# gaps

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

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


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

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


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

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


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


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

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


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

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


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

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


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

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


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

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


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

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


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

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


# generated_drafts

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

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

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

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


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

  Abstract

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

  Status of This Memo

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

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

  Terminology

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

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

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

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

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

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

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


  Table of Contents

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

  1.  Introduction

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

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

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

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

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

  2.  Terminology

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

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

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

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

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

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

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

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

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

  3.  Problem Statement

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

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

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

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

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

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

  4.  ABVP Architecture and Components

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

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

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

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

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

  5.  Behavior Capture and Attestation Mechanisms

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

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

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

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

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

  6.  Protocol Integration and Bindings

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

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

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

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

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

  7.  Verification Workflows and Enforcement

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

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

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

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

  8.  Security Considerations

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

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

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

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

  9.  IANA Considerations

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

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

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

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

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

  10.  References

  10.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8446]
           RFC 8446

     [RFC 5280]
           RFC 5280

     [RFC 7519]
           RFC 7519

     [RFC 9052]
           RFC 9052

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

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

  10.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 8259]
           RFC 8259

     [RFC 9110]
           RFC 9110

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

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

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

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

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

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

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

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

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

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


  Author's Address

     Generated by IETF Draft Analyzer
     2026-03-04

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


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

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

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

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


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

  Abstract

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

  Status of This Memo

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

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

  Terminology

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

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

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

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

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

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

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


  Table of Contents

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

  1.  Introduction

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

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

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

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

  2.  Terminology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  3.  Problem Statement

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

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

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

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

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

  4.  Privacy-Preserving Agent Learning Architecture

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

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

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

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

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

  5.  Differential Privacy for Agent Model Updates

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

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

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

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

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

  6.  Secure Multi-Tenant Federated Aggregation

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

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

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

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

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

  7.  Privacy Compliance and Verification

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

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

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

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

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

  8.  Protocol Message Formats and Flows

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

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

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

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

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

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

  9.  Security Considerations

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

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

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

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

  10.  IANA Considerations

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

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

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

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

  11.  References

  11.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8259]
           RFC 8259

     [RFC 7519]
           RFC 7519

     [RFC 8446]
           RFC 8446

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

  11.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 9110]
           RFC 9110

     [RFC 5280]
           RFC 5280

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

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

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

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

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

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

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


  Author's Address

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

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


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

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

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

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


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

  Abstract

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

  Status of This Memo

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

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

  Terminology

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

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

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

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

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

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

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


  Table of Contents

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

  1.  Introduction

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

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

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

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

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

  2.  Terminology

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

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

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

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

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

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

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

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

  3.  Problem Statement

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

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

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

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

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

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

  4.  Agent Task DAG Framework

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

     4.1. DAG Data Model

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

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

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

     4.2. Execution Context Management

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

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

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

     4.3. Task Execution Semantics

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

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

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

  5.  Task Execution Protocol

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

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

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

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

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

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

  6.  Checkpoint and Recovery Mechanisms

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

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

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

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

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

  7.  Integration with Existing Agent Protocols

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

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

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

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

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

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

  8.  Security Considerations

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

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

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

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

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

  9.  IANA Considerations

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

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

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

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

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

  10.  References

  10.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8259]
           RFC 8259

     [RFC 7519]
           RFC 7519

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

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

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

  10.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 9110]
           RFC 9110

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

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

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

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

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

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


  Author's Address

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

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


gap_topic: Human-in-the-Loop
draft_name: draft-ai-primitives-for-00
title: Human-in-the-Loop (HITL) Primitives for AI Agent Systems
family_name: agent-ecosystem
family_role: HITL
status: draft
created_at: 2026-03-04T00:31:19.926366+00:00
abstract:
  As AI agents become increasingly autonomous in network operations and other critical domains, the need for standardized human oversight mechanisms becomes paramount. This document defines a framework of Human-in-the-Loop (HITL) primitives that enable structured human intervention, approval, and oversight in AI agent decision-making processes. The framework provides three core primitives: approval workflows that require explicit human consent before action execution, override mechanisms that allow humans to modify or halt agent decisions, and explainability interfaces that provide transparency into agent reasoning. These primitives are designed to be protocol-agnostic and can be integrated with existing agent architectures to ensure human control over autonomous systems. The specification addresses the critical gap between fully autonomous AI operation and human accountability requirements, particularly in regulated environments where human oversight is mandatory. By standardizing these HITL mechanisms, organizations can deploy AI agents with appropriate human safeguards while maintaining operational efficiency and regulatory compliance.

outline_json:
  {"title": "Human-in-the-Loop (HITL) Primitives for AI Agent Systems", "abstract": "As AI agents become increasingly autonomous in network operations and other critical domains, the need for standardized human oversight mechanisms becomes paramount. This document defines a framework of Human-in-the-Loop (HITL) primitives that enable structured human intervention, approval, and oversight in AI agent decision-making processes. The framework provides three core primitives: approval workflows that require explicit human consent before action execution, override mechanisms that allow humans to modify or halt agent decisions, and explainability interfaces that provide transparency into agent reasoning. These primitives are designed to be protocol-agnostic and can be integrated with existing agent architectures to ensure human control over autonomous systems. The specification addresses the critical gap between fully autonomous AI operation and human accountability requirements, particularly in regulated environments where human oversight is mandatory. By standardizing these HITL mechanisms, organizations can deploy AI agents with appropriate human safeguards while maintaining operational efficiency and regulatory compliance.", "sections": [{"title": "Introduction", "summary": "Introduces the critical need for standardized human oversight in AI agent systems. Explains how current agent deployments lack consistent HITL mechanisms and the risks this poses.", "key_ideas": ["Human oversight gap in AI systems", "Regulatory compliance requirements", "Safety mechanisms for autonomous agents"]}, {"title": "Terminology", "summary": "Defines key terms used throughout the document including HITL primitives, approval workflows, override mechanisms, and explainability interfaces. Establishes consistent vocabulary for human-agent interaction patterns.", "key_ideas": ["HITL primitive definitions", "Human oversight terminology", "Agent decision points"]}, {"title": "Problem Statement", "summary": "Details the current lack of standardized human oversight mechanisms in AI agent systems. Examines the consequences of uncontrolled autonomous operation and the need for human accountability.", "key_ideas": ["Autonomous agent risks", "Human accountability gaps", "Regulatory oversight requirements"]}, {"title": "HITL Primitive Framework", "summary": "Defines the core architecture for human-in-the-loop primitives. Establishes the fundamental building blocks that enable human oversight of agent decisions.", "key_ideas": ["Core HITL architecture", "Primitive classification", "Human-agent interaction models"]}, {"title": "Approval Workflow Primitives", "summary": "Specifies standardized approval mechanisms that require explicit human consent before agent actions are executed. Defines approval request formats, response patterns, and timeout handling.", "key_ideas": ["Human approval requirements", "Approval request protocols", "Consent validation mechanisms"]}, {"title": "Override and Intervention Primitives", "summary": "Details mechanisms that allow humans to modify, halt, or redirect agent decisions in real-time. Includes emergency stop procedures and decision modification interfaces.", "key_ideas": ["Human override capabilities", "Emergency intervention", "Real-time decision modification"]}, {"title": "Explainability and Transparency Primitives", "summary": "Defines interfaces that provide humans with insight into agent reasoning and decision-making processes. Enables informed human oversight through transparency mechanisms.", "key_ideas": ["Agent reasoning transparency", "Decision explanation formats", "Human understanding interfaces"]}, {"title": "Integration with Agent Architectures", "summary": "Describes how HITL primitives can be integrated with existing agent systems and protocols. Provides guidance for protocol-agnostic implementation.", "key_ideas": ["Protocol integration patterns", "Existing system compatibility", "Architecture adaptation guidelines"]}, {"title": "Security Considerations", "summary": "Addresses security implications of HITL mechanisms including authentication of human operators, protection against manipulation, and secure communication channels.", "key_ideas": ["Human operator authentication", "HITL security threats", "Secure oversight channels"]}, {"title": "IANA Considerations", "summary": "Specifies any registry requirements for HITL primitive types, approval workflow identifiers, and standard response codes.", "key_ideas": ["HITL primitive registries", "Standard identifier spaces", "Response code allocations"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 8446", "RFC 9110", "RFC 8259", "draft-rosenberg-cheq"], "informative_refs": ["RFC 6749", "RFC 7519", "draft-cui-nmrg-llm-nm", "draft-irtf-nmrg-llm-nm", "draft-rosenberg-aiproto-cheq", "draft-aap-oauth-profile", "draft-cowles-volt", "draft-aylward-daap-v2"], "terminology": {"HITL Primitive": "A standardized mechanism that enables human oversight and intervention in AI agent decision-making processes", "Approval Workflow": "A structured process that requires explicit human consent before an agent action is executed", "Override Mechanism": "A capability that allows humans to modify, halt, or redirect agent decisions in real-time", "Explainability Interface": "A standardized method for agents to provide transparency into their reasoning and decision-making processes", "Human Oversight": "The structured involvement of human operators in monitoring, approving, or modifying AI agent actions", "Decision Point": "A moment in agent execution where human intervention may be required or beneficial", "Intervention Trigger": "A condition or threshold that activates human-in-the-loop mechanisms"}, "target_wg": "anima", "intended_status": "standards-track"}

