MUST be
     properly authenticated before approving task continuations or
     modifying workflow parameters. The framework SHOULD support multi-
     factor authentication and role-based access controls for human
     intervention points. Implementations MUST ensure that human
     approval requirements cannot be bypassed through agent
     coordination or DAG manipulation. When human operators modify
     workflow parameters or approve exceptional conditions, these
     actions MUST be cryptographically signed and integrated into the
     workflow's audit trail to maintain end-to-end accountability.

  9.  IANA Considerations

     This document introduces several new protocol elements and
     identifiers that require IANA registration to ensure global
     uniqueness and interoperability across implementations. The Agent
     Task DAG framework extends existing agent communication protocols
     with new message types, node classifications, and execution state
     identifiers that must be standardized for consistent
     implementation.

     The specification requires the establishment of a new "Agent Task
     DAG Parameters" registry to manage the various identifiers used
     within the framework. This registry MUST include sub-registries
     for DAG node types, edge relationship types, execution states,
     checkpoint types, and recovery action identifiers. Each sub-
     registry MUST follow the "Specification Required" registration
     policy as defined in [RFC8126], with designated experts reviewing
     submissions for technical correctness and consistency with the
     overall framework architecture. The registry MUST also accommodate
     extensions that integrate with existing agent authorization
     profiles as defined in [draft-aap-oauth-profile].

     A new "application/vnd.ietf.agent-task-dag+json" media type
     registration is REQUIRED for DAG workflow documents. This media
     type MUST reference this specification and follow the JSON format
     requirements specified in [RFC8259]. The media type enables proper
     content negotiation when agents exchange DAG definitions and
     execution state information. Additionally, new URI schemes "agent-
     dag:" and "agent-task:" are proposed for identifying DAG instances
     and individual task nodes respectively, requiring registration in
     the "Uniform Resource Identifier (URI) Schemes" registry
     maintained by IANA.

     The framework introduces new JWT claim names for representing DAG
     execution context and task bindings within agent authorization
     tokens, extending the structured claims mechanism defined in
     [draft-aap-oauth-profile]. These claim names MUST be registered in
     the "JSON Web Token Claims" registry established by [RFC7519]. The
     new claims include "dagid", "tasknode", "executioncontext",
     "checkpointref", and "recovery_state", each with specific semantic
     meanings within the DAG execution protocol. Registration of these
     claims ensures consistent interpretation across different agent
     implementations and authorization servers.

     Finally, new HTTP header fields "DAG-Execution-ID" and "DAG-
     Checkpoint" are introduced for coordination between agents during
     DAG execution. These headers MUST be registered in the "Hypertext
     Transfer Protocol (HTTP) Field Name Registry" as defined in
     [RFC9110]. The headers enable stateless coordination mechanisms
     and support the checkpoint and recovery procedures specified in
     this framework, while maintaining compatibility with existing
     HTTP-based agent communication protocols.

  10.  References

  10.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8259]
           RFC 8259

     [RFC 7519]
           RFC 7519

     [draft-aap-oauth-profile]
           draft-aap-oauth-profile

     [draft-cui-ai-agent-task]
           draft-cui-ai-agent-task

     [draft-guy-bary-stamp-protocol]
           draft-guy-bary-stamp-protocol

  10.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 9110]
           RFC 9110

     [draft-chang-agent-context-interaction]
           draft-chang-agent-context-interaction

     [draft-liu-dmsc-acps-arc]
           draft-liu-dmsc-acps-arc

     [draft-rosenberg-aiproto-framework]
           draft-rosenberg-aiproto-framework

     [draft-song-oauth-ai-agent-collaborate-authz]
           draft-song-oauth-ai-agent-collaborate-authz

     [draft-mao-rtgwg-apn-framework-for-ioa]
           draft-mao-rtgwg-apn-framework-for-ioa

     [draft-nandakumar-ai-agent-moq-transport]
           draft-nandakumar-ai-agent-moq-transport


  Author's Address

     Generated by IETF Draft Analyzer
     Family: agent-ecosystem
     2026-03-04

rating_json:
  {"s": "Proposes a DAG-based framework for orchestrating complex multi-agent workflows with standardized task decomposition, dependency management, and human oversight. Builds on existing agent authorization and negotiation protocols to enable secure, auditable coordination across autonomous agents.", "n": 3, "nn": "DAG execution frameworks exist in workflow systems; applying to multi-agent coordination is moderately novel but incremental rather than transformative.", "m": 2, "mn": "Early draft (00) with abstract-level details; lacks concrete protocol specifications, formal semantics, or implementation guidance needed for IETF standards maturity.", "o": 4, "on": "Heavy overlap with existing workflow orchestration (Argo, Temporal), task scheduling literature, and emerging multi-agent coordination work; limited differentiation from generic DAG frameworks.", "mo": 2, "mon": "First draft in March 2026 with no prior versions; unclear WG sponsorship or community support; positioned as foundation but lacks demonstrated adoption pressure.", "r": 3, "rn": "Timely given multi-agent AI adoption, but IETF standardization of application-layer agent orchestration is questionable fit compared to transport/protocol concerns.", "c": ["A2A protocols", "Agent identity/auth", "Human-agent interaction", "Policy/governance", "Other AI/agent"]}


