# llm_cache

draft_name: draft-yang-srv6ops-intelligent-routing
prompt_hash: a86e9d693f00f107
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-yang-srv6ops-intelligent-routing]
created_at: 2026-03-03T19:53:55.232107+00:00
response_json:
  [{"title": "Network Quality-based SRv6 Path Selection", "description": "Selects among multiple SRv6 Policy candidate paths based on real-time network quality metrics.", "type": "mechanism"}, {"title": "Intelligent SRv6 Routing in IPv6", "description": "Implements quality-aware source routing decisions for SRv6 policies in IPv6 environments.", "type": "extension"}, {"title": "Dynamic SRv6 Candidate Path Evaluation", "description": "Continuously evaluates dynamic SRv6 policy candidate paths against network quality indicators.", "type": "mechanism"}, {"title": "SRv6 Explicit Path Quality Optimization", "description": "Optimizes explicit SRv6 paths by incorporating network quality metrics into path selection.", "type": "extension"}]


draft_name: draft-yao-tsvwg-cco-requirement-and-analysis
prompt_hash: 9be720de71ef6e1a
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-yao-tsvwg-cco-requirement-and-analysis]
created_at: 2026-03-03T19:53:55.239098+00:00
response_json:
  [{"title": "Network-Assisted Collective Communication", "description": "Proposes in-network optimization of collective communication operations for AI training clusters.", "type": "architecture"}, {"title": "RDMA-Compatible Collective Acceleration", "description": "Designs network-assisted collective optimization mechanisms compatible with RDMA transport.", "type": "mechanism"}, {"title": "One-to-Many Collective Optimization", "description": "Reduces bandwidth overhead for broadcast/scatter operations in parallel AI clusters.", "type": "mechanism"}, {"title": "Many-to-One Collective Optimization", "description": "Reduces bandwidth overhead for gather operations in parallel AI clusters.", "type": "mechanism"}, {"title": "Many-to-Many Collective Optimization", "description": "Reduces bandwidth overhead for all-reduce and all-gather operations in parallel AI clusters.", "type": "mechanism"}, {"title": "Collective Communication Gap Analysis", "description": "Identifies requirements gap between network-assisted collective acceleration schemes and RDMA semantics.", "type": "requirement"}, {"title": "AI Cluster Transport Efficiency", "description": "Analyzes optimization opportunities for generative AI workload communication patterns.", "type": "pattern"}]


draft_name: draft-yc-ipv6-for-ioa
prompt_hash: 7a901419b8f06f2d
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-yc-ipv6-for-ioa]
created_at: 2026-03-03T19:54:10.070924+00:00
response_json:
  [{"title": "IPv6 Address Space for Agent Identity", "description": "Leverage IPv6's vast address space to assign unique identities to hundreds of billions of intelligent agents.", "type": "architecture"}, {"title": "Native End-to-End Agent Connectivity", "description": "Enable direct peer-to-peer communication between agents without intermediary translation layers using IPv6's native capabilities.", "type": "architecture"}, {"title": "Agent Discovery via IPv6 Built-in Features", "description": "Utilize IPv6 mechanisms like neighbor discovery and multicast to enable automatic agent discovery and registration.", "type": "mechanism"}, {"title": "Agent Security via IPv6 IPsec Integration", "description": "Apply IPv6's native IPsec support for securing agent-to-agent communications at the network layer.", "type": "extension"}, {"title": "Quality of Service for Agent Traffic", "description": "Define IPv6 traffic class and flow label usage patterns specific to agent workloads and priorities.", "type": "requirement"}, {"title": "IPv6 Extension Headers for Agent Metadata", "description": "Define optional IPv6 extension headers to carry agent-specific metadata and capabilities information.", "type": "extension"}, {"title": "Agent Mobility with IPv6 MIPv6", "description": "Adapt Mobile IPv6 mechanisms to support agent migration across network domains.", "type": "mechanism"}]


draft_name: draft-zeng-mcp-network-measurement
prompt_hash: 88a0c7fd88f52438
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zeng-mcp-network-measurement]
created_at: 2026-03-03T19:54:10.089001+00:00
response_json:
  [{"title": "MCP Server Model for Network Devices", "description": "Define how network devices expose measurement capabilities as MCP servers with standardized tools and resources.", "type": "architecture"}, {"title": "Natural Language Network Measurement Commands", "description": "Enable AI clients to translate natural language queries into network measurement operations via MCP prompts.", "type": "pattern"}, {"title": "Real-time Performance Monitoring Protocol", "description": "Specify MCP tools for continuous network metrics collection and streaming from device servers.", "type": "protocol"}, {"title": "Intelligent Fault Diagnosis via MCP", "description": "Define MCP resources and tools to enable AI-driven root cause analysis of network faults.", "type": "mechanism"}, {"title": "Automated Topology Discovery Workflow", "description": "Implement MCP-based orchestration for discovering and mapping network topology without manual intervention.", "type": "pattern"}, {"title": "Measurement Data Schema Standardization", "description": "Define standard JSON schemas for network measurement data exchanged via MCP.", "type": "requirement"}, {"title": "MCP Security for Measurement Access Control", "description": "Specify authentication and authorization mechanisms for MCP clients accessing network measurement capabilities.", "type": "extension"}]


draft_name: draft-zeng-mcp-network-mgmt
prompt_hash: 8ae683ff07a5713e
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zeng-mcp-network-mgmt]
created_at: 2026-03-03T19:54:10.097125+00:00
response_json:
  [{"title": "Network Equipment as MCP Servers", "description": "Define capability tokens and error codes for routers and switches to expose management functions as MCP servers.", "type": "architecture"}, {"title": "MCP Tools for Device Configuration", "description": "Specify standardized MCP tool definitions for common network device configuration operations.", "type": "protocol"}, {"title": "MCP Resources for Device State", "description": "Define MCP resource schemas to represent network device state, inventory, and status information.", "type": "protocol"}, {"title": "MCP Prompts for Network Management Workflows", "description": "Specify prompt templates to guide AI agents through complex network management tasks.", "type": "pattern"}, {"title": "Backward Compatibility with Existing MCP", "description": "Ensure network management extensions do not break existing MCP implementations through versioning.", "type": "requirement"}, {"title": "Device-Specific Error Code Extensions", "description": "Define additional error codes to represent network equipment-specific failure conditions.", "type": "extension"}, {"title": "Multi-device Management Orchestration", "description": "Provide patterns for MCP clients to coordinate operations across multiple network devices.", "type": "pattern"}]


draft_name: draft-zhang-rtgwg-multicast-requirements-gaps-aidc
prompt_hash: d2689b325eb4a0ff
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zhang-rtgwg-multicast-requirements-gaps-aidc]
created_at: 2026-03-03T19:54:10.105070+00:00
response_json:
  [{"title": "Point-to-Multipoint Data Distribution for Model Training", "description": "Define multicast requirements for efficiently distributing training data to multiple compute nodes in large language model training.", "type": "requirement"}, {"title": "Low-Latency Multicast for Inference Serving", "description": "Specify multicast mechanisms to distribute inference requests and model updates with minimal latency across data centers.", "type": "requirement"}, {"title": "Bandwidth Optimization via Multicast", "description": "Identify gaps where existing multicast cannot achieve efficiency requirements for AI datacenter point-to-multipoint traffic.", "type": "requirement"}, {"title": "Reliable Multicast for Model Synchronization", "description": "Analyze reliability requirements for multicast in distributed AI training to ensure all nodes receive consistent model updates.", "type": "requirement"}, {"title": "Multi-tenant Multicast Isolation in AIDCs", "description": "Define requirements for isolating multicast groups between different AI workloads and tenants in shared data centers.", "type": "requirement"}, {"title": "Dynamic Multicast Group Management", "description": "Specify requirements for rapid multicast group creation and membership changes to match dynamic AI workload scaling.", "type": "requirement"}, {"title": "Congestion Control for AI Multicast Traffic", "description": "Identify gaps in existing multicast congestion control for handling bursty AI training and inference patterns.", "type": "requirement"}, {"title": "In-network Aggregation for AI Multicast", "description": "Analyze requirements for leveraging in-network compute to aggregate gradients and model updates in multicast trees.", "type": "requirement"}]


draft_name: draft-zhao-anima-automatic-congestion-relief
prompt_hash: 9ec89a38c41ba53d
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zhao-anima-automatic-congestion-relief]
created_at: 2026-03-03T19:54:10.112696+00:00
response_json:
  [{"title": "Intelligent Traffic Analysis for Congestion Detection", "description": "Implement machine learning-based analysis to detect network congestion patterns and anomalies in real-time.", "type": "mechanism"}, {"title": "Dynamic Traffic Regulation", "description": "Define algorithms to automatically adjust traffic routing and rate limiting in response to detected congestion.", "type": "mechanism"}, {"title": "Fiber Failure Self-Healing Response", "description": "Specify automatic rerouting and load balancing mechanisms triggered by fiber optic failures.", "type": "mechanism"}, {"title": "Real-time Congestion Notification", "description": "Design a protocol for network devices to communicate congestion status to enable coordinated relief actions.", "type": "protocol"}, {"title": "Autonomous Network Self-Healing Architecture", "description": "Define an autonomous system architecture where networks detect and remediate congestion without manual intervention.", "type": "architecture"}, {"title": "Congestion Relief Metrics and KPIs", "description": "Establish measurement criteria for evaluating effectiveness of automatic congestion relief mechanisms.", "type": "requirement"}]


draft_name: draft-zhao-detnet-enhanced-use-cases
prompt_hash: 14d6c9ecdb9e253e
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zhao-detnet-enhanced-use-cases]
created_at: 2026-03-03T19:54:20.790980+00:00
response_json:
  [{"title": "Industrial Internet Deterministic Requirements", "description": "Define network latency, jitter, and reliability constraints specific to industrial automation and control systems.", "type": "requirement"}, {"title": "High Experience Video Requirements", "description": "Specify deterministic networking requirements for low-latency, high-quality video delivery across distributed networks.", "type": "requirement"}, {"title": "Intelligent Computing Resource Allocation", "description": "Establish deterministic network requirements for distributed AI inference and edge computing workloads.", "type": "requirement"}, {"title": "ISAC-enabled Smart Factory Architecture", "description": "Define network requirements for integrated sensing, communication, and control in factory environments.", "type": "architecture"}, {"title": "Cross-domain Property Mapping", "description": "Identify and document common deterministic properties across industrial, video, computing, and ISAC use cases.", "type": "pattern"}]


draft_name: draft-zhu-anima-service-intent
prompt_hash: 7ff3f95607279572
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zhu-anima-service-intent]
created_at: 2026-03-03T19:54:20.808613+00:00
response_json:
  [{"title": "Service Intent Semantic Model", "description": "Define a structured semantic model for expressing service-level objectives spanning network, compute, and storage resources.", "type": "architecture"}, {"title": "Service Intent Format Specification", "description": "Specify a concise serialization format for service intent with identification, versioning, and lifecycle semantics.", "type": "protocol"}, {"title": "Multi-resource Constraint Expression", "description": "Enable expression of constraints across heterogeneous resources (network, compute, storage) in a unified framework.", "type": "mechanism"}, {"title": "Intent Scope Definition", "description": "Define mechanisms for scoping service intent to specific domains, services, or resource types within autonomic networks.", "type": "mechanism"}, {"title": "Service Intent Lifecycle Management", "description": "Establish semantics for service intent provisioning, activation, modification, and deprovisioning.", "type": "pattern"}, {"title": "AI Inference Service Intent Mapping", "description": "Map emerging AI inference service requirements to structured service intent representations.", "type": "extension"}]


draft_name: draft-zm-rtgwg-mcp-network-measurement
prompt_hash: 7357f640b0b79430
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zm-rtgwg-mcp-network-measurement]
created_at: 2026-03-03T19:54:20.815141+00:00
response_json:
  [{"title": "MCP Network Device Server Model", "description": "Define architecture for network devices to operate as MCP servers exposing measurement capabilities.", "type": "architecture"}, {"title": "Natural Language Measurement Commands", "description": "Enable LLM-driven network controllers to express measurement operations in natural language via MCP protocol.", "type": "mechanism"}, {"title": "Real-time Performance Monitoring Tool", "description": "Specify MCP tools for real-time network performance metrics collection and reporting.", "type": "mechanism"}, {"title": "Intelligent Fault Diagnosis Tool", "description": "Define MCP-based tool for AI-assisted network fault detection, root cause analysis, and diagnostics.", "type": "mechanism"}, {"title": "Automated Topology Discovery Tool", "description": "Specify MCP tool for automated network topology discovery and topology resource exposure.", "type": "mechanism"}, {"title": "Automated Measurement Workflows", "description": "Enable definition and execution of automated measurement workflows through MCP prompts.", "type": "pattern"}, {"title": "MCP Network Measurement Security Model", "description": "Define authentication, authorization, and data protection mechanisms for MCP-based network measurement.", "type": "requirement"}]


draft_name: draft-zw-nmrg-mcp-network-mgmt
prompt_hash: f48b5634e8308266
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-zw-nmrg-mcp-network-mgmt]
created_at: 2026-03-03T19:54:20.821270+00:00
response_json:
  [{"title": "MCP Capability Tokens for Network Equipment", "description": "Define new capability tokens identifying network-specific MCP server capabilities for equipment management.", "type": "extension"}, {"title": "Network Equipment MCP Tools", "description": "Specify MCP tool definitions for router and switch configuration, monitoring, and control operations.", "type": "mechanism"}, {"title": "Network Resource Representation", "description": "Define MCP resource types for representing network equipment state, configuration, and metrics.", "type": "mechanism"}, {"title": "Network Management MCP Prompts", "description": "Specify structured MCP prompts for common network equipment management tasks.", "type": "mechanism"}, {"title": "Network-specific MCP Error Codes", "description": "Define network equipment-specific error codes extending MCP's JSON-RPC error handling.", "type": "extension"}, {"title": "MCP Backward Compatibility for Networks", "description": "Ensure network equipment MCP extensions do not break existing MCP implementations.", "type": "requirement"}, {"title": "Controller-to-Equipment MCP Interaction", "description": "Define interaction patterns for network controllers acting as MCP clients controlling equipment servers.", "type": "pattern"}]


draft_name: draft-zw-rtgwg-mcp-network-mgmt
prompt_hash: e0b8f360b9daa5a6
model: claude-sonnet-4-20250514
created_at: 2026-03-03T19:54:34.944862+00:00
request_json:
  Extract discrete technical ideas and mechanisms from this IETF draft.
  Return a JSON array. Each element: {"title":"short name","description":"1-2 sentences","type":"mechanism|protocol|pattern|requirement|architecture|extension"}

  draft-zw-rtgwg-mcp-network-mgmt | Model Context Protocol (MCP) Extensions for Network Equipment Management | 18pg
  Abstract: The Model Context Protocol (MCP) provides a JSON-RPC 2.0 framework
     for interaction between AI applications and external context sources.
     This document specifies minimal extensions that allow network
     equipment (routers, switches, etc.) to act as MCP servers while
     controllers act as MCP clients.  New capability tokens, tools,
     resources, prompts, and error codes are defined without breaking
     existing MCP implementations.





  Network Working Group                                            G. Zeng
  Internet-Draft                                                     Q. Wu
  Intended status: Standards Track                                  Huawei
  Expires: 23 April 2026                                   20 October 2025


  Model Context Protocol (MCP) Extensions for Network Equipment Management
                     draft-zw-rtgwg-mcp-network-mgmt-00

  Abstract

     The Model Context Protocol (MCP) provides a JSON-RPC 2.0 framework
     for interaction between AI applications and external context sources.
     This document specifies minimal extensions that allow network
     equipment (routers, switches, etc.) to act as MCP servers while
     controllers act as MCP clients.  New capability tokens, tools,
     resources, prompts, and error codes are defined without breaking
     existing MCP implementations.

  Status of This Memo

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

     Internet-Drafts are working documents of the Internet Engineering
     Task Force (IETF).  Note that other groups may also distribute
     working documents as Internet-Drafts.  The list of current Internet-
     Drafts is at https://datatracker.ietf.org/drafts/current/.

     Internet-Drafts are draft documents valid for a maximum of six months
     and may be updated, replaced, or obsoleted by other documents at any
     time.  It is inappropriate to use Internet-Drafts as reference
     material or to cite them other than as "work in progress."

     This Internet-Draft will expire on 23 April 2026.

  Copyright Notice

     Copyright (c) 2025 IETF Trust and the persons identified as the
     document authors.  All rights reserved.











  Zeng & Wu                 Expires 23 April 2026                 [Page 1]


  Internet-Draft                   MCP-NET                    October 2025


     This document is subject to BCP 78 and the IETF Trust's Legal
     Provisions Relating to IETF Documents (https://trustee.ietf.org/
     license-info) in effect on the date of publication of this document.
     Please review these documents carefully, as they describe your rights
     and restrictions with respect to this document.  Code Components
     extracted from this document must include Revised BSD License text as
     described in Section 4.e of the Trust Legal Provisions and are
     provided without warranty as described in the Revised BSD License.

  Table of Contents

     1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.  Capability Advertisement  . . . . . . . . . . . . . . . . . .   3
       3.1.  Rationale for Each Field  . . . . . . . . . . . . . . . .   4
     4.  Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     5.  Resources . . . . . . . . . . . . . . . . . . . . . . . . . .   5
       5.1.  Standard URI Templates and MIME Types . . . . . . . . . .   5
       5.2.  Subscription a

  Return 3-8 ideas. Focus on CONCRETE technical contributions, not general statements.
  JSON array only, no fences.

response_json:
  [
    {
      "title": "Network Equipment MCP Server Role",
      "description": "Network equipment (routers, switches) act as MCP servers while controllers act as MCP clients, extending the Model Context Protocol framework to network management scenarios.",
      "type": "architecture"
    },
    {
      "title": "Network-Specific Capability Tokens",
      "description": "New capability advertisement tokens that allow network equipment to declare their MCP server capabilities during the initialization handshake.",
      "type": "extension"
    },
    {
      "title": "Network Management Tools via MCP",
      "description": "Standardized tool definitions that enable network controllers to invoke management operations on network equipment through the MCP tools interface.",
      "type": "mechanism"
    },
    {
      "title": "Network Resources with URI Templates",
      "description": "Standard URI templates and MIME types for accessing network equipment resources like configuration data, operational state, and monitoring information.",
      "type": "pattern"
    },
    {
      "title": "Network Equipment Error Codes",
      "description": "Specialized error codes for network management scenarios that extend the base MCP error handling without breaking existing implementations.",
      "type": "extension"
    }
  ]


draft_name: _landscape_
prompt_hash: aa661a80655fe511
model: claude-sonnet-4-20250514
created_at: 2026-03-03T19:58:26.199712+00:00
request_json:
  You are analyzing the landscape of 361 IETF Internet-Drafts related to AI agents and autonomous systems.

  ## Categories and Draft Counts
  - Data formats/interop: 145 drafts
  - A2A protocols: 120 drafts
  - Agent identity/auth: 108 drafts
  - Autonomous netops: 93 drafts
  - Policy/governance: 91 drafts
  - ML traffic mgmt: 73 drafts
  - Agent discovery/reg: 65 drafts
  - AI safety/alignment: 44 drafts
  - Model serving/inference: 42 drafts
  - Human-agent interaction: 30 drafts
  - Other AI/agent: 26 drafts
  - Agent-to-agent communication protocols: 16 drafts
  - Agent discovery / registration: 14 drafts
  - Identity / authentication for AI agents: 13 drafts
  - Autonomous network operations: 5 drafts
  - Data formats / semantics for AI interop: 3 drafts
  - Policy / governance / ethical frameworks: 2 drafts
  - AI safety / guardrails / alignment: 1 drafts
  - ML-based traffic management / optimization: 1 drafts

  ## Most Common Technical Ideas
  - Quantum-Safe Session Key Generation (3 drafts)
  - ACaaS for Dynamic Edge AI Placement (3 drafts)
  - TE Topology Model for Edge Network Performance (3 drafts)
  - Service Objective Mapping to YANG Models (3 drafts)
  - Real-Time AI Inference Network Requirements (3 drafts)
  - AC and TE YANG Model Gap Identification (3 drafts)
  - Agent Gateway (3 drafts)
  - Tool Enumeration API (3 drafts)
  - Tool Invocation API (3 drafts)
  - Agent Certificate Authority (3 drafts)
  - Agent Registration Server (3 drafts)
  - Agent Resource Type (2 drafts)
  - Agentic Application Resource Type (2 drafts)
  - Cross-Domain Agent Identity Management (2 drafts)
  - AI Workload Characterization (2 drafts)
  - Inter-DC AI Traffic Problem Statement (2 drafts)
  - Federated AI Service Architecture (2 drafts)
  - Private Data Training Network Requirements (2 drafts)
  - Semantic Routing (2 drafts)
  - Closed-Loop Security Analytics (2 drafts)

  ## Known Overlap Clusters (groups of highly similar drafts)
  Multiple clusters of near-duplicate drafts exist, particularly in:
  - Data formats/interop (145 drafts, high internal overlap)
  - A2A protocols (120 drafts, high internal overlap)
  - Agent identity/auth (108 drafts, high internal overlap)
  - Autonomous netops (93 drafts, high internal overlap)
  - Policy/governance (91 drafts, high internal overlap)


  Identify 8-15 GAPS — areas, problems, or technical challenges NOT adequately addressed by existing drafts.