sections_json:
  ["The rapid advancement and deployment of AI agents in critical network operations and infrastructure management has created an urgent need for standardized human oversight mechanisms. As documented in [draft-cui-nmrg-llm-nm] and [draft-irtf-nmrg-llm-nm], AI agents are increasingly being deployed for network management tasks that were traditionally performed by human operators. However, the current landscape of AI agent systems lacks consistent and interoperable Human-in-the-Loop (HITL) mechanisms, creating significant risks for organizations that require human accountability and oversight in their autonomous systems.\n\nCurrent AI agent deployments typically implement ad-hoc or proprietary mechanisms for human oversight, if any oversight mechanisms exist at all. This inconsistency creates several critical problems: organizations cannot easily integrate HITL capabilities across different agent systems, human operators lack standardized interfaces for agent oversight, and regulatory compliance becomes difficult to achieve and demonstrate. The absence of standardized HITL primitives means that each agent implementation must create its own oversight mechanisms, leading to fragmented approaches that cannot interoperate and may have significant security or reliability gaps.\n\nThe risks of uncontrolled autonomous operation are particularly acute in regulated environments such as financial services, healthcare, and critical infrastructure, where human oversight is often legally mandated. When AI agents operate without appropriate human safeguards, organizations face potential regulatory violations, liability issues, and operational failures that could have been prevented through proper human oversight. Furthermore, the lack of standardized explainability interfaces means that human operators often cannot understand or validate agent decisions, undermining the effectiveness of any oversight mechanisms that do exist.\n\nThis document addresses these challenges by defining a comprehensive framework of HITL primitives that can be integrated with existing agent architectures in a protocol-agnostic manner. The framework builds upon established patterns from OAuth 2.0 [RFC6749] and JSON Web Tokens [RFC7519] to provide standardized mechanisms for approval workflows, override capabilities, and explainability interfaces. These primitives are designed to ensure that human oversight can be consistently implemented and enforced across different AI agent systems while maintaining the operational efficiency benefits of autonomous operation.\n\nThe HITL primitive framework specified in this document enables organizations to deploy AI agents with appropriate human safeguards that meet regulatory requirements and organizational policies. By standardizing these mechanisms, the framework facilitates interoperability between different agent systems and provides a foundation for secure, accountable autonomous operations. The primitives are designed to be composable and configurable, allowing organizations to implement the level of human oversight appropriate for their specific use cases and risk tolerance.", "This document uses terminology consistent with [RFC2119] and [RFC8174] when describing requirement levels. The key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.\n\n**HITL Primitive** refers to a standardized, composable mechanism that enables structured human oversight and intervention in AI agent decision-making processes. These primitives serve as the fundamental building blocks for implementing human control over autonomous systems and can be combined to create comprehensive oversight frameworks tailored to specific operational requirements.\n\n**Approval Workflow** defines a structured process that requires explicit human consent before an agent action is executed. As described in [draft-cui-nmrg-llm-nm] and [draft-irtf-nmrg-llm-nm], these workflows enforce human control over automated actions by creating mandatory checkpoints where agent decisions must receive human validation. Approval workflows include mechanisms for request formatting, response handling, timeout management, and escalation procedures.\n\n**Override Mechanism** encompasses capabilities that allow humans to modify, halt, or redirect agent decisions in real-time, both during the decision-making process and after initial action has begun. These mechanisms provide emergency intervention capabilities and enable humans to maintain ultimate control over agent behavior, even when agents are operating with significant autonomy.\n\n**Explainability Interface** represents a standardized method for agents to provide transparency into their reasoning and decision-making processes. These interfaces enable informed human oversight by presenting agent logic, data sources, confidence levels, and decision rationale in human-comprehensible formats. The explainability interface is essential for meaningful human participation in approval workflows and override decisions.\n\n**Human Oversight** denotes the structured involvement of human operators in monitoring, approving, or modifying AI agent actions. This encompasses both proactive oversight through approval workflows and reactive oversight through monitoring and intervention capabilities. Human oversight requirements may be specified through structured claims and validation patterns as referenced in authorization frameworks [RFC6749] [RFC7519].\n\n**Decision Point** identifies a specific moment in agent execution where human intervention may be required, beneficial, or optionally available. Decision points are defined based on action criticality, risk assessment, regulatory requirements, or organizational policy. Each decision point specifies the type of human involvement required and the mechanisms through which such involvement occurs.\n\n**Intervention Trigger** describes a condition, threshold, or event that automatically activates human-in-the-loop mechanisms. Triggers may be based on risk scores, confidence levels, environmental changes, error conditions, or explicit policy rules. When activated, intervention triggers initiate appropriate HITL primitives to ensure human involvement in agent decision-making processes.", "As AI agents become increasingly prevalent in critical network infrastructure and operational environments, they are often deployed with varying degrees of autonomy and inconsistent mechanisms for human oversight. Current agent implementations typically rely on ad-hoc or proprietary methods for human intervention, ranging from simple logging systems to custom approval interfaces that lack standardization across platforms and vendors. This fragmentation creates significant challenges for organizations attempting to maintain consistent oversight policies across heterogeneous agent deployments, particularly in regulated environments where human accountability is not merely preferred but legally mandated.\n\nThe absence of standardized Human-in-the-Loop (HITL) mechanisms leads to several critical problems in autonomous agent deployment. Without consistent approval workflows, agents may execute high-impact actions without appropriate human review, potentially causing unintended consequences in production systems. The lack of standardized override mechanisms means that human operators cannot reliably intervene when agents begin executing problematic decisions, creating situations where autonomous systems continue operating beyond safe parameters. Furthermore, the absence of explainability interfaces prevents operators from understanding agent reasoning, making it impossible to provide informed oversight or learn from agent behavior patterns. These gaps are particularly problematic in network management contexts, as highlighted in [DRAFT-CUI-NMRG-LLM-NM] and [DRAFT-IRTF-NMRG-LLM-NM], where LLM-generated network decisions require structured human validation before execution.\n\nThe consequences of uncontrolled autonomous operation extend beyond immediate operational risks to encompass regulatory compliance and accountability challenges. In many jurisdictions, regulations require that critical decisions affecting network infrastructure, user data, or service availability maintain clear chains of human responsibility. Current agent systems often operate as \"black boxes\" where the decision-making process is opaque to human oversight, making it difficult or impossible to demonstrate compliance with regulatory requirements. This opacity also hinders incident response and post-mortem analysis, as operators cannot determine why an agent made specific decisions or identify patterns that might prevent future issues.\n\nThe need for human accountability in AI agent systems is further complicated by the temporal aspects of autonomous operation. Unlike traditional software systems that execute predetermined logic, AI agents make dynamic decisions based on environmental conditions and learned behaviors that may not be fully predictable at deployment time. This unpredictability necessitates real-time human oversight capabilities that can adapt to emerging situations. However, without standardized primitives for human intervention, organizations resort to crude mechanisms such as complete system shutdown or manual takeover, which eliminate the benefits of autonomous operation while failing to provide granular control over agent behavior.\n\nThe lack of interoperability between different HITL implementations creates additional operational burden and security risks. Organizations deploying multiple agent systems must maintain separate oversight interfaces and procedures for each platform, increasing complexity and the likelihood of human error. This fragmentation also prevents the development of unified oversight dashboards and centralized approval workflows that could improve operational efficiency while maintaining appropriate human control. The security implications are equally concerning, as non-standardized override mechanisms may lack proper authentication, authorization, and audit capabilities, potentially enabling unauthorized intervention in autonomous systems.", "This document defines a framework of Human-in-the-Loop (HITL) primitives that provide standardized mechanisms for human oversight of AI agent systems. The framework establishes three core primitive categories that work together to ensure appropriate human control over autonomous operations while maintaining operational efficiency. These primitives are designed to be protocol-agnostic and can be integrated with existing agent architectures regardless of the underlying communication protocols or decision-making systems.\n\nThe HITL primitive framework operates on the principle that human oversight requirements vary based on context, risk level, and regulatory constraints. Each primitive category addresses a specific aspect of human-agent interaction: approval workflows ensure human consent for critical actions, override mechanisms provide real-time intervention capabilities, and explainability interfaces enable informed human decision-making. The framework defines standardized interfaces and message formats that allow these primitives to be composed into comprehensive oversight systems tailored to specific operational requirements.\n\nAt the architectural level, HITL primitives integrate with agent systems through well-defined decision points where human intervention may be required. These decision points are identified during agent operation based on configurable triggers such as risk thresholds, regulatory requirements, or operational policies. When a decision point is reached, the appropriate HITL primitive is activated, temporarily suspending autonomous operation until human oversight is satisfied. This approach ensures that human oversight requirements, as referenced in [draft-cui-nmrg-llm-nm] for network management scenarios, can be consistently enforced across diverse agent deployments.\n\nThe framework establishes a common message structure for all HITL primitives that includes essential metadata such as agent identity, decision context, timestamp, and urgency level. This standardized approach enables interoperability between different agent systems and human oversight tools while providing the flexibility to extend primitives for domain-specific requirements. The message structure follows JSON formatting conventions per [RFC8259] and incorporates security considerations including authentication tokens and integrity protection mechanisms as specified in [RFC7519] and [RFC8446].\n\nImplementation of HITL primitives MUST ensure that human oversight mechanisms cannot be bypassed or manipulated by agent systems. The framework requires that primitive activation be deterministic and based on verifiable conditions, preventing agents from selectively avoiding human oversight. Additionally, all HITL interactions MUST be logged with sufficient detail to support audit requirements and accountability frameworks. This logging requirement supports the human oversight validation patterns needed for authorization systems and ensures compliance with regulatory oversight mandates.", "Approval workflow primitives provide standardized mechanisms for requiring explicit human consent before AI agents execute specific actions. These primitives establish a structured framework where agents identify decision points that require human oversight, format approval requests with sufficient context for human evaluation, and await explicit authorization before proceeding. The approval workflow primitive ensures that critical or high-risk agent actions cannot be executed without human validation, maintaining human authority over autonomous systems while preserving operational efficiency through selective intervention points.\n\nThe core approval request structure MUST include the proposed action description, associated risk assessment, relevant context for human evaluation, and expected execution timeline. Each approval request MUST be uniquely identified and include sufficient information for a human operator to make an informed decision. The request format SHOULD follow structured data conventions as defined in [RFC8259] to ensure consistent parsing and presentation across different human interface systems. Agents MUST provide clear rationale for why the action requires approval and include any relevant alternative options that humans may consider during the evaluation process.\n\nHuman responses to approval requests MUST use standardized response codes that clearly indicate approval, denial, or modification instructions. Approved actions receive an explicit authorization token that agents MUST validate before execution, ensuring that only genuinely authorized actions proceed. Denied requests MUST include human feedback when possible to enable agent learning and improved future decision-making. The response format SHOULD accommodate conditional approvals where humans specify constraints or modifications to the proposed action while still granting execution authority.\n\nTimeout handling mechanisms are critical components of approval workflow primitives to prevent system deadlock when human operators are unavailable. Agents MUST implement configurable timeout periods appropriate to the urgency and criticality of the requested action, with default behavior clearly specified for timeout scenarios. When approval requests exceed timeout thresholds, agents SHOULD implement fallback strategies such as escalation to alternate human operators, execution of safe default actions, or graceful degradation of service. The timeout configuration SHOULD be contextually aware, allowing shorter timeouts for routine operations and longer timeouts for complex decisions requiring thorough human evaluation.\n\nIntegration with authentication and authorization systems ensures that approval responses originate from authorized human operators with appropriate privileges for the requested action type. The approval workflow primitive SHOULD leverage existing identity frameworks such as those defined in [RFC6749] and [RFC7519] to validate human operator credentials and maintain audit trails of approval decisions. This integration enables fine-grained access control where different categories of actions require approval from operators with specific roles or clearance levels, supporting organizational hierarchy and responsibility structures within human-agent collaborative systems.", "Override and intervention primitives provide real-time mechanisms that allow human operators to modify, halt, or redirect agent decisions during execution. These primitives are essential for maintaining human control over autonomous systems, particularly in situations where agent decisions may lead to undesirable outcomes or where dynamic conditions require human judgment. The override mechanisms MUST be designed to operate with minimal latency to ensure timely human intervention when required.\n\nThe core override primitive consists of three fundamental operations: halt, modify, and redirect. The halt operation immediately stops agent execution and places the system in a safe state, while the modify operation allows humans to adjust specific parameters or constraints of the current agent decision. The redirect operation enables complete substitution of the agent's proposed action with a human-specified alternative. Each override operation MUST include authentication credentials as defined in [RFC6749] and SHOULD provide a reason code indicating the basis for intervention. Override requests MUST be processed synchronously when possible, with acknowledgment timeouts not exceeding implementation-defined thresholds.\n\nEmergency stop procedures represent a specialized category of override primitives designed for critical situations requiring immediate agent termination. These procedures MUST bypass normal approval workflows and provide direct, low-latency mechanisms for halting agent operations. Emergency stops SHOULD be implemented through multiple redundant channels to ensure reliability, and MUST trigger immediate notification to designated human supervisors. The emergency stop primitive MUST include safeguards to prevent accidental activation while ensuring accessibility during genuine emergencies.\n\nDecision modification interfaces enable fine-grained human adjustment of agent decisions without complete override. These interfaces MUST provide structured formats for specifying modifications to agent parameters, constraints, or objectives using JSON [RFC8259] or equivalent structured data formats. Modification requests SHOULD include validation mechanisms to ensure proposed changes are within acceptable operational bounds. The agent MUST acknowledge modification requests and indicate whether the requested changes can be accommodated within current operational constraints.\n\nReal-time intervention capabilities require agents to expose decision points where human oversight can be effectively applied. Decision points MUST be clearly identified in agent execution flows, with appropriate pause mechanisms that allow human evaluation without timeout penalties. Agents SHOULD provide context information at each decision point, including current state, proposed actions, and confidence levels. The intervention interface MUST support both synchronous and asynchronous human responses, with clear timeout behaviors defined for each interaction mode.\n\nIntegration with agent architectures requires override primitives to maintain compatibility with existing agent communication protocols while providing standardized intervention interfaces. Override mechanisms SHOULD be implemented as middleware components that can intercept agent communications without requiring modification to core agent logic. The primitive framework MUST support distributed scenarios where human operators may be remote from agent execution environments, utilizing secure communication channels as specified in [RFC8446] for all override operations.", "This section defines standardized interfaces that enable AI agents to provide transparency into their reasoning and decision-making processes. Explainability primitives are essential for enabling informed human oversight, as humans cannot effectively supervise agent actions without understanding the underlying rationale. These interfaces MUST provide structured information about agent reasoning in formats that support human comprehension and decision-making.\n\nThe core explainability primitive is the Reasoning Trace, which captures the agent's decision-making process in a structured format. A Reasoning Trace MUST include the following elements: the initial problem or goal statement, key inputs and data sources consulted, reasoning steps taken, alternative options considered, confidence levels for decisions, and any uncertainty or limitations acknowledged by the agent. This trace SHOULD be generated in JSON format [RFC8259] to ensure machine-readable structure while remaining human-interpretable. The trace MUST be available before any approval workflow is initiated, allowing humans to make informed decisions about proposed agent actions.\n\nAgents MUST implement a Context Explanation interface that provides on-demand details about specific aspects of their reasoning. This interface allows human operators to query particular decision points, request elaboration on confidence levels, or explore alternative approaches that were considered but rejected. The interface SHOULD support structured queries using predefined categories such as \"data-sources\", \"assumptions\", \"risk-factors\", and \"alternatives\". Responses MUST be provided in a consistent format that enables both human review and automated analysis for audit purposes.\n\nThe Uncertainty Declaration primitive requires agents to explicitly communicate their confidence levels and known limitations regarding proposed actions. Agents MUST provide quantitative confidence scores where applicable and qualitative uncertainty statements for aspects that cannot be numerically assessed. This primitive is particularly critical in regulated environments where human operators need to understand the reliability of agent recommendations before granting approval, as specified in human oversight requirements frameworks [draft-cui-nmrg-llm-nm].\n\nTo support audit and compliance requirements, explainability interfaces MUST generate persistent explanation records that can be stored and retrieved for later review. These records SHOULD include timestamps, version information for the agent making decisions, and cryptographic signatures to ensure integrity. The explanation data MUST be structured to support automated analysis and pattern detection, enabling organizations to identify trends in agent decision-making and improve oversight processes over time.", "The integration of HITL primitives with existing agent architectures requires careful consideration of both the agent's internal decision-making processes and the external communication protocols used for human interaction. Agent systems MUST implement HITL primitives as composable components that can be inserted into the agent's execution pipeline without requiring fundamental architectural changes. This approach ensures that existing agent deployments can adopt human oversight mechanisms incrementally, maintaining backward compatibility while enhancing human control capabilities.\n\nAgent architectures SHOULD implement HITL primitives through a middleware layer that intercepts agent decisions at configurable decision points. This middleware approach allows the same HITL mechanisms to be applied across different agent types and execution environments. The middleware MUST support protocol-agnostic communication, enabling human oversight through various channels including HTTP-based APIs [RFC9110], WebSocket connections for real-time interaction, or message-oriented protocols. The choice of communication protocol SHOULD be configurable to accommodate different operational environments and human interface requirements.\n\nIntegration with authorization frameworks presents a critical opportunity to enforce human oversight requirements at the system level. Agent architectures SHOULD leverage OAuth 2.0 [RFC6749] scopes and claims to specify when human approval is required for specific actions, building upon human oversight requirement patterns that embed HITL constraints directly into authorization tokens [draft-aap-oauth-profile]. This integration ensures that human oversight requirements are enforced consistently across distributed agent systems and cannot be bypassed by individual agent implementations.\n\nFor network management applications, HITL primitives MUST integrate with existing network management protocols and frameworks while preserving the human-in-the-loop workflows defined for LLM-generated network management decisions [draft-cui-nmrg-llm-nm]. Agent architectures in network domains SHOULD implement decision checkpoints that align with critical network operations, ensuring that configuration changes, policy updates, and topology modifications trigger appropriate human oversight mechanisms. The integration MUST preserve existing network management interfaces while adding human oversight capabilities as an additional validation layer.\n\nThe implementation of HITL primitives SHOULD support both synchronous and asynchronous interaction patterns to accommodate different operational requirements and human availability constraints. Synchronous patterns are appropriate for real-time decision approval, while asynchronous patterns enable human oversight in environments where immediate human response is not feasible. Agent architectures MUST implement timeout mechanisms and fallback behaviors for both interaction patterns, ensuring system stability when human oversight is delayed or unavailable.\n\nConfiguration and policy management for HITL primitives SHOULD be externalized from agent implementations to enable dynamic adjustment of human oversight requirements without agent redeployment. This externalization allows organizations to adjust human oversight policies based on operational conditions, regulatory requirements, or agent performance metrics. The configuration mechanism MUST support fine-grained control over which agent decisions require human oversight, the type of oversight required, and the specific human operators authorized to provide oversight for different decision categories.", "The security of HITL primitives is paramount, as these mechanisms represent critical control points where human authority intersects with autonomous agent operation. Authentication and authorization of human operators MUST be implemented using strong cryptographic methods, with multi-factor authentication RECOMMENDED for high-impact decision points. Human operator credentials SHOULD be managed through established identity frameworks such as OAuth 2.0 [RFC6749] or equivalent, with token-based authentication providing both security and auditability. Organizations MUST implement role-based access controls that ensure only authorized personnel can approve specific types of agent actions, with the principle of least privilege applied to limit human operator permissions to necessary functions only.\n\nProtection against manipulation and spoofing attacks requires robust integrity mechanisms throughout the HITL workflow. All approval requests, human responses, and override commands MUST be cryptographically signed to prevent tampering and ensure non-repudiation. The system MUST validate that approval workflows cannot be bypassed through direct agent-to-agent communication or through exploitation of timing vulnerabilities. Particular attention MUST be paid to preventing replay attacks where previously valid human approvals could be reused inappropriately. Human operators MUST be provided with sufficient context and verification mechanisms to detect potentially malicious approval requests that might be designed to trick humans into approving harmful actions.\n\nSecure communication channels are essential for protecting HITL interactions from eavesdropping and man-in-the-middle attacks. All communication between agents, HITL interfaces, and human operators MUST use transport-layer security equivalent to TLS 1.3 [RFC8446] or stronger. The system MUST implement proper certificate validation and SHOULD use mutual TLS authentication where feasible. Session management for human operators MUST include appropriate timeout mechanisms to prevent unauthorized use of abandoned sessions, with sensitive approval workflows requiring fresh authentication for extended operations.\n\nThe explainability interfaces present unique security challenges, as they must balance transparency with protection of sensitive algorithmic details and operational information. Explanations provided to human operators MUST be sanitized to prevent information disclosure that could be exploited by attackers to reverse-engineer agent decision patterns or identify system vulnerabilities. The system MUST implement access controls that ensure explainability information is only provided to operators with appropriate clearance levels for the specific operational context. Additionally, all HITL interactions MUST be logged with tamper-evident audit trails that include cryptographic checksums and timestamps to ensure accountability and enable post-incident analysis while protecting sensitive operational details from unauthorized disclosure.", "This document introduces several new protocol elements and identifiers that require standardized registration to ensure interoperability across implementations. IANA is requested to establish and maintain registries for HITL primitive types, approval workflow identifiers, and standardized response codes as specified in this section. These registries will enable consistent implementation of human-in-the-loop mechanisms across different agent systems and organizational boundaries.\n\nIANA SHALL establish a new registry titled \"Human-in-the-Loop (HITL) Primitive Types\" to maintain standardized identifiers for the core HITL mechanisms defined in this specification. The registry MUST include entries for \"approval-workflow\", \"override-mechanism\", and \"explainability-interface\" as the initial primitive types, with additional types to be registered through the Specification Required policy as defined in [RFC8126]. Each registry entry MUST include the primitive type identifier, a brief description of its function, and a reference to the defining specification. Registration requests MUST specify how the proposed primitive type differs from existing entries and demonstrate clear utility for human oversight scenarios.\n\nA second registry titled \"HITL Approval Workflow Identifiers\" SHALL be established to maintain standardized workflow patterns for approval primitives. This registry MUST include initial entries for common workflow types such as \"single-approver\", \"multi-stage-approval\", \"consensus-required\", and \"emergency-bypass\", with new workflow identifiers registered under the Specification Required policy. Each workflow identifier entry MUST specify the approval pattern, required participant roles, decision criteria, and timeout handling mechanisms. This registry enables organizations to reference standardized approval patterns while maintaining consistency across different agent deployments.\n\nIANA SHALL create a \"HITL Response Codes\" registry to standardize the status and error codes used in human-in-the-loop communications as defined in Section 5 and Section 6 of this specification. The registry MUST include standard response codes for approval granted (200), approval denied (403), timeout exceeded (408), override initiated (300), and explanation requested (250), following the pattern established by HTTP status codes in [RFC9110]. Additional response codes MAY be registered using the Expert Review policy, with registration requests requiring demonstration of unique semantic meaning not covered by existing codes. The registry MUST specify the numeric code, textual description, applicable primitive types, and any special handling requirements for each response code to ensure consistent interpretation across implementations."]