  Return a JSON array:
  [{"topic":"short topic name","description":"2-3 sentence description","category":"closest category or new","severity":"critical|high|medium|low","evidence":"what suggests this gap matters"}]

  Focus on:
  1. Problems mentioned but not solved
  2. Missing infrastructure pieces
  3. Security/privacy/safety issues not addressed
  4. Interoperability gaps between competing proposals
  5. Real-world deployment concerns ignored

  JSON array only, no fences.

response_json:
  [
    {
      "topic": "Agent Behavior Verification",
      "description": "While many drafts address agent identity and authentication, few tackle how to verify that an agent is actually behaving according to its declared capabilities and policies. There's a critical gap in runtime behavioral attestation and compliance monitoring mechanisms.",
      "category": "AI safety/alignment",
      "severity": "critical",
      "evidence": "High overlap in identity/auth (108 drafts) but only 44 drafts on safety/alignment, with no specific focus on behavioral verification"
    },
    {
      "topic": "Cross-Domain Agent Liability",
      "description": "When autonomous agents operate across organizational boundaries and cause harm or make decisions with legal implications, there's no standardized framework for liability attribution. The policy/governance drafts don't address cross-jurisdictional legal accountability.",
      "category": "Policy/governance",
      "severity": "critical",
      "evidence": "91 policy/governance drafts but legal liability for cross-domain autonomous actions remains unaddressed"
    },
    {
      "topic": "Agent Resource Exhaustion Protection",
      "description": "Missing standardized mechanisms to prevent malicious or poorly designed agents from consuming excessive network, compute, or storage resources. Current drafts focus on traffic management but not on agent-specific resource quotas and enforcement.",
      "category": "Autonomous netops",
      "severity": "high",
      "evidence": "93 autonomous netops drafts and 73 ML traffic management drafts lack agent-specific resource protection mechanisms"
    },
    {
      "topic": "Human Override Protocols",
      "description": "Critical gap in standardized protocols for humans to safely interrupt, override, or take control of autonomous agents in emergency situations. Only 30 drafts address human-agent interaction, with no focus on emergency takeover procedures.",
      "category": "Human-agent interaction",
      "severity": "critical",
      "evidence": "Only 30 human-agent interaction drafts compared to 213+ autonomous operation drafts, with no emergency override standards"
    },
    {
      "topic": "Agent-Generated Data Provenance",
      "description": "While 145 drafts address data formats for AI interop, there's insufficient attention to tracking the provenance and lineage of data generated by agents. This creates trust and auditability issues in agent-to-agent data exchanges.",
      "category": "Data formats/interop",
      "severity": "high",
      "evidence": "145 data format drafts with high overlap but no clear standards for agent-generated data provenance tracking"
    },
    {
      "topic": "Agent Capability Degradation Handling",
      "description": "No standardized approaches for detecting and handling when an agent's capabilities degrade due to model drift, data corruption, or hardware issues. Systems need graceful degradation protocols rather than silent failures.",
      "category": "AI safety/alignment",
      "severity": "high",
      "evidence": "Only 44 safety/alignment drafts don't address capability degradation, while 213+ drafts assume stable agent performance"
    },
    {
      "topic": "Multi-Agent Coordination Deadlocks",
      "description": "With 120+ A2A protocol drafts, there's insufficient attention to preventing deadlock situations where multiple agents create circular dependencies or resource conflicts. Missing are standardized deadlock detection and resolution mechanisms.",
      "category": "A2A protocols",
      "severity": "high",
      "evidence": "120 A2A protocol drafts with high internal overlap but no systematic deadlock prevention frameworks"
    },
    {
      "topic": "Agent Privacy Preservation",
      "description": "Agents often process sensitive data but current drafts don't adequately address privacy-preserving computation, differential privacy, or secure multi-party computation for agent interactions. This is critical for deployment in regulated industries.",
      "category": "Agent identity/auth",
      "severity": "high",
      "evidence": "108 identity/auth drafts focus on authentication but lack privacy preservation mechanisms for agent data processing"
    },
    {
      "topic": "Real-time Agent Debugging",
      "description": "Missing standardized protocols for debugging autonomous agents in production environments. When agents make unexpected decisions, there are no standard interfaces for real-time introspection without disrupting operations.",
      "category": "Other AI/agent",
      "severity": "medium",
      "evidence": "26 other AI/agent drafts suggest various approaches but no standardized debugging protocols for production agents"
    },
    {
      "topic": "Agent Firmware/Model Update Security",
      "description": "While model serving is addressed in 42 drafts, there's insufficient focus on secure update mechanisms for agent models and firmware. Missing are standards for cryptographically verified, rollback-capable agent updates.",
      "category": "Model serving/inference",
      "severity": "high",
      "evidence": "42 model serving drafts but no comprehensive security standards for agent software/model updates"
    },
    {
      "topic": "Cross-Protocol Agent Migration",
      "description": "No standardized mechanisms for migrating agent state and context when moving between different A2A protocols or infrastructure providers. This creates vendor lock-in and limits agent mobility.",
      "category": "A2A protocols",
      "severity": "medium",
      "evidence": "120 A2A protocol drafts with high overlap suggest competing approaches but no migration standards between them"
    },
    {
      "topic": "Agent Energy Consumption Optimization",
      "description": "Missing standards for energy-aware agent deployment and operation. As AI workloads are energy-intensive, there's no framework for agents to optimize their energy consumption or for infrastructure to enforce energy budgets.",
      "category": "ML traffic mgmt",
      "severity": "medium",
      "evidence": "73 ML traffic management drafts focus on performance but lack energy consumption considerations for sustainable AI deployment"
    }
  ]


draft_name: draft-miller-ssh-agent
prompt_hash: 4fc23b8c24f2d199
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:39.525837+00:00
response_json:
  {"name": "draft-miller-ssh-agent", "s": "This draft documents the SSH agent protocol for secure key management. It provides standardization for an existing widely-used protocol.", "n": 1, "nn": "Documents existing SSH agent protocol with minimal novelty", "m": 4, "mn": "Well-established protocol with extensive real-world deployment", "o": 2, "on": "Some overlap with existing SSH specifications but fills documentation gap", "mo": 3, "mon": "Moderate momentum as it standardizes existing practice", "r": 4, "rn": "Highly relevant for SSH ecosystem standardization", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-zhao-nmrg-ai-agent-for-ndt
prompt_hash: 08e6543bc2e2e5c3
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:39.547761+00:00
response_json:
  {"name": "draft-zhao-nmrg-ai-agent-for-ndt", "s": "Proposes AI agent architecture for Network Digital Twin systems. Integrates AI agents with digital twin technology for network management.", "n": 3, "nn": "Novel combination of AI agents with network digital twin concepts", "m": 2, "mn": "Early stage work with limited technical depth at 8 pages", "o": 3, "on": "Some overlap with existing digital twin and AI networking research", "mo": 2, "mon": "Limited momentum, appears to be early research work", "r": 3, "rn": "Moderately relevant to emerging AI-driven network operations", "c": ["Autonomous netops", "Other AI/agent"]}


draft_name: draft-liu-agent-protocol-over-moq
prompt_hash: e11f90a92560a65a
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:39.558146+00:00
response_json:
  {"name": "draft-liu-agent-protocol-over-moq", "s": "Specifies agent-to-agent communication framework over Media over QUIC protocol. Provides structured, low-latency communication with session management and data fragmentation.", "n": 4, "nn": "Novel application of MoQ for agent communication with comprehensive framework", "m": 3, "mn": "Well-detailed at 22 pages but still emerging technology", "o": 2, "on": "Builds on MoQ but provides unique agent communication layer", "mo": 3, "mon": "Good technical depth suggests active development", "r": 4, "rn": "Highly relevant for emerging agent communication needs", "c": ["A2A protocols", "Data formats/interop"]}


draft_name: draft-wang-lisp-ai-agent
prompt_hash: 4cc320a135fe51cc
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:39.567725+00:00
response_json:
  {"name": "draft-wang-lisp-ai-agent", "s": "Explores using LISP protocol as network substrate for AI agent communication. Addresses location transparency, mobility, and multi-homing for distributed AI systems.", "n": 3, "nn": "Interesting application of existing LISP technology to AI agent networking", "m": 2, "mn": "Conceptual work at 9 pages, needs more technical development", "o": 2, "on": "Leverages LISP but applies it to new AI agent domain", "mo": 2, "mon": "Limited momentum, appears exploratory", "r": 3, "rn": "Moderately relevant for AI agent infrastructure needs", "c": ["A2A protocols", "Autonomous netops"]}


draft_name: draft-chen-oauth-scope-agent-extensions
prompt_hash: 9da933156112756e
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:39.576727+00:00
response_json:
  {"name": "draft-chen-oauth-scope-agent-extensions", "s": "Extends OAuth 2.0 scope parameter with structured syntax for fine-grained authorization of AI agent capabilities. Addresses permission management for modular agent components.", "n": 4, "nn": "Novel structured approach to OAuth scopes for agent authorization", "m": 3, "mn": "Well-developed specification with detailed syntax and use cases", "o": 2, "on": "Extends OAuth but provides unique agent-specific capabilities", "mo": 3, "mon": "Detailed specification suggests good development momentum", "r": 4, "rn": "Highly relevant for agent security and authorization needs", "c": ["Agent identity/auth", "Policy/governance"]}


draft_name: draft-hw-protocol-agent
prompt_hash: 545f256db7eed486
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:59.679816+00:00
response_json:
  {"name": "draft-hw-protocol-agent", "s": "Proposes network-level recognition of AI agent multi-modal data transmission requirements. Aims to improve quality of service for diverse data types within agent traffic flows.", "n": 3, "nn": "Network awareness of multi-modal AI traffic is emerging but not deeply explored in IETF context", "m": 2, "mn": "Early stage work with broad scope but limited technical detail on implementation", "o": 2, "on": "Some overlap with general QoS and traffic management but specific to AI agents", "mo": 3, "mon": "Growing interest in AI traffic optimization but limited industry engagement visible", "r": 4, "rn": "Highly relevant as AI agent traffic becomes dominant network workload", "c": ["ML traffic mgmt", "A2A protocols"]}


draft_name: draft-klrc-aiagent-auth
prompt_hash: acdec2d6f40d5fae
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:59.702854+00:00
response_json:
  {"name": "draft-klrc-aiagent-auth", "s": "Describes authentication and authorization framework for AI agents using existing standards like WIMSE and OAuth 2.0. Focuses on applying current protocols rather than creating new ones.", "n": 3, "nn": "Practical approach leveraging existing standards, though AI agent auth is increasingly studied", "m": 4, "mn": "Well-developed framework building on established protocols with clear implementation path", "o": 3, "on": "Builds heavily on WIMSE and OAuth but applies them to new AI agent context", "mo": 4, "mon": "Strong foundation in existing IETF work with clear industry need", "r": 5, "rn": "Critical need as AI agents proliferate and require secure authentication mechanisms", "c": ["Agent identity/auth", "A2A protocols"]}


draft_name: draft-nandakumar-ai-agent-moq-transport
prompt_hash: af908a79bc0e33a1
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:59.713845+00:00
response_json:
  {"name": "draft-nandakumar-ai-agent-moq-transport", "s": "Defines abstraction layer for using MOQ Transport as unified substrate for inter-agent communication. Demonstrates application to A2A and MCP protocols.", "n": 4, "nn": "Novel application of MOQT to agent protocols with specific abstraction layer design", "m": 4, "mn": "Detailed technical specification with concrete protocol mappings and examples", "o": 2, "on": "Builds on MOQT but creates new abstraction specifically for agent communication", "mo": 4, "mon": "Strong technical foundation and addresses real interoperability challenges", "r": 4, "rn": "Important for standardizing agent communication transport with real-time requirements", "c": ["A2A protocols", "Data formats/interop"]}


draft_name: draft-dunbar-agent-attachment
prompt_hash: d346ae72c1b04e81
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:59.726524+00:00
response_json:
  {"name": "draft-dunbar-agent-attachment", "s": "Describes protocol for autonomous agents to securely attach to overlay network infrastructure. Covers identity validation, capability exchange, and session establishment.", "n": 4, "nn": "Network attachment specifically designed for autonomous agents is a new concept", "m": 3, "mn": "Clear protocol definition but needs more detail on security and scalability aspects", "o": 2, "on": "Some similarity to general attachment protocols but specifically designed for agents", "mo": 3, "mon": "Addresses fundamental need but limited visible industry adoption yet", "r": 4, "rn": "Essential capability as agents become more autonomous and network-distributed", "c": ["Agent discovery/reg", "Agent identity/auth", "Autonomous netops"]}


draft_name: draft-jaju-httpbis-zstd-window-size
prompt_hash: 1514e07033d6d33d
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:00:59.735187+00:00
response_json:
  {"name": "draft-jaju-httpbis-zstd-window-size", "s": "Updates RFC8878 to make window size limits for Zstandard compression a requirement rather than recommendation. Addresses browser interoperability issues.", "n": 2, "nn": "Incremental update to existing standard addressing known interoperability issue", "m": 5, "mn": "Mature, focused fix to specific technical problem with clear implementation guidance", "o": 4, "on": "Direct update to existing RFC8878 with minimal scope changes", "mo": 3, "mon": "Addresses real deployment issues but limited scope for broader impact", "r": 2, "rn": "Important for HTTP compression but not directly related to AI/agent protocols", "c": ["Data formats/interop"]}


draft_name: draft-bernardos-anima-fog-monitoring
prompt_hash: 14ffe38c66a0aea6
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:20.644312+00:00
response_json:
  {"name": "draft-bernardos-anima-fog-monitoring", "s": "Proposes using GRASP for autonomic setup of monitoring agents in dynamic fog computing environments. Addresses the challenge of managing volatile, heterogeneous fog resources through self-configuring monitoring infrastructure.", "n": 3, "nn": "Applies existing autonomic networking concepts to fog monitoring, reasonable novelty in the specific use case", "m": 2, "mn": "Early stage draft with basic framework outlined but lacks detailed protocol specifications", "o": 3, "on": "Some overlap with general autonomic networking and GRASP usage patterns", "mo": 2, "mon": "Limited recent activity, fog computing adoption has been slower than anticipated", "r": 3, "rn": "Relevant for IoT and edge computing scenarios where dynamic resource management is needed", "c": ["Autonomous netops", "Agent discovery/reg"]}


draft_name: draft-ietf-httpbis-zstd-window-size
prompt_hash: d4ab993e34b24889
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:20.665682+00:00
response_json:
  {"name": "draft-ietf-httpbis-zstd-window-size", "s": "Updates RFC8878 to make Zstandard window size limits a requirement rather than recommendation for HTTP contexts. Addresses interoperability issues caused by browser memory limitations.", "n": 2, "nn": "Incremental update to existing compression standard, limited novelty", "m": 4, "mn": "Well-defined problem with clear solution, appears ready for implementation", "o": 1, "on": "Specific update to compression standard, minimal overlap with other work", "mo": 4, "mon": "IETF working group draft with recent updates, good standardization momentum", "r": 4, "rn": "High relevance for web performance and interoperability", "c": ["Data formats/interop"]}


draft_name: draft-ietf-dnsop-dns-error-reporting
prompt_hash: 1e3c84d57b053bc6
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:20.675072+00:00
response_json:
  {"name": "draft-ietf-dnsop-dns-error-reporting", "s": "Defines a lightweight mechanism for DNS resolvers to report resolution/validation errors to authoritative servers. Uses extended DNS errors and encodes reports in QNAMEs for automatic signaling.", "n": 3, "nn": "Novel approach to DNS error feedback using QNAME encoding for reporting", "m": 3, "mn": "Reasonably mature with clear protocol design, builds on established DNS error extensions", "o": 2, "on": "Related to DNS monitoring but uses unique QNAME-based reporting approach", "mo": 3, "mon": "IETF working group draft with steady progress", "r": 4, "rn": "High relevance for DNS operations and security, addresses real operational need", "c": ["Autonomous netops", "Policy/governance"]}


draft_name: draft-herbert-fast
prompt_hash: 8a4c61703a0faa01
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:20.685314+00:00
response_json:
  {"name": "draft-herbert-fast", "s": "Introduces Firewall and Service Tickets protocol for hosts to request network services and admission. Uses IPv6 Hop-by-Hop options to carry tickets that grant traversal rights or specify service requirements.", "n": 4, "nn": "Novel approach to network admission control and service signaling using ticket-based system", "m": 2, "mn": "Comprehensive but early-stage proposal, needs more implementation experience", "o": 3, "on": "Some conceptual overlap with network access control and QoS signaling mechanisms", "mo": 2, "mon": "Individual submission with limited broader community engagement", "r": 3, "rn": "Potentially relevant for zero-trust networking and service mesh architectures", "c": ["Agent identity/auth", "Policy/governance"]}


draft_name: draft-vattaparambil-positioning-of-poa
prompt_hash: 05afeffbc5b0880f
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:20.695659+00:00
response_json:
  {"name": "draft-vattaparambil-positioning-of-poa", "s": "Positions Power of Attorney based authorization as a decentralized subgranting technique for constrained environments. Compares PoA with existing authorization protocols and identity solutions.", "n": 3, "nn": "Interesting application of legal concept to technical authorization, moderate novelty", "m": 2, "mn": "Positioning paper rather than detailed protocol specification, needs more technical depth", "o": 4, "on": "Significant overlap with OAuth, delegation protocols, and capability-based security", "mo": 1, "mon": "Individual submission with minimal recent activity or community interest", "r": 2, "rn": "Limited immediate relevance, more academic positioning than practical protocol", "c": ["Agent identity/auth", "Policy/governance"]}


draft_name: draft-ietf-dtn-bpv7-admin-iana
prompt_hash: 47654030f0fd1671
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:35.901507+00:00
response_json:
  {"name": "draft-ietf-dtn-bpv7-admin-iana", "s": "Updates Bundle Protocol Version 7 to clarify use of IANA registry for Administrative Record types. Reserves code points for private and experimental use.", "n": 2, "nn": "Registry clarification rather than new protocol features", "m": 4, "mn": "Well-defined registry update with clear IANA considerations", "o": 1, "on": "Specific to Bundle Protocol administrative records", "mo": 3, "mon": "IETF working group draft with focused scope", "r": 2, "rn": "Limited to DTN Bundle Protocol implementations", "c": ["Data formats/interop"]}


draft_name: draft-he-yi-srv6ops-ipv6-enhancemnet-in-cloud-uc
prompt_hash: b2ebd7f53c212d1a
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:35.920550+00:00
response_json:
  {"name": "draft-he-yi-srv6ops-ipv6-enhancemnet-in-cloud-uc", "s": "Outlines use cases for IPv6 enhancement techniques in cloud environments. Proposes Virtual Incloud Router as agent for end-to-end IPv6 capabilities.", "n": 3, "nn": "Combines existing IPv6 enhancement with cloud integration", "m": 2, "mn": "Use case document with spelling errors and unclear requirements", "o": 3, "on": "Overlaps with SRv6 and cloud networking work", "mo": 2, "mon": "Individual submission with limited recent updates", "r": 3, "rn": "Relevant to cloud-network integration trends", "c": ["Agent identity/auth", "Autonomous netops"]}


draft_name: draft-ietf-sip-location-conveyance
prompt_hash: 20ba52d19a7d59d7
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:35.929393+00:00
response_json:
  {"name": "draft-ietf-sip-location-conveyance", "s": "Defines SIP extension for conveying geographic location information between entities. Covers end-to-end conveyance and location-based routing decisions.", "n": 3, "nn": "Applies established location concepts to SIP protocol", "m": 4, "mn": "Comprehensive 46-page specification with detailed procedures", "o": 2, "on": "Some overlap with existing location and SIP work", "mo": 4, "mon": "Active IETF working group draft with recent updates", "r": 3, "rn": "Useful for location-aware SIP applications", "c": ["A2A protocols", "Agent discovery/reg"]}