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


           Human-in-the-Loop (HITL) Primitives for AI Agent Systems
           draft-agent-ecosystem-primitives-for-00

  Abstract

     As AI agents become increasingly autonomous in network operations
     and other critical domains, the need for standardized human
     oversight mechanisms becomes paramount. This document defines a
     framework of Human-in-the-Loop (HITL) primitives that enable
     structured human intervention, approval, and oversight in AI agent
     decision-making processes. The framework provides three core
     primitives: approval workflows that require explicit human consent
     before action execution, override mechanisms that allow humans to
     modify or halt agent decisions, and explainability interfaces that
     provide transparency into agent reasoning. These primitives are
     designed to be protocol-agnostic and can be integrated with
     existing agent architectures to ensure human control over
     autonomous systems. The specification addresses the critical gap
     between fully autonomous AI operation and human accountability
     requirements, particularly in regulated environments where human
     oversight is mandatory. By standardizing these HITL mechanisms,
     organizations can deploy AI agents with appropriate human
     safeguards while maintaining operational efficiency and regulatory
     compliance.

  Status of This Memo

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

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

  Terminology

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

     HITL Primitive
        A standardized mechanism that enables human oversight and
        intervention in AI agent decision-making processes

     Approval Workflow
        A structured process that requires explicit human consent
        before an agent action is executed

     Override Mechanism
        A capability that allows humans to modify, halt, or redirect
        agent decisions in real-time

     Explainability Interface
        A standardized method for agents to provide transparency into
        their reasoning and decision-making processes

     Human Oversight
        The structured involvement of human operators in monitoring,
        approving, or modifying AI agent actions

     Decision Point
        A moment in agent execution where human intervention may be
        required or beneficial

     Intervention Trigger
        A condition or threshold that activates human-in-the-loop
        mechanisms


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement  ...........................................  5
     4.  HITL Primitive Framework  ....................................  6
     5.  Approval Workflow Primitives  ................................  7
     6.  Override and Intervention Primitives  ........................  8
     7.  Explainability and Transparency Primitives  ..................  9
     8.  Integration with Agent Architectures  ........................  10
     9.  Security Considerations  .....................................  11
     10.  IANA Considerations  .........................................  12
     11.  References  ..................................................  13

  1.  Introduction

     The rapid advancement and deployment of AI agents in critical
     network operations and infrastructure management has created an
     urgent need for standardized human oversight mechanisms. As
     documented in [draft-cui-nmrg-llm-nm] and [draft-irtf-nmrg-llm-
     nm], AI agents are increasingly being deployed for network
     management tasks that were traditionally performed by human
     operators. However, the current landscape of AI agent systems
     lacks consistent and interoperable Human-in-the-Loop (HITL)
     mechanisms, creating significant risks for organizations that
     require human accountability and oversight in their autonomous
     systems.

     Current AI agent deployments typically implement ad-hoc or
     proprietary mechanisms for human oversight, if any oversight
     mechanisms exist at all. This inconsistency creates several
     critical problems: organizations cannot easily integrate HITL
     capabilities across different agent systems, human operators lack
     standardized interfaces for agent oversight, and regulatory
     compliance becomes difficult to achieve and demonstrate. The
     absence of standardized HITL primitives means that each agent
     implementation must create its own oversight mechanisms, leading
     to fragmented approaches that cannot interoperate and may have
     significant security or reliability gaps.

     The risks of uncontrolled autonomous operation are particularly
     acute in regulated environments such as financial services,
     healthcare, and critical infrastructure, where human oversight is
     often legally mandated. When AI agents operate without appropriate
     human safeguards, organizations face potential regulatory
     violations, liability issues, and operational failures that could
     have been prevented through proper human oversight. Furthermore,
     the lack of standardized explainability interfaces means that
     human operators often cannot understand or validate agent
     decisions, undermining the effectiveness of any oversight
     mechanisms that do exist.

     This document addresses these challenges by defining a
     comprehensive framework of HITL primitives that can be integrated
     with existing agent architectures in a protocol-agnostic manner.
     The framework builds upon established patterns from OAuth 2.0
     [RFC6749] and JSON Web Tokens [RFC7519] to provide standardized
     mechanisms for approval workflows, override capabilities, and
     explainability interfaces. These primitives are designed to ensure
     that human oversight can be consistently implemented and enforced
     across different AI agent systems while maintaining the
     operational efficiency benefits of autonomous operation.

     The HITL primitive framework specified in this document enables
     organizations to deploy AI agents with appropriate human
     safeguards that meet regulatory requirements and organizational
     policies. By standardizing these mechanisms, the framework
     facilitates interoperability between different agent systems and
     provides a foundation for secure, accountable autonomous
     operations. The primitives are designed to be composable and
     configurable, allowing organizations to implement the level of
     human oversight appropriate for their specific use cases and risk
     tolerance.

  2.  Terminology

     This document uses terminology consistent with [RFC2119] and
     [RFC8174] when describing requirement levels. 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.

     HITL Primitive refers to a standardized, composable mechanism that
     enables structured human oversight and intervention in AI agent
     decision-making processes. These primitives serve as the
     fundamental building blocks for implementing human control over
     autonomous systems and can be combined to create comprehensive
     oversight frameworks tailored to specific operational
     requirements.

     Approval Workflow defines a structured process that requires
     explicit human consent before an agent action is executed. As
     described in [draft-cui-nmrg-llm-nm] and [draft-irtf-nmrg-llm-nm],
     these workflows enforce human control over automated actions by
     creating mandatory checkpoints where agent decisions must receive
     human validation. Approval workflows include mechanisms for
     request formatting, response handling, timeout management, and
     escalation procedures.

     Override Mechanism encompasses capabilities that allow humans to
     modify, halt, or redirect agent decisions in real-time, both
     during the decision-making process and after initial action has
     begun. These mechanisms provide emergency intervention
     capabilities and enable humans to maintain ultimate control over
     agent behavior, even when agents are operating with significant
     autonomy.

     Explainability Interface represents a standardized method for
     agents to provide transparency into their reasoning and decision-
     making processes. These interfaces enable informed human oversight
     by presenting agent logic, data sources, confidence levels, and
     decision rationale in human-comprehensible formats. The
     explainability interface is essential for meaningful human
     participation in approval workflows and override decisions.

     Human Oversight denotes the structured involvement of human
     operators in monitoring, approving, or modifying AI agent actions.
     This encompasses both proactive oversight through approval
     workflows and reactive oversight through monitoring and
     intervention capabilities. Human oversight requirements may be
     specified through structured claims and validation patterns as
     referenced in authorization frameworks [RFC6749] [RFC7519].

     Decision Point identifies a specific moment in agent execution
     where human intervention may be required, beneficial, or
     optionally available. Decision points are defined based on action
     criticality, risk assessment, regulatory requirements, or
     organizational policy. Each decision point specifies the type of
     human involvement required and the mechanisms through which such
     involvement occurs.

     Intervention Trigger describes a condition, threshold, or event
     that automatically activates human-in-the-loop mechanisms.
     Triggers may be based on risk scores, confidence levels,
     environmental changes, error conditions, or explicit policy rules.
     When activated, intervention triggers initiate appropriate HITL
     primitives to ensure human involvement in agent decision-making
     processes.

  3.  Problem Statement

     As AI agents become increasingly prevalent in critical network
     infrastructure and operational environments, they are often
     deployed with varying degrees of autonomy and inconsistent
     mechanisms for human oversight. Current agent implementations
     typically rely on ad-hoc or proprietary methods for human
     intervention, ranging from simple logging systems to custom
     approval interfaces that lack standardization across platforms and
     vendors. This fragmentation creates significant challenges for
     organizations attempting to maintain consistent oversight policies
     across heterogeneous agent deployments, particularly in regulated
     environments where human accountability is not merely preferred
     but legally mandated.

     The absence of standardized Human-in-the-Loop (HITL) mechanisms
     leads to several critical problems in autonomous agent deployment.
     Without consistent approval workflows, agents may execute high-
     impact actions without appropriate human review, potentially
     causing unintended consequences in production systems. The lack of
     standardized override mechanisms means that human operators cannot
     reliably intervene when agents begin executing problematic
     decisions, creating situations where autonomous systems continue
     operating beyond safe parameters. Furthermore, the absence of
     explainability interfaces prevents operators from understanding
     agent reasoning, making it impossible to provide informed
     oversight or learn from agent behavior patterns. These gaps are
     particularly problematic in network management contexts, as
     highlighted in [DRAFT-CUI-NMRG-LLM-NM] and [DRAFT-IRTF-NMRG-LLM-
     NM], where LLM-generated network decisions require structured
     human validation before execution.

     The consequences of uncontrolled autonomous operation extend
     beyond immediate operational risks to encompass regulatory
     compliance and accountability challenges. In many jurisdictions,
     regulations require that critical decisions affecting network
     infrastructure, user data, or service availability maintain clear
     chains of human responsibility. Current agent systems often
     operate as "black boxes" where the decision-making process is
     opaque to human oversight, making it difficult or impossible to
     demonstrate compliance with regulatory requirements. This opacity
     also hinders incident response and post-mortem analysis, as
     operators cannot determine why an agent made specific decisions or
     identify patterns that might prevent future issues.