draft_name: draft-ietf-websec-mime-sniff
prompt_hash: 1dcfb0bc9856410a
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:35.941360+00:00
response_json:
  {"name": "draft-ietf-websec-mime-sniff", "s": "Describes algorithm for determining effective media type of HTTP responses. Balances security and compatibility when Content-Type headers are incorrect.", "n": 3, "nn": "Formalizes existing browser behavior into standard algorithm", "m": 4, "mn": "Well-developed specification addressing real interoperability issues", "o": 2, "on": "Related to HTTP and web security standards", "mo": 4, "mon": "Active IETF working group with recent updates", "r": 4, "rn": "High relevance for web security and interoperability", "c": ["Data formats/interop", "Policy/governance"]}


draft_name: draft-ietf-vwrap-authentication
prompt_hash: bc6c5c52b19d158a
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:35.950795+00:00
response_json:
  {"name": "draft-ietf-vwrap-authentication", "s": "Establishes authentication for Virtual World Region Agent Protocol. Verifies user credentials before granting control over user's agent and digital assets.", "n": 4, "nn": "Novel application of authentication to virtual world agents", "m": 3, "mn": "Solid specification but limited to virtual world domain", "o": 2, "on": "Some overlap with general authentication protocols", "mo": 3, "mon": "IETF working group but specialized application area", "r": 4, "rn": "Highly relevant for virtual world and metaverse applications", "c": ["Agent identity/auth", "A2A protocols", "Human-agent interaction"]}


draft_name: draft-hy-srv6ops-sfc-in-cloud-uc
prompt_hash: c467fa67000f97fb
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:56.694800+00:00
response_json:
  {"name": "draft-hy-srv6ops-sfc-in-cloud-uc", "s": "Defines use cases for implementing Service Function Chaining using SRv6 in cloud environments. Proposes virtual routers as agents to enable SRv6 capabilities in cloud services.", "n": 2, "nn": "SRv6 and SFC are established technologies, limited novelty in cloud application", "m": 2, "mn": "Early draft with basic use case outline, lacks detailed technical specifications", "o": 3, "on": "Overlaps with existing SRv6 and SFC standardization efforts", "mo": 2, "mon": "Niche cloud-network integration topic with limited broad appeal", "r": 3, "rn": "Relevant to cloud-network convergence but serves specific operator needs", "c": ["Autonomous netops", "Data formats/interop"]}


draft_name: draft-illyes-rep-purpose
prompt_hash: bf8eaf1eb06f47be
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:56.717356+00:00
response_json:
  {"name": "draft-illyes-rep-purpose", "s": "Extends the Robots Exclusion Protocol to target automatic clients based on their access purpose rather than just user-agent strings. Provides more granular control over automated access.", "n": 3, "nn": "Logical extension to existing robots.txt with purpose-based targeting", "m": 3, "mn": "Concise extension with clear scope, builds on established RFC", "o": 2, "on": "Extends RFC9309 but addresses different targeting mechanism", "mo": 3, "mon": "Moderate interest from web operators dealing with diverse automated clients", "r": 4, "rn": "Highly relevant as AI agents and web crawlers proliferate", "c": ["Agent identity/auth", "Policy/governance", "Agent discovery/reg"]}


draft_name: draft-vesely-dmarc-mlm-transform
prompt_hash: a066ae65f5320b8b
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:56.727542+00:00
response_json:
  {"name": "draft-vesely-dmarc-mlm-transform", "s": "Proposes method to revert mailing list transformations of From headers caused by DMARC adoption. Uses author-centric strategy to restore original sender information.", "n": 3, "nn": "Addresses real problem created by DMARC deployment, novel restoration approach", "m": 4, "mn": "Well-developed 17-page specification with detailed technical approach", "o": 2, "on": "Unique approach to DMARC-MLM compatibility issue", "mo": 3, "mon": "Important for email infrastructure but affects specific operator segment", "r": 3, "rn": "Addresses ongoing email authentication challenges", "c": ["Data formats/interop", "Policy/governance"]}


draft_name: draft-annevk-johannhof-httpbis-cookies
prompt_hash: 04428c018abeecd7
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:56.738397+00:00
response_json:
  {"name": "draft-annevk-johannhof-httpbis-cookies", "s": "Comprehensive revision of HTTP cookie mechanism, updating RFC 6265. Addresses security and privacy issues while maintaining backward compatibility for widely deployed technology.", "n": 2, "nn": "Incremental update to existing standard rather than novel approach", "m": 5, "mn": "Mature 43-page specification ready to obsolete existing RFCs", "o": 1, "on": "Direct successor to RFC 6265, minimal overlap with other efforts", "mo": 5, "mon": "Critical web infrastructure update with broad industry impact", "r": 5, "rn": "Essential for web security and privacy, affects all web interactions", "c": ["Data formats/interop", "Policy/governance"]}


draft_name: draft-daviel-html-geo-tag
prompt_hash: 420b670c12a32580
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:01:56.756478+00:00
response_json:
  {"name": "draft-daviel-html-geo-tag", "s": "Defines method for embedding geographic location in HTML documents using META tags. Enables automated resource discovery based on latitude, longitude and civic location.", "n": 2, "nn": "Geographic tagging concept exists, limited novelty in HTML META approach", "m": 3, "mn": "Complete specification but addresses legacy HTML approach", "o": 4, "on": "Significant overlap with existing geolocation and metadata standards", "mo": 2, "mon": "Limited momentum given modern geolocation alternatives", "r": 2, "rn": "Low relevance due to superior modern geolocation methods", "c": ["Data formats/interop", "Agent discovery/reg"]}


draft_name: draft-moonesamy-rfc2369bis
prompt_hash: 441e82ec0c4f18ec
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:15.556607+00:00
response_json:
  {"name": "draft-moonesamy-rfc2369bis", "s": "Updates RFC 2369 to define standardized email header fields for mailing list commands like subscribe, unsubscribe, and help. Enables mail clients to provide automated tools for common mailing list operations.", "n": 2, "nn": "Incremental update to existing RFC with minor refinements", "m": 4, "mn": "Well-established concept being refined, clear specification", "o": 1, "on": "Unique update to specific RFC, no overlap with other drafts", "mo": 2, "mon": "Limited community interest in mailing list standards", "r": 2, "rn": "Relevant to email systems but not AI/agent focused", "c": ["Other AI/agent"]}


draft_name: draft-moonesamy-rfc2919bis
prompt_hash: 9556ba11c21b2450
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:15.577716+00:00
response_json:
  {"name": "draft-moonesamy-rfc2919bis", "s": "Updates RFC 2919 to define the List-Id header field for uniquely identifying mailing lists. Provides namespace based on domain names to ensure global uniqueness.", "n": 2, "nn": "Standard maintenance update with minimal new concepts", "m": 4, "mn": "Mature specification being updated, well-defined scope", "o": 1, "on": "Companion to RFC 2369bis but addresses different aspect", "mo": 2, "mon": "Limited broader community engagement expected", "r": 2, "rn": "Email infrastructure relevant but not AI/agent specific", "c": ["Other AI/agent"]}


draft_name: draft-rosenberg-ai-protocols
prompt_hash: ec3c1e084d6b9733
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:15.588576+00:00
response_json:
  {"name": "draft-rosenberg-ai-protocols", "s": "Establishes framework for AI agent communications on the Internet, surveying existing protocols like MCP and A2A. Defines use cases and requirements to guide future IETF standardization efforts.", "n": 4, "nn": "First comprehensive framework for AI agent protocol standardization", "m": 3, "mn": "Framework document with clear structure but early stage", "o": 2, "on": "Some overlap with existing AI protocol efforts but unique in scope", "mo": 4, "mon": "High interest area with strong industry momentum", "r": 5, "rn": "Directly addresses core AI agent communication needs", "c": ["A2A protocols", "Agent discovery/reg", "Policy/governance"]}


draft_name: draft-patwhite-aauth
prompt_hash: 66f710c33a0add30
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:15.601284+00:00
response_json:
  {"name": "draft-patwhite-aauth", "s": "Defines OAuth 2.1 extension for AI agents to obtain user consent and access tokens through polling or real-time channels. Adapts device flow for long-lived agent identities with natural language descriptions.", "n": 3, "nn": "Novel adaptation of OAuth device flow specifically for AI agents", "m": 2, "mn": "Early stage specification, needs more detail and security analysis", "o": 3, "on": "Builds heavily on OAuth 2.0 device flow but for different use case", "mo": 3, "mon": "Moderate interest as agent authorization becomes more important", "r": 4, "rn": "Addresses critical agent authorization gap in current standards", "c": ["Agent identity/auth", "Human-agent interaction"]}


draft_name: draft-lkspa-wimse-verifiable-geo-fence
prompt_hash: 721b4d762b108e1b
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:15.610696+00:00
response_json:
  {"name": "draft-lkspa-wimse-verifiable-geo-fence", "s": "Defines hardware-rooted cryptographic framework for verifying workload location without compromising privacy using zero-knowledge proofs. Establishes silicon-to-workload chain of trust for sovereign computing environments.", "n": 5, "nn": "Novel combination of hardware attestation, geofencing, and ZK proofs", "m": 2, "mn": "Complex early-stage proposal requiring significant development", "o": 2, "on": "Some overlap with attestation work but unique privacy approach", "mo": 3, "mon": "Growing interest in sovereign computing and privacy-preserving location", "r": 4, "rn": "Highly relevant for secure AI workload deployment and compliance", "c": ["Agent identity/auth", "Policy/governance", "AI safety/alignment"]}


draft_name: draft-allman-tcp-early-rexmt
prompt_hash: 0595381eecfa1fe6
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:31.713384+00:00
response_json:
  {"name": "draft-allman-tcp-early-rexmt", "s": "Proposes Early Retransmit mechanism for TCP and SCTP to recover lost segments when congestion window is small. Reduces duplicate ACKs needed for fast retransmission to avoid lengthy timeouts.", "n": 3, "nn": "Incremental improvement to existing TCP recovery mechanisms", "m": 4, "mn": "Well-established problem space with clear technical approach", "o": 2, "on": "Some overlap with other TCP optimization drafts but specific focus", "mo": 3, "mon": "TCP improvements have steady interest but not high priority", "r": 4, "rn": "Addresses real performance issues in low-bandwidth scenarios", "c": ["Other AI/agent"]}


draft_name: draft-allman-tcpx2-hack
prompt_hash: 1d2f3f28fb25ae84
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:31.735820+00:00
response_json:
  {"name": "draft-allman-tcpx2-hack", "s": "Proposes increasing TCP header size without changing protocol semantics. Aims to solve problems caused by TCP's lack of header space for certain values.", "n": 2, "nn": "Header extension concept exists, specific approach may be novel", "m": 2, "mn": "Early stage work, deployment challenges unclear", "o": 3, "on": "Multiple TCP extension mechanisms already proposed", "mo": 2, "mon": "Limited community momentum for TCP header changes", "r": 3, "rn": "Addresses known limitation but deployment barriers high", "c": ["Other AI/agent"]}


draft_name: draft-mandel-lamps-rfc5274bis
prompt_hash: 2bf20acc82994ad6
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:31.747886+00:00
response_json:
  {"name": "draft-mandel-lamps-rfc5274bis", "s": "Updates compliance requirements for Certificate Management Messages over CMS protocol. Obsoletes previous RFCs 5274 and 6402 with current best practices.", "n": 2, "nn": "Maintenance update rather than novel contribution", "m": 4, "mn": "Mature protocol area with clear requirements", "o": 1, "on": "Direct replacement for obsoleted RFCs", "mo": 3, "mon": "PKI standards have consistent but modest momentum", "r": 4, "rn": "Important for PKI interoperability and compliance", "c": ["Agent identity/auth"]}


draft_name: draft-tschofenig-lamps-nonce-cmp-est
prompt_hash: 65571a140d8c8128
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:31.759173+00:00
response_json:
  {"name": "draft-tschofenig-lamps-nonce-cmp-est", "s": "Specifies nonce mechanisms for demonstrating freshness of attestation evidence in certificate signing requests. Covers both CMP and EST protocols.", "n": 4, "nn": "Novel integration of attestation freshness with certificate protocols", "m": 3, "mn": "Building on established protocols but new use case", "o": 2, "on": "Some overlap with general attestation work", "mo": 4, "mon": "Growing interest in attestation and zero-trust architectures", "r": 5, "rn": "Highly relevant for secure device provisioning and attestation", "c": ["Agent identity/auth"]}


draft_name: draft-tiloca-lake-edhoc-implem-cons
prompt_hash: 2a9aa07769ff304f
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:31.768826+00:00
response_json:
  {"name": "draft-tiloca-lake-edhoc-implem-cons", "s": "Provides implementation guidance for EDHOC authenticated key exchange protocol. Offers practical considerations for deploying Ephemeral Diffie-Hellman Over COSE.", "n": 2, "nn": "Implementation guide rather than protocol innovation", "m": 4, "mn": "Based on established EDHOC protocol specification", "o": 1, "on": "Complementary to main EDHOC specification", "mo": 3, "mon": "IoT security protocols have steady interest", "r": 4, "rn": "Important for correct EDHOC deployment in IoT scenarios", "c": ["Agent identity/auth"]}


draft_name: draft-peterson-mimi-idprover
prompt_hash: f5a350139444cffb
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:50.667247+00:00
response_json:
  {"name": "draft-peterson-mimi-idprover", "s": "Defines a mechanism for third-party services to prove and attest the association between communication platforms and users' telephone numbers. Supports secure enrollment for messaging platforms requiring verified phone number identifiers.", "n": 3, "nn": "Moderate novelty in combining proof-of-possession with phone number attestation for messaging interoperability", "m": 2, "mn": "Early stage draft with basic specification, needs more implementation details", "o": 2, "on": "Some overlap with general identity attestation work but specific to messaging platforms", "mo": 3, "mon": "Part of active MIMI working group efforts for messaging interoperability", "r": 4, "rn": "Highly relevant for secure messaging and identity verification in communication systems", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-song-lake-ra
prompt_hash: 5b1d5b0cfc2f82a3
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:50.686037+00:00
response_json:
  {"name": "draft-song-lake-ra", "s": "Specifies how to perform remote attestation as part of the EDHOC key exchange protocol. Based on the RATS architecture for lightweight authenticated key exchange.", "n": 4, "nn": "Good novelty in integrating remote attestation with EDHOC protocol", "m": 3, "mn": "Well-developed 26-page specification building on established protocols", "o": 3, "on": "Overlaps with general remote attestation work but specific EDHOC integration", "mo": 4, "mon": "Recent draft with active development, builds on mature EDHOC work", "r": 4, "rn": "Very relevant for IoT and constrained device security requiring attestation", "c": ["Agent identity/auth", "A2A protocols"]}


draft_name: draft-ietf-lake-edhoc
prompt_hash: 2ce7c5fa20e334fc
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:50.706698+00:00
response_json:
  {"name": "draft-ietf-lake-edhoc", "s": "Specifies EDHOC, a compact authenticated Diffie-Hellman key exchange protocol for constrained environments. Provides mutual authentication, forward secrecy, and identity protection using COSE, CBOR, and CoAP.", "n": 4, "nn": "Strong novelty in creating lightweight authenticated key exchange for constrained devices", "m": 5, "mn": "Mature 113-page specification from IETF working group, well-developed protocol", "o": 2, "on": "Unique approach to lightweight authenticated key exchange, minimal overlap", "mo": 5, "mon": "Official IETF working group draft with strong momentum and adoption", "r": 5, "rn": "Extremely relevant for IoT security and constrained device communications", "c": ["A2A protocols", "Agent identity/auth"]}


draft_name: draft-yan-anima-brski-cle
prompt_hash: 7b477cf1f624005e
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:50.724305+00:00
response_json:
  {"name": "draft-yan-anima-brski-cle", "s": "Defines a certificateless enrollment framework for constrained IoT devices in BRSKI. Uses Key Encapsulation Mechanism (KEM) for quantum-safe algorithms and lightweight credentials like CWTs.", "n": 4, "nn": "High novelty in combining certificateless enrollment with quantum-safe KEM for IoT", "m": 2, "mn": "Early stage 10-page draft, references other work but lacks implementation details", "o": 3, "on": "Overlaps with BRSKI and IoT enrollment work but unique certificateless approach", "mo": 3, "mon": "Part of ANIMA working group but relatively new approach", "r": 4, "rn": "Highly relevant for constrained IoT device bootstrapping and quantum-safe security", "c": ["Agent identity/auth", "Autonomous netops", "Agent discovery/reg"]}


draft_name: draft-ounsworth-lamps-cms-dhkem
prompt_hash: 4b30d50a5faa5b63
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:02:50.733264+00:00
response_json:
  {"name": "draft-ounsworth-lamps-cms-dhkem", "s": "Defines mechanism to use DHKEM algorithm within CMS for one-pass key establishment. Wraps Ephemeral-Static DH and ECDH for KEM interfaces in cryptographic messaging.", "n": 3, "nn": "Moderate novelty in adapting DHKEM for CMS, incremental improvement", "m": 3, "mn": "Concise 8-page specification with clear technical approach", "o": 4, "on": "Significant overlap with existing KEM work and CMS specifications", "mo": 3, "mon": "Part of established LAMPS working group efforts", "r": 3, "rn": "Moderately relevant for secure messaging but more focused on cryptographic infrastructure", "c": ["Data formats/interop", "A2A protocols"]}


draft_name: draft-ingles-eap-edhoc
prompt_hash: 70a45f70f57341f5
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:09.801016+00:00
response_json:
  {"name": "draft-ingles-eap-edhoc", "s": "Specifies how to use EDHOC (Ephemeral Diffie-Hellman Over COSE) as an authentication method within the Extensible Authentication Protocol framework. Provides lightweight authenticated key exchange for network access scenarios.", "n": 3, "nn": "Combines existing EAP framework with EDHOC protocol in a straightforward integration", "m": 3, "mn": "Reasonable specification but limited implementation experience evident", "o": 2, "on": "Some overlap with other EAP methods but EDHOC integration is relatively unique", "mo": 3, "mon": "Part of broader EDHOC ecosystem but individual draft momentum unclear", "r": 3, "rn": "Relevant for IoT and constrained device authentication scenarios", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-ietf-lake-traces
prompt_hash: ea5b2fd42b00a0fa
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:09.821293+00:00
response_json:
  {"name": "draft-ietf-lake-traces", "s": "Contains example protocol traces and test vectors for the EDHOC protocol. Serves as implementation guidance and verification material for EDHOC developers.", "n": 2, "nn": "Documentation of examples rather than novel protocol development", "m": 4, "mn": "Well-developed traces document from established IETF working group", "o": 1, "on": "Unique traces document, no significant overlap with other drafts", "mo": 4, "mon": "Strong momentum as official IETF working group item with recent updates", "r": 3, "rn": "Important for EDHOC implementation but primarily developer-focused utility", "c": ["Data formats/interop"]}


draft_name: draft-lopez-lake-edhoc-psk
prompt_hash: 13bc3de23b136e10
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:09.832489+00:00
response_json:
  {"name": "draft-lopez-lake-edhoc-psk", "s": "Defines Pre-Shared Key authentication method for EDHOC protocol. Specifies message flows and security considerations for PSK-based EDHOC authentication.", "n": 2, "nn": "Extends existing EDHOC with well-known PSK authentication approach", "m": 2, "mn": "Recent draft with limited detail and implementation maturity", "o": 3, "on": "Moderate overlap with core EDHOC specification and other auth methods", "mo": 2, "mon": "Individual contribution with uncertain working group adoption prospects", "r": 3, "rn": "Useful for scenarios requiring PSK authentication with EDHOC", "c": ["Agent identity/auth"]}


draft_name: draft-tiloca-lake-app-profiles
prompt_hash: 7978b2d4c2f5d2a4
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:09.842248+00:00
response_json:
  {"name": "draft-tiloca-lake-app-profiles", "s": "Defines coordination mechanisms for EDHOC application profiles including discovery, canonical representation, and well-known profiles. Addresses out-of-band parameter agreement challenges.", "n": 4, "nn": "Novel approach to profile coordination and discovery for EDHOC deployments", "m": 3, "mn": "Comprehensive specification but implementation experience limited", "o": 2, "on": "Complements other EDHOC work without significant duplication", "mo": 3, "mon": "Recent activity and detailed specification suggests moderate momentum", "r": 4, "rn": "Addresses important operational deployment challenges for EDHOC", "c": ["Data formats/interop", "Agent discovery/reg"]}