     The need for human accountability in AI agent systems is further
     complicated by the temporal aspects of autonomous operation.
     Unlike traditional software systems that execute predetermined
     logic, AI agents make dynamic decisions based on environmental
     conditions and learned behaviors that may not be fully predictable
     at deployment time. This unpredictability necessitates real-time
     human oversight capabilities that can adapt to emerging
     situations. However, without standardized primitives for human
     intervention, organizations resort to crude mechanisms such as
     complete system shutdown or manual takeover, which eliminate the
     benefits of autonomous operation while failing to provide granular
     control over agent behavior.

     The lack of interoperability between different HITL
     implementations creates additional operational burden and security
     risks. Organizations deploying multiple agent systems must
     maintain separate oversight interfaces and procedures for each
     platform, increasing complexity and the likelihood of human error.
     This fragmentation also prevents the development of unified
     oversight dashboards and centralized approval workflows that could
     improve operational efficiency while maintaining appropriate human
     control. The security implications are equally concerning, as non-
     standardized override mechanisms may lack proper authentication,
     authorization, and audit capabilities, potentially enabling
     unauthorized intervention in autonomous systems.

  4.  HITL Primitive Framework

     This document defines a framework of Human-in-the-Loop (HITL)
     primitives that provide standardized mechanisms for human
     oversight of AI agent systems. The framework establishes three
     core primitive categories that work together to ensure appropriate
     human control over autonomous operations while maintaining
     operational efficiency. These primitives are designed to be
     protocol-agnostic and can be integrated with existing agent
     architectures regardless of the underlying communication protocols
     or decision-making systems.

     The HITL primitive framework operates on the principle that human
     oversight requirements vary based on context, risk level, and
     regulatory constraints. Each primitive category addresses a
     specific aspect of human-agent interaction: approval workflows
     ensure human consent for critical actions, override mechanisms
     provide real-time intervention capabilities, and explainability
     interfaces enable informed human decision-making. The framework
     defines standardized interfaces and message formats that allow
     these primitives to be composed into comprehensive oversight
     systems tailored to specific operational requirements.

     At the architectural level, HITL primitives integrate with agent
     systems through well-defined decision points where human
     intervention may be required. These decision points are identified
     during agent operation based on configurable triggers such as risk
     thresholds, regulatory requirements, or operational policies. When
     a decision point is reached, the appropriate HITL primitive is
     activated, temporarily suspending autonomous operation until human
     oversight is satisfied. This approach ensures that human oversight
     requirements, as referenced in [draft-cui-nmrg-llm-nm] for network
     management scenarios, can be consistently enforced across diverse
     agent deployments.

     The framework establishes a common message structure for all HITL
     primitives that includes essential metadata such as agent
     identity, decision context, timestamp, and urgency level. This
     standardized approach enables interoperability between different
     agent systems and human oversight tools while providing the
     flexibility to extend primitives for domain-specific requirements.
     The message structure follows JSON formatting conventions per
     [RFC8259] and incorporates security considerations including
     authentication tokens and integrity protection mechanisms as
     specified in [RFC7519] and [RFC8446].

     Implementation of HITL primitives MUST ensure that human oversight
     mechanisms cannot be bypassed or manipulated by agent systems. The
     framework requires that primitive activation be deterministic and
     based on verifiable conditions, preventing agents from selectively
     avoiding human oversight. Additionally, all HITL interactions MUST
     be logged with sufficient detail to support audit requirements and
     accountability frameworks. This logging requirement supports the
     human oversight validation patterns needed for authorization
     systems and ensures compliance with regulatory oversight mandates.

  5.  Approval Workflow Primitives

     Approval workflow primitives provide standardized mechanisms for
     requiring explicit human consent before AI agents execute specific
     actions. These primitives establish a structured framework where
     agents identify decision points that require human oversight,
     format approval requests with sufficient context for human
     evaluation, and await explicit authorization before proceeding.
     The approval workflow primitive ensures that critical or high-risk
     agent actions cannot be executed without human validation,
     maintaining human authority over autonomous systems while
     preserving operational efficiency through selective intervention
     points.

     The core approval request structure MUST include the proposed
     action description, associated risk assessment, relevant context
     for human evaluation, and expected execution timeline. Each
     approval request MUST be uniquely identified and include
     sufficient information for a human operator to make an informed
     decision. The request format SHOULD follow structured data
     conventions as defined in [RFC8259] to ensure consistent parsing
     and presentation across different human interface systems. Agents
     MUST provide clear rationale for why the action requires approval
     and include any relevant alternative options that humans may
     consider during the evaluation process.

     Human responses to approval requests MUST use standardized
     response codes that clearly indicate approval, denial, or
     modification instructions. Approved actions receive an explicit
     authorization token that agents MUST validate before execution,
     ensuring that only genuinely authorized actions proceed. Denied
     requests MUST include human feedback when possible to enable agent
     learning and improved future decision-making. The response format
     SHOULD accommodate conditional approvals where humans specify
     constraints or modifications to the proposed action while still
     granting execution authority.

     Timeout handling mechanisms are critical components of approval
     workflow primitives to prevent system deadlock when human
     operators are unavailable. Agents MUST implement configurable
     timeout periods appropriate to the urgency and criticality of the
     requested action, with default behavior clearly specified for
     timeout scenarios. When approval requests exceed timeout
     thresholds, agents SHOULD implement fallback strategies such as
     escalation to alternate human operators, execution of safe default
     actions, or graceful degradation of service. The timeout
     configuration SHOULD be contextually aware, allowing shorter
     timeouts for routine operations and longer timeouts for complex
     decisions requiring thorough human evaluation.

     Integration with authentication and authorization systems ensures
     that approval responses originate from authorized human operators
     with appropriate privileges for the requested action type. The
     approval workflow primitive SHOULD leverage existing identity
     frameworks such as those defined in [RFC6749] and [RFC7519] to
     validate human operator credentials and maintain audit trails of
     approval decisions. This integration enables fine-grained access
     control where different categories of actions require approval
     from operators with specific roles or clearance levels, supporting
     organizational hierarchy and responsibility structures within
     human-agent collaborative systems.

  6.  Override and Intervention Primitives

     Override and intervention primitives provide real-time mechanisms
     that allow human operators to modify, halt, or redirect agent
     decisions during execution. These primitives are essential for
     maintaining human control over autonomous systems, particularly in
     situations where agent decisions may lead to undesirable outcomes
     or where dynamic conditions require human judgment. The override
     mechanisms MUST be designed to operate with minimal latency to
     ensure timely human intervention when required.

     The core override primitive consists of three fundamental
     operations: halt, modify, and redirect. The halt operation
     immediately stops agent execution and places the system in a safe
     state, while the modify operation allows humans to adjust specific
     parameters or constraints of the current agent decision. The
     redirect operation enables complete substitution of the agent's
     proposed action with a human-specified alternative. Each override
     operation MUST include authentication credentials as defined in
     [RFC6749] and SHOULD provide a reason code indicating the basis
     for intervention. Override requests MUST be processed
     synchronously when possible, with acknowledgment timeouts not
     exceeding implementation-defined thresholds.

     Emergency stop procedures represent a specialized category of
     override primitives designed for critical situations requiring
     immediate agent termination. These procedures MUST bypass normal
     approval workflows and provide direct, low-latency mechanisms for
     halting agent operations. Emergency stops SHOULD be implemented
     through multiple redundant channels to ensure reliability, and
     MUST trigger immediate notification to designated human
     supervisors. The emergency stop primitive MUST include safeguards
     to prevent accidental activation while ensuring accessibility
     during genuine emergencies.

     Decision modification interfaces enable fine-grained human
     adjustment of agent decisions without complete override. These
     interfaces MUST provide structured formats for specifying
     modifications to agent parameters, constraints, or objectives
     using JSON [RFC8259] or equivalent structured data formats.
     Modification requests SHOULD include validation mechanisms to
     ensure proposed changes are within acceptable operational bounds.
     The agent MUST acknowledge modification requests and indicate
     whether the requested changes can be accommodated within current
     operational constraints.

     Real-time intervention capabilities require agents to expose
     decision points where human oversight can be effectively applied.
     Decision points MUST be clearly identified in agent execution
     flows, with appropriate pause mechanisms that allow human
     evaluation without timeout penalties. Agents SHOULD provide
     context information at each decision point, including current
     state, proposed actions, and confidence levels. The intervention
     interface MUST support both synchronous and asynchronous human
     responses, with clear timeout behaviors defined for each
     interaction mode.

     Integration with agent architectures requires override primitives
     to maintain compatibility with existing agent communication
     protocols while providing standardized intervention interfaces.
     Override mechanisms SHOULD be implemented as middleware components
     that can intercept agent communications without requiring
     modification to core agent logic. The primitive framework MUST
     support distributed scenarios where human operators may be remote
     from agent execution environments, utilizing secure communication
     channels as specified in [RFC8446] for all override operations.

  7.  Explainability and Transparency Primitives

     This section defines standardized interfaces that enable AI agents
     to provide transparency into their reasoning and decision-making
     processes. Explainability primitives are essential for enabling
     informed human oversight, as humans cannot effectively supervise
     agent actions without understanding the underlying rationale.
     These interfaces MUST provide structured information about agent
     reasoning in formats that support human comprehension and
     decision-making.

     The core explainability primitive is the Reasoning Trace, which
     captures the agent's decision-making process in a structured
     format. A Reasoning Trace MUST include the following elements: the
     initial problem or goal statement, key inputs and data sources
     consulted, reasoning steps taken, alternative options considered,
     confidence levels for decisions, and any uncertainty or
     limitations acknowledged by the agent. This trace SHOULD be
     generated in JSON format [RFC8259] to ensure machine-readable
     structure while remaining human-interpretable. The trace MUST be
     available before any approval workflow is initiated, allowing
     humans to make informed decisions about proposed agent actions.

     Agents MUST implement a Context Explanation interface that
     provides on-demand details about specific aspects of their
     reasoning. This interface allows human operators to query
     particular decision points, request elaboration on confidence
     levels, or explore alternative approaches that were considered but
     rejected. The interface SHOULD support structured queries using
     predefined categories such as "data-sources", "assumptions",
     "risk-factors", and "alternatives". Responses MUST be provided in
     a consistent format that enables both human review and automated
     analysis for audit purposes.

     The Uncertainty Declaration primitive requires agents to
     explicitly communicate their confidence levels and known
     limitations regarding proposed actions. Agents MUST provide
     quantitative confidence scores where applicable and qualitative
     uncertainty statements for aspects that cannot be numerically
     assessed. This primitive is particularly critical in regulated
     environments where human operators need to understand the
     reliability of agent recommendations before granting approval, as
     specified in human oversight requirements frameworks [draft-cui-
     nmrg-llm-nm].

     To support audit and compliance requirements, explainability
     interfaces MUST generate persistent explanation records that can
     be stored and retrieved for later review. These records SHOULD
     include timestamps, version information for the agent making
     decisions, and cryptographic signatures to ensure integrity. The
     explanation data MUST be structured to support automated analysis
     and pattern detection, enabling organizations to identify trends
     in agent decision-making and improve oversight processes over
     time.

  8.  Integration with Agent Architectures

     The integration of HITL primitives with existing agent
     architectures requires careful consideration of both the agent's
     internal decision-making processes and the external communication
     protocols used for human interaction. Agent systems MUST implement
     HITL primitives as composable components that can be inserted into
     the agent's execution pipeline without requiring fundamental
     architectural changes. This approach ensures that existing agent
     deployments can adopt human oversight mechanisms incrementally,
     maintaining backward compatibility while enhancing human control
     capabilities.

     Agent architectures SHOULD implement HITL primitives through a
     middleware layer that intercepts agent decisions at configurable
     decision points. This middleware approach allows the same HITL
     mechanisms to be applied across different agent types and
     execution environments. The middleware MUST support protocol-
     agnostic communication, enabling human oversight through various
     channels including HTTP-based APIs [RFC9110], WebSocket
     connections for real-time interaction, or message-oriented
     protocols. The choice of communication protocol SHOULD be
     configurable to accommodate different operational environments and
     human interface requirements.

     Integration with authorization frameworks presents a critical
     opportunity to enforce human oversight requirements at the system
     level. Agent architectures SHOULD leverage OAuth 2.0 [RFC6749]
     scopes and claims to specify when human approval is required for
     specific actions, building upon human oversight requirement
     patterns that embed HITL constraints directly into authorization
     tokens [draft-aap-oauth-profile]. This integration ensures that
     human oversight requirements are enforced consistently across
     distributed agent systems and cannot be bypassed by individual
     agent implementations.

     For network management applications, HITL primitives MUST
     integrate with existing network management protocols and
     frameworks while preserving the human-in-the-loop workflows
     defined for LLM-generated network management decisions [draft-cui-
     nmrg-llm-nm]. Agent architectures in network domains SHOULD
     implement decision checkpoints that align with critical network
     operations, ensuring that configuration changes, policy updates,
     and topology modifications trigger appropriate human oversight
     mechanisms. The integration MUST preserve existing network
     management interfaces while adding human oversight capabilities as
     an additional validation layer.

     The implementation of HITL primitives SHOULD support both
     synchronous and asynchronous interaction patterns to accommodate
     different operational requirements and human availability
     constraints. Synchronous patterns are appropriate for real-time
     decision approval, while asynchronous patterns enable human
     oversight in environments where immediate human response is not
     feasible. Agent architectures MUST implement timeout mechanisms
     and fallback behaviors for both interaction patterns, ensuring
     system stability when human oversight is delayed or unavailable.

     Configuration and policy management for HITL primitives SHOULD be
     externalized from agent implementations to enable dynamic
     adjustment of human oversight requirements without agent
     redeployment. This externalization allows organizations to adjust
     human oversight policies based on operational conditions,
     regulatory requirements, or agent performance metrics. The
     configuration mechanism MUST support fine-grained control over
     which agent decisions require human oversight, the type of
     oversight required, and the specific human operators authorized to
     provide oversight for different decision categories.

  9.  Security Considerations

     The security of HITL primitives is paramount, as these mechanisms
     represent critical control points where human authority intersects
     with autonomous agent operation. Authentication and authorization
     of human operators MUST be implemented using strong cryptographic
     methods, with multi-factor authentication RECOMMENDED for high-
     impact decision points. Human operator credentials SHOULD be
     managed through established identity frameworks such as OAuth 2.0
     [RFC6749] or equivalent, with token-based authentication providing
     both security and auditability. Organizations MUST implement role-
     based access controls that ensure only authorized personnel can
     approve specific types of agent actions, with the principle of
     least privilege applied to limit human operator permissions to
     necessary functions only.

     Protection against manipulation and spoofing attacks requires
     robust integrity mechanisms throughout the HITL workflow. All
     approval requests, human responses, and override commands MUST be
     cryptographically signed to prevent tampering and ensure non-
     repudiation. The system MUST validate that approval workflows
     cannot be bypassed through direct agent-to-agent communication or
     through exploitation of timing vulnerabilities. Particular
     attention MUST be paid to preventing replay attacks where
     previously valid human approvals could be reused inappropriately.
     Human operators MUST be provided with sufficient context and
     verification mechanisms to detect potentially malicious approval
     requests that might be designed to trick humans into approving
     harmful actions.

     Secure communication channels are essential for protecting HITL
     interactions from eavesdropping and man-in-the-middle attacks. All
     communication between agents, HITL interfaces, and human operators
     MUST use transport-layer security equivalent to TLS 1.3 [RFC8446]
     or stronger. The system MUST implement proper certificate
     validation and SHOULD use mutual TLS authentication where
     feasible. Session management for human operators MUST include
     appropriate timeout mechanisms to prevent unauthorized use of
     abandoned sessions, with sensitive approval workflows requiring
     fresh authentication for extended operations.

     The explainability interfaces present unique security challenges,
     as they must balance transparency with protection of sensitive
     algorithmic details and operational information. Explanations
     provided to human operators MUST be sanitized to prevent
     information disclosure that could be exploited by attackers to
     reverse-engineer agent decision patterns or identify system
     vulnerabilities. The system MUST implement access controls that
     ensure explainability information is only provided to operators
     with appropriate clearance levels for the specific operational
     context. Additionally, all HITL interactions MUST be logged with
     tamper-evident audit trails that include cryptographic checksums
     and timestamps to ensure accountability and enable post-incident
     analysis while protecting sensitive operational details from
     unauthorized disclosure.

  10.  IANA Considerations

     This document introduces several new protocol elements and
     identifiers that require standardized registration to ensure
     interoperability across implementations. IANA is requested to
     establish and maintain registries for HITL primitive types,
     approval workflow identifiers, and standardized response codes as
     specified in this section. These registries will enable consistent
     implementation of human-in-the-loop mechanisms across different
     agent systems and organizational boundaries.

     IANA SHALL establish a new registry titled "Human-in-the-Loop
     (HITL) Primitive Types" to maintain standardized identifiers for
     the core HITL mechanisms defined in this specification. The
     registry MUST include entries for "approval-workflow", "override-
     mechanism", and "explainability-interface" as the initial
     primitive types, with additional types to be registered through
     the Specification Required policy as defined in [RFC8126]. Each
     registry entry MUST include the primitive type identifier, a brief
     description of its function, and a reference to the defining
     specification. Registration requests MUST specify how the proposed
     primitive type differs from existing entries and demonstrate clear
     utility for human oversight scenarios.

     A second registry titled "HITL Approval Workflow Identifiers"
     SHALL be established to maintain standardized workflow patterns
     for approval primitives. This registry MUST include initial
     entries for common workflow types such as "single-approver",
     "multi-stage-approval", "consensus-required", and "emergency-
     bypass", with new workflow identifiers registered under the
     Specification Required policy. Each workflow identifier entry MUST
     specify the approval pattern, required participant roles, decision
     criteria, and timeout handling mechanisms. This registry enables
     organizations to reference standardized approval patterns while
     maintaining consistency across different agent deployments.

     IANA SHALL create a "HITL Response Codes" registry to standardize
     the status and error codes used in human-in-the-loop
     communications as defined in Section 5 and Section 6 of this
     specification. The registry MUST include standard response codes
     for approval granted (200), approval denied (403), timeout
     exceeded (408), override initiated (300), and explanation
     requested (250), following the pattern established by HTTP status
     codes in [RFC9110]. Additional response codes MAY be registered
     using the Expert Review policy, with registration requests
     requiring demonstration of unique semantic meaning not covered by
     existing codes. The registry MUST specify the numeric code,
     textual description, applicable primitive types, and any special
     handling requirements for each response code to ensure consistent
     interpretation across implementations.

  11.  References

  11.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8446]
           RFC 8446

     [RFC 9110]
           RFC 9110

     [RFC 8259]
           RFC 8259

     [draft-rosenberg-cheq]
           draft-rosenberg-cheq

  11.2.  Informative References

     [RFC 6749]
           RFC 6749

     [RFC 7519]
           RFC 7519

     [draft-cui-nmrg-llm-nm]
           draft-cui-nmrg-llm-nm

     [draft-irtf-nmrg-llm-nm]
           draft-irtf-nmrg-llm-nm

     [draft-rosenberg-aiproto-cheq]
           draft-rosenberg-aiproto-cheq

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

     [draft-cowles-volt]
           draft-cowles-volt

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


  Author's Address

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

rating_json:
  {"s": "Proposes standardized Human-in-the-Loop (HITL) primitives for AI agent systems, defining approval workflows, override mechanisms, and explainability interfaces to ensure human oversight and control in autonomous operations. Addresses the gap between full autonomy and accountability requirements in regulated environments.", "n": 3, "nn": "HITL concepts are established in AI safety; framing as IETF primitives for protocol integration is moderately novel but lacks specific technical innovation beyond conceptual packaging.", "m": 1, "mn": "Very early stage (00 draft); abstract-only submission with no detailed specifications, APIs, protocol bindings, or implementation guidance. Requires substantial development to become actionable.", "o": 4, "on": "Significant overlap with existing AI safety frameworks (oversight, interpretability), human-agent interaction research, and policy/governance discussions; limited differentiation from prior work.", "mo": 2, "mon": "Limited signals of adoption pressure or ecosystem demand for IETF standardization of HITL primitives; unclear if this addresses a genuine interoperability gap versus policy/domain-specific solutions.", "r": 3, "rn": "Relevant to regulated AI deployment and safety, but scope (human-agent interaction, governance) sits at the margins of traditional IETF mission; better suited for application-layer working groups or AI safety consortia.", "c": ["Human-agent interaction", "AI safety/alignment", "Policy/governance", "Autonomous netops"]}


gap_topic: Agent Ecosystem Protocol Bindings
draft_name: draft-ai-agent-rollback-protocol-00
title: Real-Time Agent Rollback Protocol (RARP) for Autonomous Network Operations
family_name: agent-ecosystem
family_role: AEPB
status: draft
created_at: 2026-03-04T00:34:20.287506+00:00
abstract:
  Autonomous agents in network operations environments require the ability to quickly and safely rollback actions when incorrect decisions are made. While existing protocols enable agent communication and coordination, no standardized mechanism exists for distributed rollback operations across heterogeneous agent systems. This document specifies the Real-Time Agent Rollback Protocol (RARP), which provides coordinated rollback mechanisms for autonomous network agents. RARP defines checkpoint creation, rollback initiation procedures, state consistency verification, and cross-domain rollback coordination through agent gateways. The protocol integrates with existing agent communication frameworks and supports both immediate rollback for safety-critical scenarios and delayed rollback for complex distributed operations. RARP enables production deployment of autonomous network operations by providing the safety mechanisms necessary for agent decision reversal across distributed systems.