draft_name: draft-ietf-dnsop-dnssec-bootstrapping
prompt_hash: 7304447c022fb9c2
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:09.851390+00:00
response_json:
  {"name": "draft-ietf-dnsop-dnssec-bootstrapping", "s": "Introduces automated DNSSEC bootstrapping using authenticated signals from zone operators. Enables cryptographic validation of CDS/CDNSKEY records without out-of-band processes.", "n": 4, "nn": "Novel in-band method for DNSSEC bootstrapping replacing manual processes", "m": 4, "mn": "Mature specification from established working group with clear implementation path", "o": 2, "on": "Updates existing RFCs but provides unique automated approach", "mo": 4, "mon": "Active IETF working group item with collaborative development on GitHub", "r": 4, "rn": "Highly relevant for DNS infrastructure automation and security", "c": ["Autonomous netops", "Policy/governance"]}


draft_name: draft-ar-emu-pqc-eapaka
prompt_hash: 569c83c63df1f21b
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:29.467912+00:00
response_json:
  {"name": "draft-ar-emu-pqc-eapaka", "s": "Enhances EAP-AKA' forward secrecy with post-quantum/traditional hybrid cryptography to protect against future quantum computer threats. Builds on RFC9678 by replacing ECDHE with quantum-safe algorithms.", "n": 4, "nn": "Novel application of PQ/T hybrid approach to EAP-AKA', addressing specific quantum threats to cellular authentication", "m": 3, "mn": "Early draft with clear problem statement but implementation details need refinement", "o": 2, "on": "Some overlap with general PQ hybrid work but specific to EAP-AKA' protocol", "mo": 4, "mon": "Strong momentum given urgent need for post-quantum migration in cellular networks", "r": 4, "rn": "High relevance for mobile network security and quantum readiness", "c": ["Other AI/agent"]}


draft_name: draft-kwiatkowski-tls-ecdhe-mlkem
prompt_hash: 529819aeb4b23b0f
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:29.490932+00:00
response_json:
  {"name": "draft-kwiatkowski-tls-ecdhe-mlkem", "s": "Defines three hybrid key agreement methods for TLS 1.3 combining post-quantum ML-KEM with elliptic curve Diffie-Hellman. Provides concrete ciphersuites for quantum-safe TLS.", "n": 3, "nn": "Standard application of established hybrid approach to TLS, well-executed but incremental", "m": 4, "mn": "Mature specification with clear definitions and practical focus", "o": 3, "on": "Moderate overlap with other TLS post-quantum proposals but different KEM choices", "mo": 5, "mon": "Very high momentum due to NIST ML-KEM standardization and TLS deployment needs", "r": 5, "rn": "Extremely relevant for widespread TLS quantum readiness", "c": ["Other AI/agent"]}


draft_name: draft-ecdh-psi
prompt_hash: d42b5b88f02de059
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:29.502635+00:00
response_json:
  {"name": "draft-ecdh-psi", "s": "Describes ECDH-based Private Set Intersection protocol using elliptic curves and hash-to-curve methods. Implements classical Meadows protocol with modern cryptographic primitives.", "n": 2, "nn": "Standard implementation of known PSI techniques, not particularly novel", "m": 3, "mn": "Reasonably detailed but lacks implementation guidance and security analysis depth", "o": 4, "on": "Significant overlap with existing PSI literature and protocols", "mo": 2, "mon": "Limited momentum, niche application area", "r": 3, "rn": "Moderate relevance for privacy-preserving applications", "c": ["Data formats/interop"]}


draft_name: draft-serafin-lake-ta-hint
prompt_hash: 7d31fbb10946b146
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:29.515348+00:00
response_json:
  {"name": "draft-serafin-lake-ta-hint", "s": "Defines trust anchor hint format for EDHOC protocol to help identify trusted third parties. Provides optimization for certificate chain discovery in constrained environments.", "n": 2, "nn": "Incremental improvement to existing EDHOC protocol, addressing practical deployment issue", "m": 3, "mn": "Early stage but builds on solid EDHOC foundation", "o": 2, "on": "Specific to EDHOC, minimal overlap with other trust anchor mechanisms", "mo": 3, "mon": "Moderate momentum tied to EDHOC adoption in IoT", "r": 3, "rn": "Relevant for IoT security but limited scope", "c": ["Agent identity/auth"]}


draft_name: draft-ietf-core-oscore-edhoc
prompt_hash: 8e1aabc70938369b
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:29.524757+00:00
response_json:
  {"name": "draft-ietf-core-oscore-edhoc", "s": "Specifies how to run EDHOC over CoAP to establish OSCORE security contexts for constrained environments. Includes optimizations to combine EDHOC execution with first OSCORE transaction.", "n": 3, "nn": "Good integration work with useful optimization for reducing round trips", "m": 4, "mn": "Well-developed specification with comprehensive coverage of integration scenarios", "o": 1, "on": "Unique integration of EDHOC with CoAP/OSCORE", "mo": 4, "mon": "Strong momentum in IoT security standardization", "r": 4, "rn": "High relevance for IoT and constrained device security", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-housley-lamps-private-key-attest-attr
prompt_hash: 0ec488456e1300a4
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:49.378780+00:00
response_json:
  {"name": "draft-housley-lamps-private-key-attest-attr", "s": "Defines an X.509 attribute that allows certificate subjects to make signed statements proving possession of private keys. Enables CAs to accept attestations instead of direct proof-of-possession during certificate enrollment.", "n": 3, "nn": "Moderate novelty in formalizing private key attestation as X.509 attribute", "m": 4, "mn": "Well-defined specification building on established X.509 and PKI standards", "o": 2, "on": "Some overlap with general PKI enrollment but specific attestation approach is distinct", "mo": 3, "mon": "Standard PKI evolution with practical deployment potential", "r": 3, "rn": "Relevant for PKI deployments but limited to certificate enrollment scenarios", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-ietf-anima-brski-ae
prompt_hash: ae9960374194d0de
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:49.401709+00:00
response_json:
  {"name": "draft-ietf-anima-brski-ae", "s": "Extends BRSKI protocol to support alternative certificate enrollment mechanisms beyond EST, particularly CMP. Enables more flexible automated device onboarding in enterprise networks.", "n": 3, "nn": "Incremental enhancement to existing BRSKI with alternative enrollment options", "m": 4, "mn": "Comprehensive specification with clear architecture updates to established protocol", "o": 3, "on": "Builds directly on BRSKI with some overlap to certificate enrollment protocols", "mo": 4, "mon": "IETF working group item with strong industry backing for device onboarding", "r": 4, "rn": "High relevance for automated network device deployment and IoT scenarios", "c": ["Autonomous netops", "Agent identity/auth", "Agent discovery/reg"]}


draft_name: draft-nourse-scep
prompt_hash: 4f26465c76d4f77b
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:49.414468+00:00
response_json:
  {"name": "draft-nourse-scep", "s": "Specifies the Simple Certificate Enrollment Protocol using PKCS#7 and PKCS#10 over HTTP. Formalizes the widely deployed protocol originally developed by VeriSign for Cisco.", "n": 2, "nn": "Standardizing existing widely-deployed protocol rather than new innovation", "m": 5, "mn": "Mature protocol with extensive real-world deployment and implementation experience", "o": 4, "on": "Significant overlap with other certificate enrollment protocols like EST and CMP", "mo": 3, "mon": "Steady momentum for standardizing de facto standard protocol", "r": 4, "rn": "High practical relevance due to widespread existing deployment", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-barnes-hpke-hpke
prompt_hash: 322a0d460696193a
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:49.425591+00:00
response_json:
  {"name": "draft-barnes-hpke-hpke", "s": "Defines hybrid public key encryption scheme supporting authenticated variants and flexible cryptographic primitives. Provides framework for secure encryption with multiple authentication modes.", "n": 4, "nn": "Novel hybrid approach combining multiple cryptographic primitives with authentication options", "m": 4, "mn": "Comprehensive specification with detailed instantiations using standard primitives", "o": 2, "on": "Unique hybrid approach with limited overlap to existing encryption schemes", "mo": 4, "mon": "CFRG product with strong cryptographic research backing", "r": 5, "rn": "Very high relevance as fundamental cryptographic building block for secure communications", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-ruas-cfrg-ecdp
prompt_hash: 6e397b7f53cfa901
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:03:49.448960+00:00
response_json:
  {"name": "draft-ruas-cfrg-ecdp", "s": "Specifies peer-to-peer communication protocol for decentralized networks using elliptic curve cryptography. Combines ECDSA authentication with ECDH key exchange over TCP/UDP.", "n": 2, "nn": "Combines existing ECC techniques without significant cryptographic innovation", "m": 2, "mn": "Basic specification with limited implementation details and security analysis", "o": 4, "on": "Heavy overlap with existing P2P protocols and standard ECC applications", "mo": 2, "mon": "Limited momentum as individual submission without working group support", "r": 3, "rn": "Moderate relevance for decentralized applications but competes with established solutions", "c": ["A2A protocols", "Agent identity/auth"]}


draft_name: draft-papon-lake-pq-edhoc
prompt_hash: c4575e12b3d459e3
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:09.076737+00:00
response_json:
  {"name": "draft-papon-lake-pq-edhoc", "s": "Extends EDHOC protocol with quantum-resistant features using post-quantum signatures and KEMs. Provides 3-message and 3-or-4-message variants balancing security and complexity.", "n": 4, "nn": "Novel hybrid approach combining signatures and KEMs for quantum resistance in EDHOC", "m": 3, "mn": "Solid technical approach but needs implementation validation and interoperability testing", "o": 2, "on": "Some overlap with other post-quantum EDHOC proposals but unique hybrid approach", "mo": 4, "mon": "Strong momentum given urgency of post-quantum cryptography adoption", "r": 5, "rn": "Highly relevant for securing IoT and constrained device communications against quantum threats", "c": ["Agent identity/auth", "Data formats/interop"]}


draft_name: draft-wu-sidr-aspa-validation-signaling
prompt_hash: 6ab2b805d7176ece
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:09.099194+00:00
response_json:
  {"name": "draft-wu-sidr-aspa-validation-signaling", "s": "Defines BGP extended community to carry AS_PATH validation state based on ASPA. Enables IBGP speakers to use validation state in local policy decisions.", "n": 3, "nn": "Incremental but useful extension to existing ASPA validation mechanisms", "m": 4, "mn": "Well-defined technical solution building on established BGP mechanisms", "o": 2, "on": "Complements existing ASPA work without significant overlap", "mo": 3, "mon": "Steady progress in BGP security space with industry interest", "r": 4, "rn": "Important for BGP security and route validation in internet infrastructure", "c": ["Policy/governance", "Data formats/interop"]}


draft_name: draft-amsuess-ace-brski-ace
prompt_hash: dbe11742b00b2c86
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:09.110072+00:00
response_json:
  {"name": "draft-amsuess-ace-brski-ace", "s": "Extends BRSKI voucher mechanism to provision ACE credentials for domain onboarding. Bridges PKI-managed and ACE-managed domain authentication.", "n": 4, "nn": "Creative integration of two different authentication frameworks", "m": 2, "mn": "Early stage work needing more detailed protocol specification and validation", "o": 1, "on": "Unique combination of BRSKI and ACE with minimal existing overlap", "mo": 2, "mon": "Limited adoption momentum, niche use case", "r": 3, "rn": "Relevant for IoT device onboarding but serves specific architectural scenarios", "c": ["Agent identity/auth", "Autonomous netops"]}


draft_name: draft-kim-nmrg-2nmai5g
prompt_hash: 2eba9032286dc889
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:09.119411+00:00
response_json:
  {"name": "draft-kim-nmrg-2nmai5g", "s": "Proposes AI-driven autonomous security and network management for B5G networks using SOAR principles. Focuses on maintaining constant security quality through intent-based management.", "n": 2, "nn": "Combines existing concepts (AI, SOAR, intent-based networking) without clear innovation", "m": 1, "mn": "Very early conceptual stage with unclear technical specifications and requirements", "o": 4, "on": "Significant overlap with existing AI-driven network management proposals", "mo": 2, "mon": "Limited technical detail suggests low implementation momentum", "r": 3, "rn": "Relevant topic area but lacks concrete technical contributions", "c": ["Autonomous netops", "AI safety/alignment", "ML traffic mgmt"]}


draft_name: draft-yang-alto-multi-domain
prompt_hash: 625aa83ea005e436
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:09.128134+00:00
response_json:
  {"name": "draft-yang-alto-multi-domain", "s": "Identifies use cases and challenges for ALTO in multi-domain environments where paths span multiple autonomous networks. Provides preliminary implementation approaches.", "n": 3, "nn": "Addresses known gap in ALTO multi-domain scenarios with structured analysis", "m": 3, "mn": "Good problem analysis and use cases but implementation solutions need more detail", "o": 2, "on": "Builds on ALTO foundation with some overlap but addresses specific multi-domain gap", "mo": 3, "mon": "ALTO has established community with interest in multi-domain extensions", "r": 4, "rn": "Highly relevant for CDN optimization and application-layer traffic engineering", "c": ["ML traffic mgmt", "Data formats/interop", "Policy/governance"]}


draft_name: draft-chen-nmrg-ibn-management
prompt_hash: 242e5c884e1f38d8
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:29.922730+00:00
response_json:
  {"name": "draft-chen-nmrg-ibn-management", "s": "Proposes an intent-based network management architecture with five levels of autonomous operation. Focuses on reducing complexity and achieving fully automated network management with humans out of the loop.", "n": 3, "nn": "Intent-based networking concepts exist but specific five-level autonomous management framework shows some novelty", "m": 2, "mn": "High-level architectural proposal lacking detailed technical specifications and implementation details", "o": 3, "on": "Moderate overlap with existing IBN and autonomous network management research", "mo": 2, "mon": "Individual submission with limited apparent industry backing or working group adoption", "r": 4, "rn": "Highly relevant to current trends in network automation and autonomous operations", "c": ["Autonomous netops", "Policy/governance"]}


draft_name: draft-ietf-idr-bgp-dpa
prompt_hash: 345765501e26917c
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:29.944033+00:00
response_json:
  {"name": "draft-ietf-idr-bgp-dpa", "s": "Introduces Destination Preference Attribute for BGP to enable autonomous systems to specify globally transitive metrics. Aims to facilitate symmetric routing and load sharing in multi-provider environments.", "n": 3, "nn": "New BGP attribute with specific application for return traffic preferences shows moderate novelty", "m": 3, "mn": "Well-defined technical specification but compact at only 4 pages, may need more detail", "o": 2, "on": "Some overlap with BGP traffic engineering but specific DPA approach is relatively unique", "mo": 4, "mon": "IETF working group draft with established BGP community interest", "r": 3, "rn": "Relevant to BGP operations but addresses specific use case rather than broad applicability", "c": ["A2A protocols"]}


draft_name: draft-ietf-idr-bgp-metrics
prompt_hash: 2026dd02b88e6b23
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:29.953285+00:00
response_json:
  {"name": "draft-ietf-idr-bgp-metrics", "s": "Proposes extended per-hop BGP metrics to enable common routing policies that currently require complex configurations. Supplements BGP-4 with globally significant metrics.", "n": 3, "nn": "Per-hop BGP metrics provide useful extension but build incrementally on existing BGP framework", "m": 3, "mn": "Technical specification appears solid but references very old BGP RFCs suggesting dated approach", "o": 2, "on": "Some overlap with BGP extensions but specific per-hop metrics approach is distinctive", "mo": 4, "mon": "IETF working group draft with established routing community backing", "r": 4, "rn": "Addresses practical operational needs for BGP policy implementation", "c": ["A2A protocols"]}


draft_name: draft-ietf-bgp-idrp-usage
prompt_hash: 0c2656add54d1a75
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:29.961274+00:00
response_json:
  {"name": "draft-ietf-bgp-idrp-usage", "s": "Describes usage guidelines for BGP and IDRP protocols in Internet inter-autonomous system routing. Serves as companion document defining practical application of these routing protocols.", "n": 2, "nn": "Usage document for existing protocols with limited novel technical contribution", "m": 3, "mn": "Comprehensive usage guide but references very dated protocol versions and email lists", "o": 4, "on": "Significant overlap with existing BGP deployment and usage documentation", "mo": 3, "mon": "IETF draft but appears to cover legacy protocols with questionable current relevance", "r": 2, "rn": "Limited relevance due to focus on dated protocol versions and deployment scenarios", "c": ["A2A protocols", "Policy/governance"]}


draft_name: draft-ietf-dtn-dtnma
prompt_hash: 6149bfb1ea57e0cc
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:29.971156+00:00
response_json:
  {"name": "draft-ietf-dtn-dtnma", "s": "Defines management architecture for Delay-Tolerant Networks operating in challenged environments with long delays and frequent disruptions. Emphasizes asynchronous transport and autonomous local control.", "n": 4, "nn": "Novel approach to network management for extremely challenging network conditions", "m": 4, "mn": "Comprehensive 59-page specification with detailed architecture and technical depth", "o": 1, "on": "Unique focus on DTN management with little overlap in existing network management approaches", "mo": 4, "mon": "IETF working group draft with specialized but dedicated community support", "r": 3, "rn": "Relevant for specialized environments like space networks but limited general applicability", "c": ["Autonomous netops", "A2A protocols"]}


draft_name: draft-mangrove-workgroup-mangrove
prompt_hash: 8738d7ddcc5a1703
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:49.166564+00:00
response_json:
  {"name": "draft-mangrove-workgroup-mangrove", "s": "Mangrove proposes a unified framework for Internet topology and routing modeling by aggregating diverse data sources like traceroute, BGP, and allocation records. It constructs hierarchical representations to compute packet paths and performance metrics across geographic regions, autonomous systems, and IP prefixes.", "n": 3, "nn": "Novel unified approach but builds on established topology mapping concepts", "m": 2, "mn": "Early stage with incomplete abstract and limited implementation details", "o": 3, "on": "Some overlap with existing topology discovery and routing analysis work", "mo": 2, "mon": "New workgroup with single draft, unclear community support", "r": 4, "rn": "High relevance for network operations and autonomous network management", "c": ["Autonomous netops", "Data formats/interop"]}


draft_name: draft-ietf-grow-va
prompt_hash: 3b802b99a1b3f363
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:49.184554+00:00
response_json:
  {"name": "draft-ietf-grow-va", "s": "Virtual Aggregation (VA) enables FIB suppression to reduce routing table size in routers by an order of magnitude. The approach can be deployed autonomously by ISPs without inter-ISP cooperation and coexists with legacy equipment.", "n": 4, "nn": "Novel approach to addressing DFRT growth through FIB suppression", "m": 4, "mn": "Well-developed 24-page document from established IETF working group", "o": 2, "on": "Some related work on routing table optimization but unique approach", "mo": 4, "mon": "IETF working group draft with strong institutional backing", "r": 5, "rn": "Directly addresses critical Internet scaling challenges", "c": ["Autonomous netops", "ML traffic mgmt"]}


draft_name: draft-cowles-aee
prompt_hash: 7cbd692c1f5b6954
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:49.201989+00:00
response_json:
  {"name": "draft-cowles-aee", "s": "Agent Envelope Exchange defines a minimal 14-field JSON envelope format for inter-agent communication. It separates message routing concerns from payload semantics to enable portable workflows across heterogeneous agent frameworks.", "n": 4, "nn": "Clean separation of envelope and payload is novel for agent communication", "m": 4, "mn": "Comprehensive 21-page specification with clear conformance levels", "o": 2, "on": "Some overlap with messaging standards but agent-specific focus is unique", "mo": 3, "mon": "Individual submission but addresses emerging need for agent interop", "r": 5, "rn": "Highly relevant as foundational infrastructure for agent ecosystems", "c": ["A2A protocols", "Data formats/interop", "Agent identity/auth"]}


draft_name: draft-mapmw-task-discovery
prompt_hash: 0e325263e6f8aa32
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:49.215527+00:00
response_json:
  {"name": "draft-mapmw-task-discovery", "s": "Defines an architectural framework for task-centric agent discovery where task owners publish structured task cards to platforms. Autonomous agents can discover tasks, negotiate terms, and coordinate multi-agent collaboration through defined functional layers.", "n": 4, "nn": "Task-centric approach is novel complement to agent-to-agent discovery", "m": 4, "mn": "Thorough 21-page architecture with survey of existing approaches", "o": 3, "on": "Acknowledges and builds on existing agent discovery mechanisms", "mo": 3, "mon": "Individual submission but addresses clear gap in current approaches", "r": 5, "rn": "Essential for enabling open agent marketplaces and collaboration", "c": ["Agent discovery/reg", "A2A protocols", "Human-agent interaction"]}


draft_name: draft-chu-oauth-as-attested-user-cert
prompt_hash: f6d6dd4a3be15c98
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:49.231459+00:00
response_json:
  {"name": "draft-chu-oauth-as-attested-user-cert", "s": "Extends OAuth 2.0 Rich Authorization Requests to allow clients like AI agents to request AS-attested user certificates in access tokens. This enables resource servers to verify delegation evidence signed by resource owners.", "n": 3, "nn": "Extends existing OAuth RAR framework for agent-specific use case", "m": 3, "mn": "Concise 9-page specification but could benefit from more examples", "o": 2, "on": "Builds on OAuth standards but agent certificate binding is unique", "mo": 2, "mon": "Individual submission with narrow scope may limit adoption", "r": 4, "rn": "Important for agent authentication and delegation scenarios", "c": ["Agent identity/auth", "A2A protocols"]}


draft_name: draft-ma-v6ops-pe-ipv6only-reqs
prompt_hash: 759c82f261939183
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:58.274217+00:00
response_json:
  {"name": "draft-ma-v6ops-pe-ipv6only-reqs", "s": "Defines requirements for Provider Edge devices in IPv6-only underlay networks supporting multi-domain operations. Focuses on service delivery and interoperability while providing IPv4-as-a-Service capabilities.", "n": 2, "nn": "Incremental work on IPv6-only deployment scenarios, building on existing PE requirements", "m": 3, "mn": "Requirements document with clear scope but needs implementation validation", "o": 3, "on": "Moderate overlap with existing IPv6 transition and PE requirements drafts", "mo": 3, "mon": "IPv6-only deployments gaining traction but not urgent priority", "r": 3, "rn": "Relevant for service providers transitioning to IPv6-only infrastructure", "c": ["Other AI/agent"]}


draft_name: draft-yang-dynamic-ecn-threshold
prompt_hash: 683502030928aba6
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:04:58.284311+00:00
response_json:
  {"name": "draft-yang-dynamic-ecn-threshold", "s": "Proposes coupling ECN marking thresholds with dynamic buffer allocation to prevent misalignment issues. Provides deterministic algorithm to maintain adaptive relationship between marking thresholds and buffer capacity.", "n": 4, "nn": "Novel approach to ECN threshold management addressing real operational problems", "m": 4, "mn": "Well-defined problem with concrete solution and reference algorithm", "o": 2, "on": "Limited overlap with existing ECN work, addresses specific dynamic buffer scenario", "mo": 3, "mon": "Addresses practical network operations issue but narrow deployment scope", "r": 4, "rn": "Highly relevant for modern network equipment with dynamic buffer management", "c": ["ML traffic mgmt"]}


draft_name: draft-allman-tcp-early-rexmt
prompt_hash: e54daafb95a3deed
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-allman-tcp-early-rexmt]
created_at: 2026-03-04T00:05:07.158881+00:00
response_json:
  [{"title": "Early Retransmit", "description": "Reduce the number of duplicate acknowledgments required to trigger fast retransmission when congestion window is small, enabling faster recovery without waiting for timeout.", "type": "mechanism"}]


draft_name: draft-allman-tcpx2-hack
prompt_hash: 9c40901e1f9f2ddd
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-allman-tcpx2-hack]
created_at: 2026-03-04T00:05:07.174734+00:00
response_json:
  [{"title": "TCP Header Size Extension", "description": "Increase TCP header space to accommodate values that previously could not fit without changing protocol semantics.", "type": "extension"}]


draft_name: draft-amsuess-ace-brski-ace
prompt_hash: e074db5ebcdf9f82
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-amsuess-ace-brski-ace]
created_at: 2026-03-04T00:05:07.180026+00:00
response_json:
  [{"title": "ACE Credential Provisioning via BRSKI", "description": "Extend the BRSKI voucher artifact to support onboarding devices into ACE-managed domains in addition to PKI-managed domains.", "type": "extension"}]


draft_name: draft-ar-emu-pqc-eapaka
prompt_hash: 5423f507c17269cd
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ar-emu-pqc-eapaka]
created_at: 2026-03-04T00:05:07.188059+00:00
response_json:
  [{"title": "Hybrid Post-Quantum Cryptography for EAP-AKA'", "description": "Replace traditional ECDHE ephemeral key exchange with post-quantum resistant algorithms to protect against future quantum computing threats in EAP-AKA' forward secrecy.", "type": "extension"}]


draft_name: draft-barnes-hpke-hpke
prompt_hash: 1d7f334b1cdbacdc
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-barnes-hpke-hpke]
created_at: 2026-03-04T00:05:12.589672+00:00
response_json:
  [{"title": "Hybrid Public Key Encryption Scheme", "description": "A modular encryption scheme combining asymmetric KEM, KDF, and AEAD primitives for encrypting arbitrary-sized plaintexts with recipient public keys.", "type": "protocol"}, {"title": "Authenticated HPKE Variants", "description": "Multiple authentication modes for HPKE including pre-shared key authentication and optional KEM private key authentication.", "type": "extension"}, {"title": "KEM-Agnostic Encryption Framework", "description": "A flexible encryption architecture that works with any combination of asymmetric KEM, KDF, and AEAD functions with selective KEM capability requirements.", "type": "architecture"}]


draft_name: draft-bernardos-anima-fog-monitoring
prompt_hash: 603745c1c2f1756d
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-bernardos-anima-fog-monitoring]
created_at: 2026-03-04T00:05:12.597570+00:00
response_json:
  [{"title": "Dynamic Service Function Chain Orchestration", "description": "Runtime orchestration mechanism for service function chains across heterogeneous, volatile, and mobile fog resources that adapt to topology changes.", "type": "mechanism"}, {"title": "Predictive Resource Anticipation", "description": "Autonomic fog orchestration that predicts and proactively handles resource changes ahead of time rather than only reacting to failures.", "type": "pattern"}]


draft_name: draft-chen-nmrg-ibn-management
prompt_hash: 29ba618551acd1d8
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-chen-nmrg-ibn-management]
created_at: 2026-03-04T00:05:12.603825+00:00
response_json:
  [{"title": "Intent-Based Network Management", "description": "Network management approach using high-level intent expressions to enable automated management with minimal human intervention.", "type": "pattern"}, {"title": "Autonomous Network Management Maturity Levels", "description": "A five-level framework defining progression from manual to fully autonomous network management.", "type": "architecture"}]


draft_name: draft-chen-oauth-scope-agent-extensions
prompt_hash: 8ffde470266991bd
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-chen-oauth-scope-agent-extensions]
created_at: 2026-03-04T00:05:12.610262+00:00
response_json:
  [{"title": "Structured OAuth Scope Syntax", "description": "Extension to OAuth 2.0 scope parameter introducing structured syntax for expressing fine-grained permissions.", "type": "extension"}, {"title": "Modular Capability Unit Authorization", "description": "Standardized mechanism for expressing and obtaining consent for complex permissions required by encapsulated, reusable functional modules within agent ecosystems.", "type": "mechanism"}]


draft_name: draft-chu-oauth-as-attested-user-cert
prompt_hash: 55f90db109df4193
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-chu-oauth-as-attested-user-cert]
created_at: 2026-03-04T00:05:12.616405+00:00
response_json:
  [{"title": "AS-Attested User Certificate in Access Tokens", "description": "Extension to OAuth 2.0 Rich Authorization Requests enabling Authorization Servers to bind Resource Owner public key certificates to access tokens.", "type": "extension"}, {"title": "Delegation Evidence Verification via Token-Bound Certificates", "description": "Mechanism enabling Resource Servers to verify application-layer delegation evidence (like Verifiable Credentials) using AS-attested keys bound to access tokens.", "type": "mechanism"}]


draft_name: draft-cowles-aee
prompt_hash: 6fe0d73d0bd808f4
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-cowles-aee]
created_at: 2026-03-04T00:05:18.245269+00:00
response_json:
  [{"title": "14-field JSON envelope format", "description": "A minimal JSON envelope with precisely 14 fields for message identity, typing, correlation, tracing, priority, and extensibility.", "type": "architecture"}, {"title": "Envelope-payload separation", "description": "Separates message routing and lifecycle management from domain-specific intent schemas to enable portability across agent frameworks.", "type": "pattern"}, {"title": "Entity identifier conventions", "description": "Standardized conventions for representing agent and service identities within the envelope format.", "type": "mechanism"}, {"title": "Conformance levels", "description": "Defines multiple conformance tiers to allow partial implementations while maintaining interoperability.", "type": "requirement"}]


draft_name: draft-daviel-html-geo-tag
prompt_hash: b60d0e99703da580
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-daviel-html-geo-tag]
created_at: 2026-03-04T00:05:18.263608+00:00
response_json:
  [{"title": "HTML META tag geolocation embedding", "description": "Embeds machine-readable latitude, longitude, and elevation within HTML META tags for automated geographic resource discovery.", "type": "extension"}, {"title": "Civic location META tags", "description": "Extends geographic tagging with civic address information in addition to coordinate-based location.", "type": "extension"}]


draft_name: draft-dunbar-agent-attachment
prompt_hash: 8afdc29323497ac8
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-dunbar-agent-attachment]
created_at: 2026-03-04T00:05:18.271168+00:00
response_json:
  [{"title": "Agent attachment protocol", "description": "Network-centric protocol enabling agents to securely attach to overlay infrastructure with identity validation and capability exchange.", "type": "protocol"}, {"title": "Edge node intermediation", "description": "Uses local edge nodes as attachment points to enable remote agent communication while hiding underlying network topology.", "type": "architecture"}]


draft_name: draft-ecdh-psi
prompt_hash: 9aaf169763786386
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ecdh-psi]
created_at: 2026-03-04T00:05:18.277659+00:00
response_json:
  [{"title": "ECDH-based private set intersection", "description": "Instantiates Meadows match-making protocol using standard elliptic curves and hash-to-curve methods for privacy-preserving set intersection.", "type": "mechanism"}, {"title": "Elliptic curve point masking", "description": "Masks data items encoded as elliptic curve points using private keys from both parties before intersection computation.", "type": "mechanism"}]


draft_name: draft-he-yi-srv6ops-ipv6-enhancemnet-in-cloud-uc
prompt_hash: 93a5cec51088c7fc
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-he-yi-srv6ops-ipv6-enhancemnet-in-cloud-uc]
created_at: 2026-03-04T00:05:18.283756+00:00
response_json:
  [{"title": "Virtual In-cloud Router agent", "description": "Proposes a virtual router agent deployed within cloud to bridge cloud and network with IPv6 and SRv6 capabilities.", "type": "architecture"}, {"title": "Service Function Chaining via SRv6 in cloud", "description": "Enables service function chaining based on Segment Routing for IPv6 in cloud environments.", "type": "mechanism"}, {"title": "IPv6 performance measurement in cloud", "description": "Establishes requirements for performance measurement capabilities based on IPv6 within cloud deployments.", "type": "requirement"}]


draft_name: draft-herbert-fast
prompt_hash: c9f141aa73b6d09f
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-herbert-fast]
created_at: 2026-03-04T00:05:23.955950+00:00
response_json:
  [{"title": "Ticket-based Network Access Control", "description": "Packets carry cryptographic tickets that grant traversal rights or specify service profiles to be applied by network nodes.", "type": "mechanism"}, {"title": "Unified Firewall and Service Signaling", "description": "Single ticket data structure encodes both firewall admission and service request semantics in a composable manner.", "type": "extension"}, {"title": "Host-Local Ticket Issuance Agent", "description": "Applications request and attach tickets from a local network agent rather than signaling firewall rules directly.", "type": "architecture"}]


draft_name: draft-housley-lamps-private-key-attest-attr
prompt_hash: 3ece2972a42f23f7
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-housley-lamps-private-key-attest-attr]
created_at: 2026-03-04T00:05:23.972117+00:00
response_json:
  [{"title": "Private Key Possession Attestation Attribute", "description": "X.509 certificate attribute allowing a certificate subject to provide a signed statement proving possession of their private key without direct CA interaction.", "type": "extension"}, {"title": "Cross-Certificate Key Proof Reuse", "description": "Certificate subject can prove possession of a new key using a previously-issued signature certificate, enabling efficient multi-key enrollment.", "type": "pattern"}]


draft_name: draft-hw-protocol-agent
prompt_hash: caf1cae94f7a65fc
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-hw-protocol-agent]
created_at: 2026-03-04T00:05:23.978296+00:00
response_json:
  [{"title": "Multi-modal Data Traffic Classification for AI Agents", "description": "Network protocols to identify and expose diverse transmission quality requirements (latency, bandwidth, reliability) for different data modalities within single AI agent flows.", "type": "requirement"}, {"title": "AI Agent Protocol Capabilities Framework", "description": "Specification of necessary network protocol features and capabilities to support heterogeneous quality-of-service demands from AI agent multi-modal data streams.", "type": "architecture"}]


draft_name: draft-hy-srv6ops-sfc-in-cloud-uc
prompt_hash: c731880a472e0748
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-hy-srv6ops-sfc-in-cloud-uc]
created_at: 2026-03-04T00:05:23.984556+00:00
response_json:
  [{"title": "Virtual Router SRv6 Proxy in Cloud", "description": "Deployment of virtual routers as SRv6-aware agents within cloud environments to enable Service Function Chaining when native cloud infrastructure lacks SRv6 support.", "type": "architecture"}]


draft_name: draft-ietf-anima-brski-ae
prompt_hash: aea1b5feaa7c6930
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-anima-brski-ae]
created_at: 2026-03-04T00:05:23.991103+00:00
response_json:
  [{"title": "Alternative Enrollment Protocols in BRSKI", "description": "Extends BRSKI to support certificate enrollment mechanisms beyond EST, such as CMP using authenticated self-contained signed objects.", "type": "extension"}, {"title": "Flexible Device Onboarding Framework", "description": "BRSKI-AE provides a pluggable architecture allowing different enrollment protocols to be selected based on network device onboarding constraints.", "type": "architecture"}]


draft_name: draft-ietf-core-oscore-edhoc
prompt_hash: a0223d8741180559
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-core-oscore-edhoc]
created_at: 2026-03-04T00:05:27.551878+00:00
response_json:
  [{"title": "EDHOC-OSCORE Integration", "description": "Combines EDHOC key exchange execution with the first OSCORE transaction to reduce round trips for establishing security context.", "type": "mechanism"}]


draft_name: draft-ietf-dnsop-dns-error-reporting
prompt_hash: 924ff6c5d2153964
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-dnsop-dns-error-reporting]
created_at: 2026-03-04T00:05:27.559011+00:00
response_json:
  [{"title": "QNAME-Encoded Error Reporting", "description": "Encodes DNS resolution errors in the QNAME itself to enable automatic error signaling from validating resolvers to operator-specified monitoring agents.", "type": "mechanism"}, {"title": "Extended DNS Error Feedback", "description": "Provides operators of authoritative servers with lightweight reports on DNS records that fail to resolve or validate based on extended DNS errors.", "type": "pattern"}]


draft_name: draft-ietf-dnsop-dnssec-bootstrapping
prompt_hash: 1a649fa17026a282
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-dnsop-dnssec-bootstrapping]
created_at: 2026-03-04T00:05:27.565564+00:00
response_json:
  [{"title": "In-Band Authenticated Zone Signals", "description": "Establishes an in-band method for DNS operators to publish authenticated information about their zones without out-of-band validation.", "type": "mechanism"}, {"title": "CDS/CDNSKEY Validation Without Secure Delegation", "description": "Allows parents to cryptographically validate child CDS/CDNSKEY records and provision DS records even when the child zone lacks existing secure delegation.", "type": "pattern"}]


draft_name: draft-ietf-dtn-bpv7-admin-iana
prompt_hash: 953ae8c8a20b1273
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-dtn-bpv7-admin-iana]
created_at: 2026-03-04T00:05:27.571640+00:00
response_json:
  [{"title": "Administrative Record Types Registry", "description": "Establishes an IANA registry for Bundle Protocol Version 7 Administrative Record types with reserved code points for private and experimental use.", "type": "extension"}]


draft_name: draft-ietf-dtn-dtnma
prompt_hash: acf3c72e68753e44
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ietf-dtn-dtnma]
created_at: 2026-03-04T00:05:32.147882+00:00
response_json:
  [{"title": "Asynchronous Management Protocol", "description": "A management architecture that operates over delay-tolerant bundle protocol without requiring synchronized transport behavior.", "type": "architecture"}, {"title": "Autonomous Local Control Model", "description": "Management approach enabling devices to make independent control decisions without central coordination in challenged networks.", "type": "pattern"}, {"title": "Lightweight Management Footprint", "description": "Management system designed with minimal resource and dependency requirements suitable for constrained devices.", "type": "requirement"}]


draft_name: draft-ietf-grow-va
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  [{"title": "Virtual Aggregation", "description": "FIB suppression mechanism that reduces FIB size by an order of magnitude by selectively not loading certain RIB entries without requiring inter-ISP coordination.", "type": "mechanism"}, {"title": "Autonomous FIB Suppression Deployment", "description": "Approach allowing ISPs to independently implement FIB reduction without cooperation from adjacent ISPs or legacy router upgrades.", "type": "pattern"}]


draft_name: draft-ietf-httpbis-zstd-window-size
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  [{"title": "Zstandard Window Size Requirement", "description": "Elevates window size limits for zstd compression in HTTP from a recommendation to a mandatory requirement to ensure interoperability.", "type": "requirement"}]


draft_name: draft-ietf-idr-bgp-dpa
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request_json: batch-ideas[draft-ietf-idr-bgp-dpa]
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response_json:
  [{"title": "Destination Preference Attribute", "description": "New BGP path attribute enabling single autonomous systems to specify globally transitive metrics for return traffic optimization.", "type": "extension"}, {"title": "Symmetric Routing Facilitation", "description": "BGP extension allowing upstream speakers to favor specific paths for return traffic to implement symmetric routing policies.", "type": "pattern"}]


draft_name: draft-ietf-idr-bgp-metrics
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request_json: batch-ideas[draft-ietf-idr-bgp-metrics]
created_at: 2026-03-04T00:05:32.184169+00:00
response_json:
  [{"title": "Per-Hop Extended BGP Metrics", "description": "Extension adding per-hop metrics to BGP to enable implementation of common routing policies without ad-hoc configuration.", "type": "extension"}]


draft_name: draft-ietf-lake-edhoc
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request_json: batch-ideas[draft-ietf-lake-edhoc]
created_at: 2026-03-04T00:05:36.196612+00:00
response_json:
  [{"title": "Compact authenticated DH key exchange", "description": "A lightweight Ephemeral Diffie-Hellman protocol optimized for constrained environments using COSE, CBOR, and CoAP to minimize code size.", "type": "protocol"}, {"title": "Identity protection in key exchange", "description": "Mechanism to hide peer identities during the Diffie-Hellman key exchange process.", "type": "mechanism"}, {"title": "OSCORE context establishment", "description": "Using EDHOC as a bootstrapping mechanism to establish Object Security for Constrained RESTful Environments (OSCORE) security contexts.", "type": "extension"}]


draft_name: draft-ietf-sip-location-conveyance
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  [{"title": "Geographic location conveyance in SIP", "description": "SIP extension mechanism to carry geographic location information between SIP entities in signaling messages.", "type": "extension"}, {"title": "Location-based SIP routing", "description": "Pattern enabling SIP servers to make routing decisions for user agent clients based on their geographic location.", "type": "pattern"}]


draft_name: draft-ietf-vwrap-authentication
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  [{"title": "Application-layer identity association", "description": "Authentication mechanism establishing a trust relationship between a client application and a remote identity management service.", "type": "mechanism"}, {"title": "Capability-based asset control", "description": "Requirement that authentication must grant capabilities sufficient to assert control over user agents and digital assets.", "type": "requirement"}]


draft_name: draft-ietf-websec-mime-sniff
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response_json:
  [{"title": "Content-Type sniffing algorithm", "description": "Algorithm to determine effective media type of HTTP responses by examining both Content-Type headers and response content while balancing security and compatibility.", "type": "mechanism"}]


draft_name: draft-illyes-rep-purpose
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response_json:
  [{"title": "Purpose-based robot targeting rule", "description": "Extend robots.txt with a new rule that targets automatic clients based on their declared purpose for accessing a service, complementing existing user-agent prefix and wildcard matching.", "type": "extension"}]


draft_name: draft-ingles-eap-edhoc
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draft_name: draft-jaju-httpbis-zstd-window-size
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draft_name: draft-klrc-aiagent-auth
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request_json: batch-ideas[draft-klrc-aiagent-auth]
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draft_name: draft-kwiatkowski-tls-ecdhe-mlkem
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model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-kwiatkowski-tls-ecdhe-mlkem]
created_at: 2026-03-04T00:05:44.459180+00:00
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  [{"title": "Hybrid ECDHE-MLKEM Key Agreement", "description": "Combines elliptic curve Diffie-Hellman with post-quantum ML-KEM in three variants for TLS 1.3 to provide transitional security against future quantum threats.", "type": "mechanism"}]