outline_json:
  {"title": "Real-Time Agent Rollback Protocol (RARP) for Autonomous Network Operations", "abstract": "Autonomous agents in network operations environments require the ability to quickly and safely rollback actions when incorrect decisions are made. While existing protocols enable agent communication and coordination, no standardized mechanism exists for distributed rollback operations across heterogeneous agent systems. This document specifies the Real-Time Agent Rollback Protocol (RARP), which provides coordinated rollback mechanisms for autonomous network agents. RARP defines checkpoint creation, rollback initiation procedures, state consistency verification, and cross-domain rollback coordination through agent gateways. The protocol integrates with existing agent communication frameworks and supports both immediate rollback for safety-critical scenarios and delayed rollback for complex distributed operations. RARP enables production deployment of autonomous network operations by providing the safety mechanisms necessary for agent decision reversal across distributed systems.", "sections": [{"title": "Introduction", "summary": "Introduces the critical need for rollback mechanisms in autonomous network operations and positions RARP within the broader agent ecosystem. Establishes the relationship to existing agent communication and coordination protocols.", "key_ideas": ["Autonomous netops safety requirements", "Integration with existing agent architectures", "Production deployment enablement"]}, {"title": "Terminology", "summary": "Defines key terms used throughout the specification including rollback points, agent transactions, and coordination states. Establishes consistent vocabulary with existing agent protocol drafts.", "key_ideas": ["Rollback point definitions", "Agent transaction semantics", "State consistency terminology"]}, {"title": "Problem Statement", "summary": "Details the specific challenges of agent rollback in distributed network environments including state consistency, cross-domain coordination, and timing constraints. Identifies gaps in current agent communication protocols.", "key_ideas": ["Distributed state consistency challenges", "Cross-domain rollback complexity", "Safety-critical timing requirements"]}, {"title": "RARP Architecture and Components", "summary": "Defines the overall architecture for rollback coordination including rollback coordinators, checkpoint managers, and integration points with existing agent gateways. Specifies hierarchical and distributed deployment models.", "key_ideas": ["Rollback coordinator design", "Checkpoint management architecture", "Gateway integration patterns"]}, {"title": "Checkpoint Creation and Management", "summary": "Specifies protocols for creating, storing, and validating rollback checkpoints across distributed agent systems. Defines checkpoint metadata, storage requirements, and consistency verification procedures.", "key_ideas": ["Checkpoint creation protocols", "Distributed storage mechanisms", "Consistency verification procedures"]}, {"title": "Rollback Initiation and Coordination", "summary": "Details the procedures for initiating rollback operations including trigger conditions, coordination messaging, and conflict resolution. Specifies both immediate and coordinated rollback scenarios.", "key_ideas": ["Rollback trigger mechanisms", "Cross-agent coordination procedures", "Conflict resolution protocols"]}, {"title": "Integration with Existing Agent Protocols", "summary": "Describes how RARP integrates with existing agent communication frameworks and gateway mechanisms. Provides binding specifications for common agent collaboration protocols.", "key_ideas": ["Protocol binding mechanisms", "Gateway integration patterns", "Legacy system compatibility"]}, {"title": "Security Considerations", "summary": "Addresses security implications of rollback operations including authorization, audit trails, and protection against malicious rollback attacks. Considers cross-domain security implications.", "key_ideas": ["Rollback authorization mechanisms", "Audit trail requirements", "Attack vector mitigation"]}, {"title": "IANA Considerations", "summary": "Specifies registry requirements for RARP message types, error codes, and capability identifiers. Details registration procedures for rollback-related protocol parameters.", "key_ideas": ["Message type registrations", "Error code assignments", "Capability identifier space"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 8259", "RFC 6241", "draft-han-rtgwg-agent-gateway-intercomm-framework", "draft-li-dmsc-macp", "draft-fu-nmop-agent-communication-framework"], "informative_refs": ["RFC 8446", "RFC 9000", "RFC 6749", "draft-chuyi-nmrg-ai-agent-network", "draft-jadoon-nmrg-agentic-ai-autonomous-networks", "draft-vandoulas-aidp", "draft-cui-ai-agent-discovery-invocation", "draft-wang-nmrg-magent-im", "draft-cui-nmrg-llm-benchmark", "draft-yue-anima-agent-recovery-networks"], "terminology": {"Rollback Point": "A consistent state snapshot across distributed agents from which rollback operations can be initiated", "Agent Transaction": "A coordinated set of actions performed by one or more agents that can be treated as an atomic unit for rollback purposes", "Rollback Coordinator": "An entity responsible for orchestrating rollback operations across multiple agents and domains", "Checkpoint Consistency": "The property that all agents participating in a rollback point have synchronized their state at the same logical time", "Cross-Domain Rollback": "A rollback operation that spans multiple administrative or protocol domains requiring gateway-mediated coordination", "Immediate Rollback": "A rollback operation initiated without coordination delays for safety-critical scenarios", "Coordinated Rollback": "A rollback operation that requires multi-agent coordination and consensus before execution"}, "target_wg": "nmrg", "intended_status": "standards-track"}