draft_name: draft-liu-agent-protocol-over-moq
prompt_hash: dc9432f7fc0de9e2
model: claude-haiku-4-5-20251001
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draft_name: draft-lkspa-wimse-verifiable-geo-fence
prompt_hash: 61fd3ec3a90f8b1a
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-lkspa-wimse-verifiable-geo-fence]
created_at: 2026-03-04T00:05:44.482902+00:00
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  [{"title": "Hardware-rooted Geofencing Verification", "description": "Replaces IP-based location verification with cryptographic proofs rooted in hardware to prevent spoofing via VPNs or proxies.", "type": "mechanism"}, {"title": "High-Assurance Profile for Sovereign Workloads", "description": "Defines a security profile for workload identities that provides cryptographically verifiable residency proofs for sovereign computing environments.", "type": "pattern"}, {"title": "Workload Identity Agent Architecture", "description": "Specifies a host-based agent for managing workload identities with hardware-backed attestation capabilities.", "type": "architecture"}]


draft_name: draft-lopez-lake-edhoc-psk
prompt_hash: 12495cae8ccceef9
model: claude-haiku-4-5-20251001
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  [{"title": "EDHOC PSK Authentication Method", "description": "Defines Pre-Shared Key authentication procedures and message flows for the EDHOC key exchange protocol.", "type": "mechanism"}]


draft_name: draft-ma-v6ops-pe-ipv6only-reqs
prompt_hash: 96d6ff551e33bcff
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-ma-v6ops-pe-ipv6only-reqs]
created_at: 2026-03-04T00:05:44.495574+00:00
response_json:
  [{"title": "IPv6-only Provider Edge Requirements", "description": "Specifies functional, protocol, and operational requirements for PE devices in IPv6-exclusive underlay networks supporting IPv4-as-a-Service.", "type": "requirement"}]


draft_name: draft-mandel-lamps-rfc5274bis
prompt_hash: 28d5405a8879ee83
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-mandel-lamps-rfc5274bis]
created_at: 2026-03-04T00:05:48.796238+00:00
response_json:
  [{"title": "CMC Compliance Requirements", "description": "Establishes a set of compliance statements and requirements for the CMC enrollment protocol to enable interoperable implementations.", "type": "requirement"}]


draft_name: draft-mangrove-workgroup-mangrove
prompt_hash: 1a93e6ceeba8c36f
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-mangrove-workgroup-mangrove]
created_at: 2026-03-04T00:05:48.812447+00:00
response_json:
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draft_name: draft-mapmw-task-discovery
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draft_name: draft-miller-ssh-agent
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draft_name: draft-moonesamy-rfc2369bis
prompt_hash: c0e2e69dc1bd0629
model: claude-haiku-4-5-20251001
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created_at: 2026-03-04T00:05:48.829006+00:00
response_json:
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draft_name: draft-moonesamy-rfc2919bis
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created_at: 2026-03-04T00:05:53.299609+00:00
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draft_name: draft-nandakumar-ai-agent-moq-transport
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created_at: 2026-03-04T00:05:53.316551+00:00
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draft_name: draft-nourse-scep
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created_at: 2026-03-04T00:05:53.322208+00:00
response_json:
  [{"title": "Simple Certificate Enrollment Protocol", "description": "A PKI communication protocol using PKCS#7 and PKCS#10 over HTTP for certificate enrollment.", "type": "protocol"}]


draft_name: draft-ounsworth-lamps-cms-dhkem
prompt_hash: 600bc097f6e57aaa
model: claude-haiku-4-5-20251001
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response_json:
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draft_name: draft-papon-lake-pq-edhoc
prompt_hash: d16278d436071ede
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created_at: 2026-03-04T00:05:53.333440+00:00
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draft_name: draft-patwhite-aauth
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created_at: 2026-03-04T00:05:58.257189+00:00
response_json:
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draft_name: draft-peterson-mimi-idprover
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request_json: batch-ideas[draft-peterson-mimi-idprover]
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response_json:
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draft_name: draft-rosenberg-ai-protocols
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created_at: 2026-03-04T00:05:58.280241+00:00
response_json:
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draft_name: draft-ruas-cfrg-ecdp
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draft_name: draft-serafin-lake-ta-hint
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created_at: 2026-03-04T00:05:58.292693+00:00
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draft_name: draft-song-lake-ra
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request_json: batch-ideas[draft-song-lake-ra]
created_at: 2026-03-04T00:06:02.358056+00:00
response_json:
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draft_name: draft-tiloca-lake-app-profiles
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created_at: 2026-03-04T00:06:02.375795+00:00
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draft_name: draft-tschofenig-lamps-nonce-cmp-est
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created_at: 2026-03-04T00:06:02.386456+00:00
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draft_name: draft-vattaparambil-positioning-of-poa
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created_at: 2026-03-04T00:06:02.393667+00:00
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draft_name: draft-vesely-dmarc-mlm-transform
prompt_hash: a5971afa2eda39dc
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created_at: 2026-03-04T00:06:08.605990+00:00
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draft_name: draft-wang-lisp-ai-agent
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draft_name: draft-wu-sidr-aspa-validation-signaling
prompt_hash: 5c07e97ae24d4275
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created_at: 2026-03-04T00:06:08.628153+00:00
response_json:
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draft_name: draft-yan-anima-brski-cle
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created_at: 2026-03-04T00:06:08.633578+00:00
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draft_name: draft-yang-alto-multi-domain
prompt_hash: 062c8efb1dc55cbd
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-yang-alto-multi-domain]
created_at: 2026-03-04T00:06:08.638948+00:00
response_json:
  [{"title": "Multi-Domain ALTO Service Discovery", "description": "Mechanisms for ALTO clients to discover and query network information across autonomous network boundaries in multi-domain paths.", "type": "requirement"}, {"title": "Cross-Domain Network Information Aggregation", "description": "Protocol extensions enabling ALTO servers to aggregate and expose network metrics across multiple independent administrative domains.", "type": "architecture"}, {"title": "Multi-Domain Trust and Privacy Handling", "description": "Framework for ALTO to handle sensitive network information disclosure across trust domain boundaries while preserving operator confidentiality.", "type": "pattern"}]


draft_name: draft-yang-dynamic-ecn-threshold
prompt_hash: 3a54e7bc69bfbb5a
model: claude-haiku-4-5-20251001
request_json: batch-ideas[draft-yang-dynamic-ecn-threshold]
created_at: 2026-03-04T00:06:10.341194+00:00
response_json:
  [{"title": "Dynamic ECN Threshold Coupling", "description": "Dynamically adjust ECN marking thresholds in real-time based on instantaneous buffer pool capacity rather than static configuration.", "type": "mechanism"}, {"title": "Buffer-Aware ECN Marking", "description": "Align ECN thresholds with actual dynamic buffer allocation to prevent premature or late congestion marking.", "type": "extension"}, {"title": "Reference Algorithm for Dynamic ECN", "description": "Deterministic algorithm provided to compute ECN thresholds as a function of active queue count and shared buffer availability.", "type": "mechanism"}]


draft_name: _landscape_
prompt_hash: bace21e8e514fcb4
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:06:32.785508+00:00
request_json:
  You are analyzing the landscape of 433 IETF Internet-Drafts related to AI agents and autonomous systems.

  ## Categories and Draft Counts
  - Data formats/interop: 174 drafts
  - A2A protocols: 155 drafts
  - Agent identity/auth: 152 drafts
  - Autonomous netops: 114 drafts
  - Policy/governance: 108 drafts
  - Agent discovery/reg: 89 drafts
  - ML traffic mgmt: 79 drafts
  - AI safety/alignment: 47 drafts
  - Model serving/inference: 42 drafts
  - Other AI/agent: 34 drafts
  - Human-agent interaction: 33 drafts

  ## Most Common Technical Ideas
  - Quantum-Safe Session Key Generation (3 drafts)
  - ACaaS for Dynamic Edge AI Placement (3 drafts)
  - TE Topology Model for Edge Network Performance (3 drafts)
  - Service Objective Mapping to YANG Models (3 drafts)
  - Real-Time AI Inference Network Requirements (3 drafts)
  - AC and TE YANG Model Gap Identification (3 drafts)
  - Agent Gateway (3 drafts)
  - AI Agent Communication Framework (3 drafts)
  - Tool Enumeration API (3 drafts)
  - Tool Invocation API (3 drafts)
  - Agent Certificate Authority (3 drafts)
  - Agent Registration Server (3 drafts)
  - Agent Resource Type (2 drafts)
  - Agentic Application Resource Type (2 drafts)
  - Cross-Domain Agent Identity Management (2 drafts)
  - AI Workload Characterization (2 drafts)
  - Inter-DC AI Traffic Problem Statement (2 drafts)
  - Federated AI Service Architecture (2 drafts)
  - Private Data Training Network Requirements (2 drafts)
  - Semantic Routing (2 drafts)

  ## Known Overlap Clusters (groups of highly similar drafts)
  Multiple clusters of near-duplicate drafts exist, particularly in:
  - Data formats/interop (174 drafts, high internal overlap)
  - A2A protocols (155 drafts, high internal overlap)
  - Agent identity/auth (152 drafts, high internal overlap)
  - Autonomous netops (114 drafts, high internal overlap)
  - Policy/governance (108 drafts, high internal overlap)


  Identify 8-15 GAPS — areas, problems, or technical challenges NOT adequately addressed by existing drafts.

  Return a JSON array:
  [{"topic":"short topic name","description":"2-3 sentence description","category":"closest category or new","severity":"critical|high|medium|low","evidence":"what suggests this gap matters"}]

  Focus on:
  1. Problems mentioned but not solved
  2. Missing infrastructure pieces
  3. Security/privacy/safety issues not addressed
  4. Interoperability gaps between competing proposals
  5. Real-world deployment concerns ignored

  JSON array only, no fences.

response_json:
  [
    {
      "topic": "Multi-Agent Consensus Protocols",
      "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.",
      "category": "A2A protocols",
      "severity": "high",
      "evidence": "High overlap in A2A protocols suggests fragmented approaches without consensus mechanisms, critical for autonomous system coordination"
    },
    {
      "topic": "Agent Behavioral Verification",
      "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.",
      "category": "AI safety/alignment",
      "severity": "critical",
      "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",
      "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.",
      "category": "Agent discovery/reg",
      "severity": "medium",
      "evidence": "Agent registration servers exist but no migration protocols, essential for dynamic edge deployments mentioned in technical ideas"
    },
    {
      "topic": "Real-Time Agent Rollback Mechanisms",
      "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.",
      "category": "Autonomous netops",
      "severity": "high",
      "evidence": "114 autonomous netops drafts but no rollback mechanisms, critical for production network safety"
    },
    {
      "topic": "Agent Resource Accounting and Billing",
      "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.",
      "category": "new",
      "severity": "medium",
      "evidence": "Edge AI placement and resource types defined but no economic models for multi-tenant agent deployments"
    },
    {
      "topic": "Federated Agent Learning Privacy",
      "description": "While federated architectures exist, there's insufficient specification for privacy-preserving agent learning that prevents data leakage between federated participants during model updates.",
      "category": "Policy/governance",
      "severity": "high",
      "evidence": "Federated AI architectures mentioned but private data training requirements suggest unresolved privacy challenges"
    },
    {
      "topic": "Agent Capability Negotiation",
      "description": "Agents need standardized protocols to negotiate their capabilities and limitations before collaboration. Tool enumeration exists but not dynamic capability negotiation during runtime.",
      "category": "A2A protocols",
      "severity": "medium",
      "evidence": "Tool enumeration APIs exist but no dynamic capability negotiation, needed for heterogeneous agent coordination"
    },
    {
      "topic": "Cross-Domain Agent Audit Trails",
      "description": "When agents operate across multiple domains or organizations, there's no standardized format for maintaining audit trails that satisfy different regulatory requirements simultaneously.",
      "category": "Agent identity/auth",
      "severity": "high",
      "evidence": "Cross-domain identity management exists but audit requirements vary by jurisdiction, creating compliance gaps"
    },
    {
      "topic": "Agent Failure Cascade Prevention",
      "description": "No protocols exist to prevent agent failures from cascading across interconnected autonomous systems. This is critical as agent interdependencies increase in production deployments.",
      "category": "AI safety/alignment",
      "severity": "critical",
      "evidence": "Limited AI safety focus (47 drafts) despite high interconnectivity implied by A2A protocols and autonomous netops"
    },
    {
      "topic": "Human Override Standardization",
      "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.",
      "category": "Human-agent interaction",
      "severity": "high",
      "evidence": "Only 33 human-agent interaction drafts suggest limited focus on human control mechanisms essential for safe deployment"
    },
    {
      "topic": "Agent Performance Benchmarking",
      "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.",
      "category": "new",
      "severity": "medium",
      "evidence": "AI workload characterization mentioned but no performance comparison frameworks, needed for agent selection and optimization"
    }
  ]


draft_name: w3c-web-user-agents
prompt_hash: c599fbdaec65a2f7
model: claude-sonnet-4-20250514
request_json: batch[0]
created_at: 2026-03-04T00:09:11.753895+00:00
response_json:
  {"name": "w3c-web-user-agents", "s": "Establishes duties and responsibilities of web user agents (browsers and intermediaries) toward their users. Proposes that user agents should prioritize user interests over other constituencies through collectively-discussed standards.", "n": 2, "nn": "Basic concept of user agent duties exists, but formal standardization approach is somewhat novel", "m": 1, "mn": "Very early stage with minimal detail on implementation or specific requirements", "o": 3, "on": "Moderate overlap with existing W3C browser standards and user agent specifications", "mo": 2, "mon": "Uncertain momentum without clear working group or implementation commitments", "r": 3, "rn": "Relevant to web standards but limited direct connection to AI/agent networking focus", "c": ["Policy/governance", "Human-agent interaction"]}


draft_name: _pipeline_behavior verification_
prompt_hash: ea86b26174160e0b
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:15:11.386101+00:00
request_json:
  You are writing an IETF Internet-Draft to address a gap in the AI/agent standardization landscape.

  ## Gap to Address
  Topic: behavior verification
  Description: behavior verification
  Category: 
  Evidence: 
  Severity: medium

  ## Convergent Ideas from Existing Drafts
  These ideas from the current landscape converge on this topic — build on them, don't duplicate:
  - [mechanism] Behavioral Trustworthiness Assessment: Authorization component that evaluates an agent's behavioral patterns and trustworthiness as input to access decisions. Goes beyond identity verification to consider agent behavior history. (from draft-chen-agent-decoupled-authorization-model)
  - [protocol] AIPREF Vocabulary Protocol Alignment: Establish protocol bindings that align real-time communication behavior with standardized AIPREF vocabulary for AI requirements. (from draft-altanai-aipref-realtime-protocol-bindings)
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): An advanced attestation system using multiple Trusted Execution Environment vendors for high-assurance scenarios. Provides redundant hardware-based verification of AI agent integrity. (from draft-aylward-aiga-1)
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): A high-assurance verification system using multiple Trusted Execution Environment vendors to attest to AI agent compliance. This redundant approach prevents single-vendor compromise scenarios. (from draft-aylward-aiga-2)
  - [mechanism] Cryptographic Identity Verification: Hardware-backed cryptographic system for verifying the identity of autonomous AI agents. Uses digital signatures and certificates to establish trusted agent identities. (from draft-aylward-daap-v2)
  - [protocol] Post-Discovery Authorization Handshake: A runtime authorization layer that governs agent behavior after discovery through ARDP but before permitting tool execution or authority delegation. Provides fine-grained control between discovery and execution phases. (from draft-barney-caam)
  - [extension] JOSE DVS Extension: Extension to the JOSE framework that adds derived verification signature capabilities. Provides alternative authentication method beyond traditional pre-shared keys. (from draft-bastian-jose-dvs)
  - [protocol] Public Key Derived HMAC: HMAC-based message authentication where the HMAC key is derived from public key information rather than using pre-shared symmetric keys. Allows verification using only public information from the JWS. (from draft-bastian-jose-pkdh)
  - [pattern] Five Enforcement Pillars with Typed Schemas: A structured set of security enforcement categories with formally defined data schemas that provide comprehensive coverage of AI agent security concerns. Each pillar represents a specific aspect of agent behavior that must be monitored and controlled. (from draft-berlinai-vera)
  - [mechanism] Cryptographic Proof-Based Autonomy: A verification mechanism that uses cryptographic proofs to validate agent compliance with security policies before granting operational privileges. This replaces traditional time-based or reputation-based trust models. (from draft-berlinai-vera)
  - [requirement] Verifiable Agent Behavior Attestation: A mechanism to demonstrate that recorded agent behavior truthfully represents actual behavior through cryptographic verification. This supports accountability, compliance, and human oversight of autonomous agent actions. (from draft-birkholz-verifiable-agent-conversations)
  - [mechanism] Agent Reasoning Trace Capture: Structured capture of agent reasoning processes within conversation records, allowing for verification and audit of decision-making processes. This provides transparency into how agents arrive at their conclusions and actions. (from draft-birkholz-verifiable-agent-conversations)
  - [requirement] Congestion Control Behavior Definitions: Vendor-neutral definitions for congestion control mechanisms and their measurable behaviors in AI fabric environments. (from draft-calabria-bmwg-ai-fabric-terminology)
  - [requirement] Dynamic Behavior Authentication: Authentication framework that manages and verifies dynamic behavior patterns rather than static identity credentials. Addresses the challenge of AI agents whose behavior changes over time. (from draft-chen-ai-agent-auth-new-requirements)
  - [pattern] Continuous Trustworthiness Verification: Shift from one-time 'Who are you?' authentication to ongoing 'Are you still trustworthy?' verification. Addresses autonomous agents that operate independently over extended periods. (from draft-chen-ai-agent-auth-new-requirements)
  - [mechanism] Delegation Evidence Verification via Token-Bound Certificates: Mechanism enabling Resource Servers to verify application-layer delegation evidence (like Verifiable Credentials) using AS-attested keys bound to access tokens. (from draft-chu-oauth-as-attested-user-cert)
  - [mechanism] Public Registry Enrollment Mode: Federated trust ecosystem enrollment mode using public registries for trust anchor discovery. Enables cross-organizational device verification and trust establishment. (from draft-condrey-rats-witnessd-enrollment)
  - [requirement] Real-Time Task Adaptability: A requirement for agent protocols to support dynamic adjustment of task execution based on changing conditions. Enables agents to modify their behavior without manual intervention. (from draft-cui-ai-agent-task)
  - [mechanism] DNS TXT Records for Agent Identity and Keys: Agent identity information and cryptographic keys are published and distributed using DNS TXT records. This enables cryptographic verification of agent authenticity through DNS infrastructure. (from draft-cui-dns-native-agent-naming-resolution)
  - [mechanism] TPM Hardware Attestation Headers: Email headers containing cryptographic evidence from Trusted Platform Module (TPM) chips that allow receivers to verify the sender's hardware authenticity. The verification chain runs directly from email header to TPM manufacturer's root Certificate Authority. (from draft-drake-email-tpm-attestation)

  ## RFC Foundations
  Most-referenced RFCs in this space — cite where relevant:
  - RFC 2119 (cited by 285 drafts)
  - RFC 8174 (cited by 237 drafts)
  - RFC 8446 (cited by 42 drafts)
  - RFC 6749 (cited by 36 drafts)
  - RFC 9110 (cited by 34 drafts)
  - RFC 8126 (cited by 26 drafts)
  - RFC 8259 (cited by 26 drafts)
  - RFC 5280 (cited by 22 drafts)
  - RFC 7519 (cited by 22 drafts)
  - RFC 9052 (cited by 20 drafts)

  ## Similar Existing Drafts
  These drafts are closest to this gap — differentiate from them:
  - draft-aylward-daap-v2: Distributed AI Accountability Protocol (DAAP) Version 2.0 (similarity: 0.60)
  - draft-messous-eat-ai: Entity Attestation Token (EAT) Profile for Autonomous AI Agents (similarity: 0.59)
  - draft-liu-agent-operation-authorization: Agent Operation Authorization (similarity: 0.58)
  - draft-gaikwad-south-authorization: SOUTH: Stochastic Authorization for Agent and Service Requests (similarity: 0.58)
  - draft-birkholz-verifiable-agent-conversations: Verifiable Agent Conversation Records (similarity: 0.57)
  - draft-jiang-seat-dynamic-attestation: Dynamic Attestation for AI Agent Communication (similarity: 0.56)
  - draft-romanchuk-normative-admissibility: Normative Admissibility Framework for Agent Speech Acts (similarity: 0.56)
  - draft-aap-oauth-profile: Agent Authorization Profile (AAP) for OAuth 2.0 (similarity: 0.55)

  ## Top-Rated Drafts in Category
  Drafts the community considers strong in this area:
  - draft-cowles-volt: Verifiable Operations Ledger and Trace (VOLT) Protocol (score: 4.8)
  - draft-aylward-daap-v2: Distributed AI Accountability Protocol (DAAP) Version 2.0 (score: 4.8)
  - draft-guy-bary-stamp-protocol: Secure Task-bound Agent Message Proof (STAMP) Protocol (score: 4.6)
  - draft-drake-email-tpm-attestation: Hardware Attestation for Email Sender Verification (score: 4.6)
  - draft-williams-netmod-lm-hierarchy-topology: Hierarchical Topology for Language Model Coordination (score: 4.6)

  ## Working Group Context
  434 drafts, 52 WG-adopted. Top WGs: lake (7), lamps (6), anima (3), dnsop (3), httpbis (3)

  ## Ecosystem Vision
  (Elevator Version)

  **Problem**: 260+ IETF drafts on AI agents; no single model for orchestration (DAG), human oversight (HITL), or for running the same system in both fast and regulated environments.

  **Proposal**: Five coordinated drafts — (A) shared architecture and terminology, (B) DAG execution with checkpoints and rollback, (C) HITL primitives (approval, override, explainability), (D) protocol-agnostic binding so any A2A protocol can participate, (E) assurance profiles so the same stack works in K8s or in a proof-heavy regulated regime.

  **Outcome**: One holistic agent ecosystem: DAG + HITL built in, agnostic and extensible, applicable everywhere from relaxed to fully proven.

  ## Sibling Drafts (same family)
  (none — this is the first draft in the family)

  Generate a detailed outline for an Internet-Draft that fills this gap.
  Return JSON:
  {
    "title": "full draft title",
    "abstract": "150-250 word abstract",
    "sections": [
      {"title": "section title", "summary": "2-3 sentence summary of content", "key_ideas": ["idea titles to incorporate"]}
    ],
    "normative_refs": ["RFC NNNN", "draft-name"],
    "informative_refs": ["RFC NNNN", "draft-name"],
    "terminology": {"term": "definition"},
    "target_wg": "suggested IETF working group",
    "intended_status": "informational|standards-track|experimental"
  }

  Requirements:
  - Include standard sections: Introduction, Terminology, Problem Statement, then 2-4 technical sections, Security Considerations, IANA Considerations
  - Reference specific RFCs and drafts from the context above
  - Use terminology consistent with sibling drafts if any
  - Abstract should clearly state the problem, approach, and contribution
  JSON only, no fences.

response_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"
  }


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s0_
prompt_hash: 27d2684b5d77438b
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:15:25.763675+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 1: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Behavioral Trustworthiness Assessment: Authorization component that evaluates an agent's behavioral patterns and trustworthiness as input to access decisions. Goes beyond identity verification to consider agent behavior history. (from draft-chen-agent-decoupled-authorization-model)
  - [requirement] Dynamic Behavior Authentication: Authentication framework that manages and verifies dynamic behavior patterns rather than static identity credentials. Addresses the challenge of AI agents whose behavior changes over time. (from draft-chen-ai-agent-auth-new-requirements)
  - [pattern] Continuous Trustworthiness Verification: Shift from one-time 'Who are you?' authentication to ongoing 'Are you still trustworthy?' verification. Addresses autonomous agents that operate independently over extended periods. (from draft-chen-ai-agent-auth-new-requirements)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_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.

  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s1_
prompt_hash: 6b762a4e6a0a4313
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:15:38.359882+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 2: Terminology
  Summary: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.

  ## Relevant Ideas to Incorporate
  - [mechanism] Cryptographic Proof-Based Autonomy: A verification mechanism that uses cryptographic proofs to validate agent compliance with security policies before granting operational privileges. This replaces traditional time-based or reputation-based trust models. (from draft-berlinai-vera)
  - [requirement] Verifiable Agent Behavior Attestation: A mechanism to demonstrate that recorded agent behavior truthfully represents actual behavior through cryptographic verification. This supports accountability, compliance, and human oversight of autonomous agent actions. (from draft-birkholz-verifiable-agent-conversations)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s2_
prompt_hash: 88fa496c6e538f53
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:15:56.174404+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 3: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): An advanced attestation system using multiple Trusted Execution Environment vendors for high-assurance scenarios. Provides redundant hardware-based verification of AI agent integrity. (from draft-aylward-aiga-1)
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): A high-assurance verification system using multiple Trusted Execution Environment vendors to attest to AI agent compliance. This redundant approach prevents single-vendor compromise scenarios. (from draft-aylward-aiga-2)
  - [mechanism] Agent Reasoning Trace Capture: Structured capture of agent reasoning processes within conversation records, allowing for verification and audit of decision-making processes. This provides transparency into how agents arrive at their conclusions and actions. (from draft-birkholz-verifiable-agent-conversations)
  - [requirement] Real-Time Task Adaptability: A requirement for agent protocols to support dynamic adjustment of task execution based on changing conditions. Enables agents to modify their behavior without manual intervention. (from draft-cui-ai-agent-task)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s3_
prompt_hash: db4b6e26910742e8
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:16:14.613439+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 4: 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.

  ## Relevant Ideas to Incorporate
  - [pattern] Five Enforcement Pillars with Typed Schemas: A structured set of security enforcement categories with formally defined data schemas that provide comprehensive coverage of AI agent security concerns. Each pillar represents a specific aspect of agent behavior that must be monitored and controlled. (from draft-berlinai-vera)
  - [mechanism] Public Registry Enrollment Mode: Federated trust ecosystem enrollment mode using public registries for trust anchor discovery. Enables cross-organizational device verification and trust establishment. (from draft-condrey-rats-witnessd-enrollment)
  - [mechanism] DNS TXT Records for Agent Identity and Keys: Agent identity information and cryptographic keys are published and distributed using DNS TXT records. This enables cryptographic verification of agent authenticity through DNS infrastructure. (from draft-cui-dns-native-agent-naming-resolution)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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].


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s4_
prompt_hash: e84fc4f8feecb18f
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:16:31.763896+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 5: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Cryptographic Identity Verification: Hardware-backed cryptographic system for verifying the identity of autonomous AI agents. Uses digital signatures and certificates to establish trusted agent identities. (from draft-aylward-daap-v2)
  - [mechanism] Agent Reasoning Trace Capture: Structured capture of agent reasoning processes within conversation records, allowing for verification and audit of decision-making processes. This provides transparency into how agents arrive at their conclusions and actions. (from draft-birkholz-verifiable-agent-conversations)
  - [mechanism] TPM Hardware Attestation Headers: Email headers containing cryptographic evidence from Trusted Platform Module (TPM) chips that allow receivers to verify the sender's hardware authenticity. The verification chain runs directly from email header to TPM manufacturer's root Certificate Authority. (from draft-drake-email-tpm-attestation)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s5_
prompt_hash: 27bd3c8f92bfe819
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:16:49.253126+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 6: 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.

  ## Relevant Ideas to Incorporate
  - [protocol] AIPREF Vocabulary Protocol Alignment: Establish protocol bindings that align real-time communication behavior with standardized AIPREF vocabulary for AI requirements. (from draft-altanai-aipref-realtime-protocol-bindings)
  - [protocol] Post-Discovery Authorization Handshake: A runtime authorization layer that governs agent behavior after discovery through ARDP but before permitting tool execution or authority delegation. Provides fine-grained control between discovery and execution phases. (from draft-barney-caam)
  - [extension] JOSE DVS Extension: Extension to the JOSE framework that adds derived verification signature capabilities. Provides alternative authentication method beyond traditional pre-shared keys. (from draft-bastian-jose-dvs)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s6_
prompt_hash: 5e9ca38c4cb5b2a8
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:17:03.355406+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 7: 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.

  ## Relevant Ideas to Incorporate
  - [protocol] Public Key Derived HMAC: HMAC-based message authentication where the HMAC key is derived from public key information rather than using pre-shared symmetric keys. Allows verification using only public information from the JWS. (from draft-bastian-jose-pkdh)
  - [requirement] Congestion Control Behavior Definitions: Vendor-neutral definitions for congestion control mechanisms and their measurable behaviors in AI fabric environments. (from draft-calabria-bmwg-ai-fabric-terminology)
  - [mechanism] Delegation Evidence Verification via Token-Bound Certificates: Mechanism enabling Resource Servers to verify application-layer delegation evidence (like Verifiable Credentials) using AS-attested keys bound to access tokens. (from draft-chu-oauth-as-attested-user-cert)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s7_
prompt_hash: 3b435ffd6d2a3353
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:17:22.810447+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 8: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): An advanced attestation system using multiple Trusted Execution Environment vendors for high-assurance scenarios. Provides redundant hardware-based verification of AI agent integrity. (from draft-aylward-aiga-1)
  - [mechanism] Multi-Vendor TEE Attestation (M-TACE): A high-assurance verification system using multiple Trusted Execution Environment vendors to attest to AI agent compliance. This redundant approach prevents single-vendor compromise scenarios. (from draft-aylward-aiga-2)
  - [mechanism] Cryptographic Proof-Based Autonomy: A verification mechanism that uses cryptographic proofs to validate agent compliance with security policies before granting operational privileges. This replaces traditional time-based or reputation-based trust models. (from draft-berlinai-vera)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Agent Behavior Verification Protocol (ABVP)_s8_
prompt_hash: d7fc80a28ba6fc34
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:17:40.712131+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms used throughout the protocol including behavior attestation, verification proofs, and trust anchors. Establishes consistent vocabulary for behavior verification concepts.
  3. Problem Statement: 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.
  4. ABVP Architecture and Components: 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.
  5. Behavior Capture and Attestation Mechanisms: Specifies the technical mechanisms for capturing agent behavior traces and generating cryptographic attestations. Details the integration with existing TEE and hardware attestation systems.
  6. Protocol Integration and Bindings: Describes how ABVP integrates with existing agent protocols and authorization frameworks. Defines protocol bindings that enable behavior verification across different communication patterns.
  7. Verification Workflows and Enforcement: Details the operational workflows for behavior verification including real-time verification, batch verification, and dispute resolution. Specifies enforcement mechanisms and policy integration points.
  8. Security Considerations: Analyzes security implications of behavior verification including privacy concerns, attestation integrity, and resistance to behavior spoofing attacks. Addresses trust model assumptions and threat vectors.
  9. IANA Considerations: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  Write section 9: IANA Considerations
  Summary: Specifies registry requirements for behavior verification schemas, attestation formats, and protocol identifiers. Details namespace management for behavior verification vocabularies.

  ## Relevant Ideas to Incorporate
  - [protocol] AIPREF Vocabulary Protocol Alignment: Establish protocol bindings that align real-time communication behavior with standardized AIPREF vocabulary for AI requirements. (from draft-altanai-aipref-realtime-protocol-bindings)
  - [pattern] Five Enforcement Pillars with Typed Schemas: A structured set of security enforcement categories with formally defined data schemas that provide comprehensive coverage of AI agent security concerns. Each pillar represents a specific aspect of agent behavior that must be monitored and controlled. (from draft-berlinai-vera)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8446
  - RFC 5280
  - RFC 7519
  - RFC 9052
  - draft-birkholz-verifiable-agent-conversations
  - draft-aylward-aiga-1
  - RFC 6749
  - RFC 8259

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _selfrate_1_
prompt_hash: afd835fa6d89d555
model: claude-haiku-4-5-20251001
created_at: 2026-03-04T00:17:53.946939+00:00
request_json:
  Rate this IETF draft. JSON only.

  draft-ai-agent-behavior-verification-protocol-00 | Agent Behavior Verification Protocol (ABVP) | 2026-03-04 | ?pg
  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.

  Return JSON: {"s":"2-3 sentence summary","n":<1-5>,"nn":"novelty note","m":<1-5>,"mn":"maturity note","o":<1-5>,"on":"overlap note","mo":<1-5>,"mon":"momentum note","r":<1-5>,"rn":"relevance note","c":["categories"]}

  Scale: 1=very low..5=very high. Overlap: 1=unique,5=heavy overlap.
  Categories: A2A protocols, AI safety/alignment, ML traffic mgmt, Autonomous netops, Agent identity/auth, Data formats/interop, Policy/governance, Model serving/inference, Agent discovery/reg, Human-agent interaction, Other AI/agent
  JSON only, no fences.

response_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"
    ]
  }


draft_name: _pipeline_Federated Agent Learning Privacy_
prompt_hash: 4996527cd981334a
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:22:32.798312+00:00
request_json:
  You are writing an IETF Internet-Draft to address a gap in the AI/agent standardization landscape.

  ## Gap to Address
  Topic: Federated Agent Learning Privacy
  Description: While federated architectures exist, there's insufficient specification for privacy-preserving agent learning that prevents data leakage between federated participants during model updates.
  Category: Policy/governance
  Evidence: Federated AI architectures mentioned but private data training requirements suggest unresolved privacy challenges
  Severity: high

  ## Convergent Ideas from Existing Drafts
  These ideas from the current landscape converge on this topic — build on them, don't duplicate:
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Privacy-Preserving Human Tokens: Token-based system to clearly separate human traffic from agent traffic while preserving user privacy in the identification process. (from draft-dhir-http-agent-profile)
  - [mechanism] Layered Cookies Model: Updated approach to cookie specification that addresses historical security and privacy issues while maintaining backward compatibility. Represents an evolution from RFC 6265. (from draft-ietf-httpbis-layered-cookies)
  - [architecture] Agent Gateway Control Plane: Centralized control-plane entities that handle agent registration, authentication, capability management, and semantic routing while allowing direct peer-to-peer interactions between agents. Agent Gateways act as intermediaries for control functions without interfering with data plane communications. (from draft-li-dmsc-mcps-agw)
  - [mechanism] Inference-Time Data Access Policy Claims: Claims that specify and attest to constraints on what data an AI agent can access during inference operations. Enables enforcement of data usage boundaries and privacy controls. (from draft-messous-eat-ai)
  - [architecture] Autonomous Agent Governance Framework: A governance model that defines how AI agents can operate autonomously while maintaining appropriate oversight and control mechanisms. This addresses the balance between agent independence and system control. (from draft-mozley-aidiscovery)
  - [architecture] Data and Agent Aware-Inference and Training Network (DA-ITN): A unified, intelligent, multi-plane network architecture that connects AI clients, data providers, model providers, agent providers, service facilitators, and computational resources. Features dedicated control, data, and operations & management (OAM) planes to orchestrate training, inference, and agentic services. (from draft-akhavain-moussa-ai-network)
  - [requirement] AI Agent Structured Threat Model: A formalized threat modeling framework that identifies specific risks posed by compromised AI agents including data exfiltration, unauthorized transactions, and cascading system failures. It provides a systematic approach to understanding AI agent security risks. (from draft-berlinai-vera)
  - [architecture] Agentic Data Optimization Layer (ADOL): A foundational data layer that provides backward-compatible optimizations for agent communication protocols. Serves as an optimization layer between existing protocols and LLM processing. (from draft-chang-agent-token-efficient)
  - [architecture] Federated Policy Enforcement: A framework for harmonizing and enforcing policies across multiple administrative domains while maintaining local policy autonomy. Supports policy conflict resolution and unified enforcement for cross-domain agent interactions. (from draft-cui-dmsc-agent-cdi)
  - [mechanism] SD-JWT Privacy-Preserving Trust Proof: Alternative attestation mechanism using Selective Disclosure JWT (RFC 9901) that proves hardware trust level claims without revealing specific hardware identity. Maintains privacy while demonstrating genuine TPM presence. (from draft-drake-email-tpm-attestation)
  - [architecture] CATS Flow Distribution Architecture: An architectural model that separates control plane policy distribution from data plane flow steering decisions. The architecture enables autonomous data plane operation while maintaining centralized policy control. (from draft-fu-cats-flow-lb)
  - [mechanism] Cryptographic Erasure Compliance: Mechanism to ensure agent memory associated with specific user or agent identities can be cryptographically erased for privacy compliance. Addresses regulatory requirements for data deletion. (from draft-gaikwad-aps-profile)
  - [mechanism] AGw Data Synchronization Mechanism: Technical approach for maintaining data consistency and synchronization between distributed Agent Gateways. Ensures coherent state management across the Agent Internet ecosystem. (from draft-han-rtgwg-agent-gateway-intercomm-framework)
  - [mechanism] Distributed Agent Communication Security Model: Security mechanisms tailored for protecting intercommunication between Agent Gateways in distributed intelligent agent deployments. Addresses unique security challenges in cross-domain agent collaboration. (from draft-han-rtgwg-agent-gateway-intercomm-framework)
  - [mechanism] ZKP-based Agent Attestation: Zero-Knowledge Proofs enable AI agents to prove their attestation properties during certificate enrollment without revealing sensitive details. This provides private, efficient, and continuous attestation while maintaining scalability. (from draft-huang-acme-scalable-agent-enrollment)
  - [pattern] Privacy-Respecting Capability Attestation: Attestation approach that enables capability verification while preserving agent privacy in decentralized environments. Balances transparency requirements with confidentiality needs. (from draft-huang-rats-agentic-eat-cap-attest)
  - [architecture] AI Agent Protocol Capabilities Framework: Specification of necessary network protocol features and capabilities to support heterogeneous quality-of-service demands from AI agent multi-modal data streams. (from draft-hw-protocol-agent)

  ## RFC Foundations
  Most-referenced RFCs in this space — cite where relevant:
  - RFC 2119 (cited by 73 drafts)
  - RFC 8174 (cited by 65 drafts)
  - RFC 6749 (cited by 17 drafts)
  - RFC 9110 (cited by 12 drafts)
  - RFC 7519 (cited by 12 drafts)
  - RFC 9334 (cited by 8 drafts)
  - RFC 7515 (cited by 7 drafts)
  - RFC 8259 (cited by 6 drafts)
  - RFC 5280 (cited by 6 drafts)
  - RFC 8446 (cited by 5 drafts)

  ## Similar Existing Drafts
  These drafts are closest to this gap — differentiate from them:
  - draft-kale-agntcy-federated-privacy: Privacy-Preserving Federated Learning Architecture for Multi-Tenant AI Agent Systems (similarity: 0.78)
  - draft-zhang-dmsc-mas-communication: Security Analysis of Multi-agents Secured Communication and Limitations of Existing Protocols (similarity: 0.68)
  - draft-akhavain-moussa-ai-network: AI Network for Training, Inference, and Agentic Interactions (similarity: 0.68)
  - draft-gaikwad-aps-profile: Agent Persistent State Profile (similarity: 0.67)
  - draft-narajala-ans: Agent Name Service (ANS): A Universal Directory for Secure AI Agent Discovery and Interoperability (similarity: 0.67)
  - draft-berlinai-vera: VERA: Verifiable Enforcement for Runtime Agents (similarity: 0.66)
  - draft-li-cats-idn: A Framework of Intelligence Delivery Network (IDN) for Deep Learning Inference (similarity: 0.66)
  - draft-chuyi-nmrg-agentic-network-inference: Agentic Network Architecture and Protocol for Supporting Agent Interconnection Communication and Multi-level Inference (similarity: 0.65)

  ## Top-Rated Drafts in Category
  Drafts the community considers strong in this area:
  - draft-cowles-volt: Verifiable Operations Ledger and Trace (VOLT) Protocol (score: 4.8)
  - draft-aylward-daap-v2: Distributed AI Accountability Protocol (DAAP) Version 2.0 (score: 4.8)
  - draft-goswami-agentic-jwt: Secure Intent Protocol: JWT Compatible Agentic Identity and Workflow Management (score: 4.5)
  - draft-wang-cats-odsi: An Open, Decentralized, and Scalable Framework for Large Language Model Inference (score: 4.5)
  - draft-birkholz-verifiable-agent-conversations: Verifiable Agent Conversation Records (score: 4.5)

  ## Working Group Context
  434 drafts, 52 WG-adopted. Top WGs: lake (7), lamps (6), anima (3), dnsop (3), httpbis (3)

  ## Ecosystem Vision
  (Elevator Version)

  **Problem**: 260+ IETF drafts on AI agents; no single model for orchestration (DAG), human oversight (HITL), or for running the same system in both fast and regulated environments.

  **Proposal**: Five coordinated drafts — (A) shared architecture and terminology, (B) DAG execution with checkpoints and rollback, (C) HITL primitives (approval, override, explainability), (D) protocol-agnostic binding so any A2A protocol can participate, (E) assurance profiles so the same stack works in K8s or in a proof-heavy regulated regime.

  **Outcome**: One holistic agent ecosystem: DAG + HITL built in, agnostic and extensible, applicable everywhere from relaxed to fully proven.

  ## Sibling Drafts (same family)
  (none — this is the first draft in the family)

  Generate a detailed outline for an Internet-Draft that fills this gap.
  Return JSON:
  {
    "title": "full draft title",
    "abstract": "150-250 word abstract",
    "sections": [
      {"title": "section title", "summary": "2-3 sentence summary of content", "key_ideas": ["idea titles to incorporate"]}
    ],
    "normative_refs": ["RFC NNNN", "draft-name"],
    "informative_refs": ["RFC NNNN", "draft-name"],
    "terminology": {"term": "definition"},
    "target_wg": "suggested IETF working group",
    "intended_status": "informational|standards-track|experimental"
  }

  Requirements:
  - Include standard sections: Introduction, Terminology, Problem Statement, then 2-4 technical sections, Security Considerations, IANA Considerations
  - Reference specific RFCs and drafts from the context above
  - Use terminology consistent with sibling drafts if any
  - Abstract should clearly state the problem, approach, and contribution
  JSON only, no fences.

response_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"
  }


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s0_
prompt_hash: 61c38e23a33da4b0
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:22:45.647225+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 1: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_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.

  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s1_
prompt_hash: 0e73fb0b28aa2e9e
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:23:03.184718+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 2: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s2_
prompt_hash: e8cf94177851753a
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:23:21.176541+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 3: 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.

  ## Relevant Ideas to Incorporate
  - [requirement] AI Agent Structured Threat Model: A formalized threat modeling framework that identifies specific risks posed by compromised AI agents including data exfiltration, unauthorized transactions, and cascading system failures. It provides a systematic approach to understanding AI agent security risks. (from draft-berlinai-vera)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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 (ε_total, δ_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.

  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s3_
prompt_hash: 3f58413d195da2be
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:23:37.162213+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 4: 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.

  ## Relevant Ideas to Incorporate
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Agent Gateway Control Plane: Centralized control-plane entities that handle agent registration, authentication, capability management, and semantic routing while allowing direct peer-to-peer interactions between agents. Agent Gateways act as intermediaries for control functions without interfering with data plane communications. (from draft-li-dmsc-mcps-agw)
  - [architecture] Federated Policy Enforcement: A framework for harmonizing and enforcing policies across multiple administrative domains while maintaining local policy autonomy. Supports policy conflict resolution and unified enforcement for cross-domain agent interactions. (from draft-cui-dmsc-agent-cdi)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s4_
prompt_hash: 4b0b89302d3c6a34
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:23:55.824216+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 5: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s5_
prompt_hash: e9b0599b7b4e21fc
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:24:12.201729+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 6: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] ZKP-based Agent Attestation: Zero-Knowledge Proofs enable AI agents to prove their attestation properties during certificate enrollment without revealing sensitive details. This provides private, efficient, and continuous attestation while maintaining scalability. (from draft-huang-acme-scalable-agent-enrollment)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s6_
prompt_hash: 6a79de46219c5d07
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:24:29.731901+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 7: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] SD-JWT Privacy-Preserving Trust Proof: Alternative attestation mechanism using Selective Disclosure JWT (RFC 9901) that proves hardware trust level claims without revealing specific hardware identity. Maintains privacy while demonstrating genuine TPM presence. (from draft-drake-email-tpm-attestation)
  - [mechanism] Cryptographic Erasure Compliance: Mechanism to ensure agent memory associated with specific user or agent identities can be cryptographically erased for privacy compliance. Addresses regulatory requirements for data deletion. (from draft-gaikwad-aps-profile)
  - [pattern] Privacy-Respecting Capability Attestation: Attestation approach that enables capability verification while preserving agent privacy in decentralized environments. Balances transparency requirements with confidentiality needs. (from draft-huang-rats-agentic-eat-cap-attest)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s7_
prompt_hash: 26407956465a8663
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:24:49.074129+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 8: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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 "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.

  The 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.

  Privacy 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.

  The 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.

  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s8_
prompt_hash: b0da744cd6ae7c49
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:25:05.565013+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 9: 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.

  ## Relevant Ideas to Incorporate
  - [mechanism] Distributed Agent Communication Security Model: Security mechanisms tailored for protecting intercommunication between Agent Gateways in distributed intelligent agent deployments. Addresses unique security challenges in cross-domain agent collaboration. (from draft-han-rtgwg-agent-gateway-intercomm-framework)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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.


draft_name: _pipeline_Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments_s9_
prompt_hash: aa49ebd2ebe8f7be
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:25:19.502806+00:00
request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: Defines key terms including federated agent learning, differential privacy budgets, secure aggregation, and tenant isolation. Establishes consistent vocabulary for privacy-preserving federated protocols.
  3. Problem Statement: 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.
  4. Privacy-Preserving Agent Learning Architecture: 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.
  5. Differential Privacy for Agent Model Updates: 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.
  6. Secure Multi-Tenant Federated Aggregation: Details the cryptographic protocols for secure parameter aggregation across tenant boundaries. Includes homomorphic encryption schemes, secure multiparty computation, and verification mechanisms for aggregation correctness.
  7. Privacy Compliance and Verification: Defines mechanisms for verifying privacy compliance including zero-knowledge proofs of differential privacy adherence, audit trails, and cryptographic erasure for data deletion requirements.
  8. Protocol Message Formats and Flows: Specifies the concrete message formats, communication flows, and API definitions for the privacy-preserving federated learning protocol. Includes JSON schemas and example interactions.
  9. Security Considerations: Analyzes security properties of the protocol including resistance to known attacks, key management requirements, and operational security considerations for multi-tenant deployments.
  10. IANA Considerations: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  Write section 10: IANA Considerations
  Summary: Specifies any required IANA registrations for new message types, error codes, or protocol identifiers.

  ## Relevant Ideas to Incorporate
  - [mechanism] Differential Privacy for Agent Models: Integration of differential privacy mechanisms specifically designed for AI agent model training to provide formal privacy guarantees. This prevents sensitive behavioral data exposure during cross-tenant knowledge transfer. (from draft-kale-agntcy-federated-privacy)
  - [mechanism] Multi-Tenant Federated Averaging: A federated averaging mechanism adapted for multi-tenant AI agent systems that enables model parameter aggregation across organizational boundaries. The protocol maintains tenant isolation while allowing collaborative learning from distributed agent behaviors. (from draft-kale-agntcy-federated-privacy)
  - [architecture] Privacy-Preserving Reference Architecture: A comprehensive reference architecture that combines federated learning, differential privacy, and secure aggregation for multi-tenant AI deployments. The architecture provides formal privacy guarantees while enabling collaborative model improvement. (from draft-kale-agntcy-federated-privacy)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - RFC 8446
  - draft-kale-agntcy-federated-privacy
  - RFC 6749
  - RFC 9110
  - RFC 5280
  - draft-berlinai-vera

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_json:
  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 (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.

  The 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.

  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 (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.


draft_name: _selfrate_2_
prompt_hash: 4b8eaa1aec13ebc5
model: claude-haiku-4-5-20251001
created_at: 2026-03-04T00:25:23.802906+00:00
request_json:
  Rate this IETF draft. JSON only.

  draft-ai-agent-learning-protocol-00 | Privacy-Preserving Agent Learning Protocol for Federated Multi-Tenant Environments | 2026-03-04 | ?pg
  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.

  Return JSON: {"s":"2-3 sentence summary","n":<1-5>,"nn":"novelty note","m":<1-5>,"mn":"maturity note","o":<1-5>,"on":"overlap note","mo":<1-5>,"mon":"momentum note","r":<1-5>,"rn":"relevance note","c":["categories"]}

  Scale: 1=very low..5=very high. Overlap: 1=unique,5=heavy overlap.
  Categories: A2A protocols, AI safety/alignment, ML traffic mgmt, Autonomous netops, Agent identity/auth, Data formats/interop, Policy/governance, Model serving/inference, Agent discovery/reg, Human-agent interaction, Other AI/agent
  JSON only, no fences.

response_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"
    ]
  }


draft_name: _pipeline_Agent Task DAG_
prompt_hash: 3e8bad59a9a1de6e
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:25:47.953989+00:00
request_json:
  You are writing an IETF Internet-Draft to address a gap in the AI/agent standardization landscape.

  ## Gap to Address
  Topic: Agent Task DAG
  Description: Agent Task DAG
  Category: 
  Evidence: 
  Severity: medium

  ## Convergent Ideas from Existing Drafts
  These ideas from the current landscape converge on this topic — build on them, don't duplicate:
  - [profile] Agent Authorization Profile (AAP): An authorization profile that extends OAuth 2.0 and JWT with structured claims for autonomous AI agents. Enables systems to reason about agent identity, task context, operational constraints, delegation chains, and human oversight requirements. (from draft-aap-oauth-profile)
  - [mechanism] Structured Claims for Agent Context: Standardized JWT claims that capture agent identity, task context, and operational constraints in machine-readable format. Allows authorization systems to make context-aware decisions for AI agents. (from draft-aap-oauth-profile)
  - [mechanism] Agent Context Distribution: A mechanism for distributing context information among multiple agents in a multi-agent system. Impacts execution latency, token consumption, and task completion success rates in complex multi-round workflows. (from draft-chang-agent-context-interaction)
  - [protocol] Agent Task Negotiation: A protocol for AI agents to negotiate task assignments, resource allocation, and execution parameters. Supports autonomous agreement on workload distribution among multiple agents. (from draft-cui-ai-agent-task)
  - [mechanism] Context-Aware Task Scheduling: A scheduling mechanism that considers environmental context, agent capabilities, and current network conditions when assigning tasks. Optimizes task execution based on real-time system state. (from draft-cui-ai-agent-task)
  - [requirement] Real-Time Task Adaptability: A requirement for agent protocols to support dynamic adjustment of task execution based on changing conditions. Enables agents to modify their behavior without manual intervention. (from draft-cui-ai-agent-task)
  - [mechanism] Representative Task Suite: A standardized collection of network configuration scenarios and challenges designed to comprehensively test LLM agent capabilities. The task suite covers various network management operations from basic setup to complex troubleshooting scenarios. (from draft-cui-nmrg-llm-benchmark)
  - [mechanism] Agent Capability-Based Routing: Routing strategy that selects paths and next-hop agents based on advertised capabilities and task requirements rather than traditional network topology. (from draft-du-catalist-routing-considerations)
  - [mechanism] STAMP Token Format: Structured token format that encodes task bindings, agent identities, and cryptographic proofs. Designed specifically for multi-agent environments with dynamic workflows. (from draft-guy-bary-stamp-protocol)
  - [pattern] Task Duration Handling: Protocol differentiation between short-lived and long-running tasks with appropriate token lifecycle management. Addresses the temporal aspects of autonomous agent operations. (from draft-guy-bary-stamp-protocol)
  - [protocol] Agent Discovery Protocol: A discovery mechanism that allows agents to find and identify other agents with relevant capabilities for collaborative task execution. Enables dynamic formation of agent collaborations. (from draft-liu-dmsc-acps-arc)
  - [mechanism] Task-based Agent Searching: Discovery mechanism that locates agents based on their capabilities or task types rather than identities. Supports dynamic collaboration by finding suitable agents for specific functions. (from draft-liu-dmsc-gw-requirements)
  - [framework] APN Framework for IoA: Application of Application-aware Packet Network (APN) framework to provide fine-grained network services for AI agent interactions. Maps different AI agent task requirements to corresponding network service differentiation. (from draft-mao-rtgwg-apn-framework-for-ioa)
  - [mechanism] Agent Task-to-Network Mapping: A mechanism to translate varying AI agent interaction requirements into specific network service demands. Enables network differentiation based on agent collaboration patterns and task characteristics. (from draft-mao-rtgwg-apn-framework-for-ioa)
  - [protocol] AI Agent Collaboration Protocol: A communication protocol specifically designed for AI agents to coordinate tasks across multiple network devices. The protocol defines message formats, authentication mechanisms, and task coordination procedures between distributed AI agents. (from draft-mzsg-rtgwg-agent-cross-device-comm-framework)
  - [extension] Unified Transport for A2A and MCP: Concrete mapping of Agent-to-Agent and Model Context Protocol onto MOQT to enable agent discovery, task delegation, and resource access through a single transport substrate. (from draft-nandakumar-ai-agent-moq-transport)
  - [protocol] Agent to Agent Protocol (A2A): A protocol specifically designed for direct communication between AI agents. This enables AI agents to interact with each other for collaborative task execution and resource sharing. (from draft-rosenberg-aiproto-framework)
  - [pattern] Applier-On-Behalf-Of Authorization Pattern: A leading AI agent applies for access tokens on behalf of multiple sub-AI agents to enable coordinated task completion. This eliminates the need for each sub-agent to individually interact with the authorization server. (from draft-song-oauth-ai-agent-collaborate-authz)
  - [mechanism] Intent Resolution and Matching: A process for analyzing incoming intent queries and matching them against registered agent capabilities. Performs semantic understanding of intent to determine appropriate agent selection for task execution. (from draft-sun-zhang-iaip)
  - [mechanism] Intent-based Routing and Forwarding: A routing mechanism that makes forwarding decisions based on semantic intent and agent capabilities rather than traditional network addresses. Dispatches intent queries to the most suitable agents based on task requirements. (from draft-sun-zhang-iaip)

  ## RFC Foundations
  Most-referenced RFCs in this space — cite where relevant:
  - RFC 2119 (cited by 285 drafts)
  - RFC 8174 (cited by 237 drafts)
  - RFC 8446 (cited by 42 drafts)
  - RFC 6749 (cited by 36 drafts)
  - RFC 9110 (cited by 34 drafts)
  - RFC 8126 (cited by 26 drafts)
  - RFC 8259 (cited by 26 drafts)
  - RFC 5280 (cited by 22 drafts)
  - RFC 7519 (cited by 22 drafts)
  - RFC 9052 (cited by 20 drafts)

  ## Similar Existing Drafts
  These drafts are closest to this gap — differentiate from them:
  - draft-rosenberg-cheq: CHEQ: A Protocol for Confirmation AI Agent Decisions with Human in the Loop (HITL) (similarity: 0.61)
  - draft-ounsworth-lamps-cms-dhkem: Use of the DH-Based KEM (DHKEM) in the Cryptographic Message Syntax (CMS) (similarity: 0.61)
  - draft-rosenberg-aiproto-cheq: CHEQ: A Protocol for Confirmation AI Agent Decisions with Human in the Loop (HITL) (similarity: 0.61)
  - draft-mao-rtgwg-apn-framework-for-ioa: APN Framework for Internet of Agent (IoA) (similarity: 0.60)
  - draft-chen-lake-edhoc-aka: EDHOC Authenticated with AKA (similarity: 0.60)
  - draft-zhang-rtgwg-llmmoe-multicast: Multicast usage in LLM MoE (similarity: 0.59)
  - draft-ietf-lake-edhoc-psk: EDHOC Authenticated with Pre-Shared Keys (PSK) (similarity: 0.59)
  - draft-ye-problems-and-requirements-of-dns-for-ioa: Problems Statement and Requirements Analysis of DNS for Internet of Agents (IoA) (similarity: 0.59)

  ## Top-Rated Drafts in Category
  Drafts the community considers strong in this area:
  - draft-cowles-volt: Verifiable Operations Ledger and Trace (VOLT) Protocol (score: 4.8)
  - draft-aylward-daap-v2: Distributed AI Accountability Protocol (DAAP) Version 2.0 (score: 4.8)
  - draft-guy-bary-stamp-protocol: Secure Task-bound Agent Message Proof (STAMP) Protocol (score: 4.6)
  - draft-drake-email-tpm-attestation: Hardware Attestation for Email Sender Verification (score: 4.6)
  - draft-williams-netmod-lm-hierarchy-topology: Hierarchical Topology for Language Model Coordination (score: 4.6)

  ## Working Group Context
  434 drafts, 52 WG-adopted. Top WGs: lake (7), lamps (6), anima (3), dnsop (3), httpbis (3)

  ## Ecosystem Vision
  (Elevator Version)

  **Problem**: 260+ IETF drafts on AI agents; no single model for orchestration (DAG), human oversight (HITL), or for running the same system in both fast and regulated environments.

  **Proposal**: Five coordinated drafts — (A) shared architecture and terminology, (B) DAG execution with checkpoints and rollback, (C) HITL primitives (approval, override, explainability), (D) protocol-agnostic binding so any A2A protocol can participate, (E) assurance profiles so the same stack works in K8s or in a proof-heavy regulated regime.

  **Outcome**: One holistic agent ecosystem: DAG + HITL built in, agnostic and extensible, applicable everywhere from relaxed to fully proven.

  ## Sibling Drafts (same family)
  (none — this is the first draft in the family)

  Generate a detailed outline for an Internet-Draft that fills this gap.
  Return JSON:
  {
    "title": "full draft title",
    "abstract": "150-250 word abstract",
    "sections": [
      {"title": "section title", "summary": "2-3 sentence summary of content", "key_ideas": ["idea titles to incorporate"]}
    ],
    "normative_refs": ["RFC NNNN", "draft-name"],
    "informative_refs": ["RFC NNNN", "draft-name"],
    "terminology": {"term": "definition"},
    "target_wg": "suggested IETF working group",
    "intended_status": "informational|standards-track|experimental"
  }

  Requirements:
  - Include standard sections: Introduction, Terminology, Problem Statement, then 2-4 technical sections, Security Considerations, IANA Considerations
  - Reference specific RFCs and drafts from the context above
  - Use terminology consistent with sibling drafts if any
  - Abstract should clearly state the problem, approach, and contribution
  JSON only, no fences.

response_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"
  }


prompt_hash: ae016122840dc28a
model: claude-sonnet-4-20250514
created_at: 2026-03-04T00:26:01.064380+00:00
draft_name:
  _pipeline_Agent Task DAG: A Framework for Directed Acyclic Graph Execution in Multi-Agent Systems_s0_

request_json:
  Write the following section of an Internet-Draft titled "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.

  Full outline:
  1. Introduction: 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.
  2. Terminology: 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.
  3. Problem Statement: 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.
  4. Agent Task DAG Framework: 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.
  5. Task Execution Protocol: Describes the protocol for executing DAG workflows including task scheduling, parallel execution, and coordination between agents. Defines message formats and state synchronization mechanisms.
  6. Checkpoint and Recovery Mechanisms: Specifies checkpoint creation, rollback procedures, and failure recovery strategies for DAG execution. Ensures workflow resilience and enables human intervention points.
  7. Integration with Existing Agent Protocols: Details how the DAG framework integrates with existing agent authorization profiles, discovery protocols, and communication mechanisms. Provides protocol-agnostic binding approaches.
  8. Security Considerations: Addresses security aspects of DAG execution including task authorization, context isolation, and audit trail requirements. Considers threats specific to multi-agent workflow execution.
  9. IANA Considerations: Specifies any new registries or protocol identifiers needed for the Agent Task DAG framework.

  Write section 1: 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.

  ## Relevant Ideas to Incorporate
  - [profile] Agent Authorization Profile (AAP): An authorization profile that extends OAuth 2.0 and JWT with structured claims for autonomous AI agents. Enables systems to reason about agent identity, task context, operational constraints, delegation chains, and human oversight requirements. (from draft-aap-oauth-profile)
  - [mechanism] Structured Claims for Agent Context: Standardized JWT claims that capture agent identity, task context, and operational constraints in machine-readable format. Allows authorization systems to make context-aware decisions for AI agents. (from draft-aap-oauth-profile)
  - [mechanism] Agent Context Distribution: A mechanism for distributing context information among multiple agents in a multi-agent system. Impacts execution latency, token consumption, and task completion success rates in complex multi-round workflows. (from draft-chang-agent-context-interaction)

  ## RFC References to Cite
  - RFC 2119
  - RFC 8174
  - RFC 8259
  - RFC 7519
  - draft-aap-oauth-profile
  - draft-cui-ai-agent-task
  - draft-guy-bary-stamp-protocol
  - RFC 6749
  - RFC 9110
  - draft-chang-agent-context-interaction

  ## Cross-References to Sister Drafts
  (none — this is the first draft in the family)

  ## 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

  Follow IETF Internet-Draft conventions:
  - Formal, precise technical language
  - Use RFC 2119 keywords (MUST, SHOULD, MAY) where appropriate
  - Reference existing RFCs and drafts where relevant (use [RFCNNNN] format)
  - 3-6 paragraphs per section
  - Use the terminology definitions provided above consistently

  Write the section content only (no section number or title). Plain text.

response_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.

  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.