sections_json:
  ["The proliferation of autonomous agents in network operations has introduced unprecedented capabilities for self-healing, optimization, and adaptive management across complex distributed systems. As described in [draft-chuyi-nmrg-ai-agent-network], AI-powered agents can now perform sophisticated reasoning and decision-making across previously isolated network management domains. However, the autonomous nature of these systems introduces a critical challenge: when agents make incorrect decisions or encounter unexpected conditions, there exists no standardized mechanism to safely and efficiently reverse their actions across distributed environments.\n\nCurrent agent communication frameworks, including those specified in [draft-fu-nmop-agent-communication-framework] and [draft-li-dmsc-macp], provide robust mechanisms for agent coordination and message exchange but do not address the fundamental requirement for transaction-like rollback capabilities. While traditional network management protocols such as NETCONF [RFC6241] include rollback mechanisms for configuration changes, these operate within single administrative domains and cannot coordinate complex rollback operations across heterogeneous agent systems spanning multiple domains and protocol layers.\n\nThe Real-Time Agent Rollback Protocol (RARP) addresses this gap by providing a standardized framework for coordinated rollback operations in autonomous network environments. RARP builds upon existing agent communication protocols and extends the cross-domain collaboration mechanisms outlined in [draft-han-rtgwg-agent-gateway-intercomm-framework] to enable rollback coordination through gateway intermediaries. The protocol supports both immediate rollback for safety-critical scenarios where agent actions must be reversed without delay, and coordinated rollback for complex distributed operations requiring multi-agent consensus and state synchronization.\n\nThe architecture defined in this document integrates with existing agent controller coordination mechanisms [draft-jadoon-nmrg-agentic-ai-autonomous-networks] while introducing specialized rollback coordinators and checkpoint managers that operate alongside current agent communication infrastructure. RARP leverages established security frameworks including TLS 1.3 [RFC8446] and OAuth 2.0 [RFC6749] to ensure authenticated and authorized rollback operations across administrative boundaries. By providing these safety mechanisms, RARP enables the production deployment of autonomous network operations with the confidence that agent decisions can be safely reversed when necessary.\n\nThis specification defines the protocol semantics, message formats using JSON [RFC8259] encoding, and integration patterns necessary for implementing RARP across diverse agent ecosystems. 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 uses terminology consistent with existing agent communication and network management protocols. The key words \"MUST\", \"MUST NOT\", \"REQUIRED\", \"SHALL\", \"SHALL NOT\", \"SHOULD\", \"SHOULD NOT\", \"RECOMMENDED\", \"NOT RECOMMENDED\", \"MAY\", and \"OPTIONAL\" in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.\n\nThe following terms are defined for use throughout this specification:\n\n**Agent**: An autonomous software entity capable of making decisions and performing actions in network operations environments, as defined in [draft-fu-nmop-agent-communication-framework]. Agents operate with varying degrees of autonomy and may collaborate through standardized communication protocols.\n\n**Agent Gateway**: A protocol intermediary that enables communication and coordination between agents operating in different administrative domains or using different communication protocols, as specified in [draft-han-rtgwg-agent-gateway-intercomm-framework]. Agent gateways provide protocol translation and policy enforcement for cross-domain agent interactions.\n\n**Agent Transaction**: A coordinated set of actions performed by one or more agents that can be treated as an atomic unit for rollback purposes. Agent transactions may span multiple network devices, protocol domains, or administrative boundaries and maintain consistency properties across distributed operations.\n\n**Checkpoint**: A persistent snapshot of agent state and network configuration that serves as a potential rollback target. Checkpoints contain sufficient information to restore agents and affected network elements to a previously known consistent state.\n\n**Checkpoint Consistency**: The property that all agents participating in a rollback point have synchronized their state at the same logical time. Consistency verification ensures that rollback operations restore the system to a coherent state across all participating entities.\n\n**Checkpoint Manager**: A system component responsible for creating, storing, validating, and managing rollback checkpoints. Checkpoint managers coordinate with agents to capture state snapshots and maintain checkpoint metadata required for rollback operations.\n\n**Coordination State**: The current status of multi-agent collaboration activities, including pending transactions, active rollback operations, and inter-agent dependencies. Coordination states are maintained by rollback coordinators to ensure proper sequencing of rollback operations.\n\n**Cross-Domain Rollback**: A rollback operation that spans multiple administrative or protocol domains requiring gateway-mediated coordination. Cross-domain rollbacks involve additional complexity for authentication, authorization, and state synchronization across domain boundaries.\n\n**Coordinated Rollback**: A rollback operation that requires multi-agent coordination and consensus before execution. Coordinated rollbacks involve explicit agreement protocols to ensure all affected agents participate in the rollback operation and reach consistent post-rollback states.\n\n**Immediate Rollback**: A rollback operation initiated without coordination delays for safety-critical scenarios. Immediate rollbacks prioritize rapid response over coordination completeness and are typically used when network safety or security is at immediate risk.\n\n**Rollback Coordinator**: An entity responsible for orchestrating rollback operations across multiple agents and domains. Rollback coordinators implement the consensus and coordination protocols required for distributed rollback operations and may operate in hierarchical configurations for scalability.\n\n**Rollback Point**: A consistent state snapshot across distributed agents from which rollback operations can be initiated. Rollback points represent verified consistent states that can be safely restored through coordinated agent actions.", "The deployment of autonomous agents in network operations environments introduces fundamental challenges in ensuring operational safety through reliable rollback mechanisms. Current agent communication protocols, including those specified in [draft-fu-nmop-agent-communication-framework] and [draft-han-rtgwg-agent-gateway-intercomm-framework], provide sophisticated mechanisms for agent coordination and cross-domain collaboration but lack standardized approaches for distributed rollback operations. When autonomous agents make incorrect decisions or encounter unexpected failure conditions, the ability to quickly and consistently revert to a known-good state becomes critical for maintaining network stability and service availability.\n\nState consistency across distributed agent systems presents the most significant challenge in implementing effective rollback mechanisms. Unlike traditional centralized systems where rollback operations can be performed atomically, autonomous network agents operate across multiple administrative domains, protocol layers, and time scales as described in [draft-jadoon-nmrg-agentic-ai-autonomous-networks]. Each agent maintains its own local state and interacts with network infrastructure through different interfaces, including NETCONF [RFC6241], RESTful APIs, and proprietary management protocols. Ensuring that all participating agents can synchronously return to a consistent checkpoint state requires sophisticated coordination mechanisms that current agent communication frameworks do not provide. The distributed nature of these systems means that network partitions, communication delays, and partial failures can result in inconsistent rollback states where some agents successfully revert while others remain in post-action states.\n\nCross-domain coordination introduces additional complexity as agents operating in different administrative domains must coordinate rollback operations through gateway intermediaries. The agent gateway framework specified in [draft-han-rtgwg-agent-gateway-intercomm-framework] enables cross-domain agent collaboration but does not address the specific requirements for propagating rollback requests, maintaining checkpoint consistency across domain boundaries, or handling authorization and security constraints in multi-domain rollback scenarios. Different domains may have varying rollback policies, checkpoint retention requirements, and security constraints that must be negotiated and enforced during cross-domain rollback operations. Furthermore, the hierarchical nature of network operations means that rollback decisions made at higher levels may cascade to multiple lower-level domains, requiring sophisticated dependency tracking and coordination protocols.\n\nTiming constraints in network operations environments create additional challenges for rollback protocol design. Safety-critical scenarios, such as security incidents or cascading failures, require immediate rollback capabilities that cannot wait for full distributed coordination to complete. However, immediate rollback operations risk creating inconsistent states if not all participating agents can execute the rollback synchronously. Conversely, complex distributed operations may require coordinated rollback procedures that involve extensive negotiation and validation phases, but network conditions may change during these coordination periods, potentially invalidating the target rollback state. Current agent communication protocols lack mechanisms for expressing these timing constraints and do not provide differentiated handling for immediate versus coordinated rollback scenarios.\n\nExisting agent communication frameworks also lack adequate mechanisms for rollback-specific concerns including checkpoint metadata management, rollback authorization, and audit trail generation. The multi-agent coordination protocols specified in [draft-li-dmsc-macp] provide general coordination primitives but do not address the specific state management requirements for maintaining consistent checkpoint data across distributed systems. Additionally, current protocols do not define standardized approaches for validating checkpoint integrity, handling rollback conflicts when multiple agents attempt simultaneous rollback operations, or providing the detailed audit capabilities required for post-rollback analysis and compliance reporting in production network environments.", "The Real-Time Agent Rollback Protocol architecture is designed to integrate seamlessly with existing autonomous agent infrastructures while providing coordinated rollback capabilities across distributed network operations environments. The architecture follows a layered approach that separates rollback coordination logic from agent-specific implementations, enabling deployment across heterogeneous agent systems. RARP components leverage existing agent communication frameworks defined in [draft-fu-nmop-agent-communication-framework] and integrate with agent gateway mechanisms specified in [draft-han-rtgwg-agent-gateway-intercomm-framework] to provide cross-domain rollback coordination capabilities.\n\nThe core RARP architecture consists of three primary component types: Rollback Coordinators, Checkpoint Managers, and Agent Rollback Interfaces. Rollback Coordinators serve as the orchestration layer for rollback operations and MUST implement coordination protocols for both immediate and delayed rollback scenarios. These coordinators maintain awareness of agent relationships, transaction boundaries, and rollback dependencies across the distributed system. Checkpoint Managers handle the creation, storage, validation, and retrieval of rollback points, implementing consistency verification procedures to ensure distributed state coherence. Agent Rollback Interfaces provide the integration layer between RARP components and existing agent systems, translating rollback operations into agent-specific state restoration procedures while maintaining compatibility with established agent communication protocols.\n\nRARP supports both hierarchical and distributed deployment models to accommodate varying network topologies and administrative requirements. In hierarchical deployments, a primary Rollback Coordinator oversees subordinate coordinators within each administrative domain, providing centralized rollback decision-making while delegating local coordination to domain-specific components. This model aligns with the centralized agent controller coordination patterns described in [draft-jadoon-nmrg-agentic-ai-autonomous-networks] and enables efficient rollback operations across large-scale autonomous network deployments. Distributed deployments eliminate single points of failure by implementing peer-to-peer coordination among Rollback Coordinators, using consensus mechanisms to ensure consistent rollback decisions across all participating domains.\n\nIntegration with existing agent gateway infrastructure enables RARP to operate across heterogeneous agent systems without requiring modifications to established communication protocols. Agent gateways specified in [draft-han-rtgwg-agent-gateway-intercomm-framework] are extended with RARP capability negotiation and rollback message translation functions, allowing rollback coordination between agents using different communication frameworks. The architecture maintains protocol compatibility by implementing rollback operations as extensions to existing agent collaboration protocols rather than replacing established communication mechanisms. This approach ensures that RARP can be incrementally deployed in production environments without disrupting existing agent operations.\n\nThe RARP architecture incorporates checkpoint consistency verification mechanisms that operate independently of agent-specific state representations. Checkpoint Managers implement distributed timestamp synchronization and state validation procedures to ensure that rollback points represent truly consistent distributed states across all participating agents. The architecture supports integration with AI Agent Network systems as described in [draft-chuyi-nmrg-ai-agent-network] by providing rollback interfaces that can reverse automated reasoning and decision-making operations performed by large language model-based agents. Component communication within the RARP architecture utilizes secure transport mechanisms including TLS 1.3 [RFC8446] and QUIC [RFC9000] to ensure rollback coordination messages are protected against tampering and unauthorized access during transmission between distributed components.", "Checkpoint creation in RARP enables autonomous agents to establish consistent state snapshots that serve as restoration points for rollback operations. Agents MUST implement checkpoint creation capabilities that capture both local state information and coordination metadata necessary for distributed rollback operations. The checkpoint creation process involves state serialization, metadata generation, and consistency coordination with peer agents participating in the same logical transaction scope. Agents SHOULD create checkpoints at natural transaction boundaries and MAY create additional checkpoints based on risk assessment algorithms or external triggers.\n\nThe checkpoint data structure MUST include agent state information, transaction identifiers, temporal consistency markers, and dependency relationships with other agents as specified in [draft-han-rtgwg-agent-gateway-intercomm-framework]. Checkpoint metadata MUST conform to the JSON format specified in [RFC8259] and include fields for checkpoint identifier, creation timestamp, agent identifier, transaction scope, dependency list, and integrity verification data. Cross-domain checkpoints MUST additionally include gateway coordination information and domain-specific authorization tokens as defined in [draft-fu-nmop-agent-communication-framework]. The checkpoint identifier MUST be globally unique and SHOULD incorporate both temporal and spatial components to ensure uniqueness across distributed deployments.\n\nCheckpoint storage mechanisms MUST provide durability guarantees appropriate for the operational context and SHOULD implement redundancy strategies to prevent single points of failure. Agents MAY utilize local storage, distributed storage systems, or centralized checkpoint repositories depending on deployment constraints and consistency requirements. Storage implementations MUST support atomic write operations and SHOULD provide integrity verification through cryptographic mechanisms as specified in [RFC8446]. Cross-domain checkpoint storage MUST implement access control mechanisms that respect administrative boundaries while enabling authorized rollback operations.\n\nCheckpoint consistency verification ensures that distributed checkpoints represent a globally consistent state across all participating agents. The consistency verification process MUST implement logical clock synchronization or vector clock mechanisms to establish temporal relationships between distributed checkpoints. Agents MUST validate checkpoint consistency before committing checkpoint data and SHOULD implement timeout mechanisms to handle non-responsive participants. For cross-domain scenarios, consistency verification MUST account for network partitions and administrative policy constraints that may affect coordination capabilities.\n\nCheckpoint lifecycle management encompasses creation, validation, storage, retrieval, and cleanup operations across the distributed agent system. Agents MUST implement checkpoint retention policies that balance storage costs with rollback capability requirements and SHOULD provide configuration mechanisms for policy customization. Checkpoint cleanup operations MUST respect dependency relationships and transaction boundaries to prevent premature deletion of required rollback data. The checkpoint manager component SHOULD implement background processes for checkpoint optimization, compression, and garbage collection to maintain system performance over extended operational periods.", "Rollback operations in RARP are initiated through a well-defined trigger and coordination mechanism that ensures consistent state recovery across distributed agent systems. Rollback initiation can occur through multiple pathways: explicit administrative commands, automated safety triggers when agents detect anomalous conditions, or cascade triggers when dependent agent operations fail. The protocol defines two primary rollback modes - immediate rollback for safety-critical scenarios where rapid state recovery is essential, and coordinated rollback for complex distributed operations requiring multi-agent consensus. All rollback operations MUST specify a target rollback point identifier and include sufficient context information to enable receiving agents to validate the rollback request against their local checkpoint metadata.\n\nThe coordination messaging framework builds upon the Cross-Domain Agent Collaboration Protocol [draft-han-rtgwg-agent-gateway-intercomm-framework] to enable rollback operations across heterogeneous agent systems and administrative boundaries. When a rollback coordinator receives a rollback initiation request, it MUST first validate the requesting entity's authorization and verify that the target rollback point exists across all participating agents. The coordinator then broadcasts a rollback preparation message to all agents within the rollback scope, allowing each agent to perform local consistency checks and report any conflicts or dependencies that might prevent successful rollback. This two-phase approach ensures that rollback operations only proceed when all participating agents can successfully return to the specified rollback point without creating inconsistent intermediate states.\n\nImmediate rollback scenarios bypass the standard coordination phase when safety-critical conditions are detected, such as security breaches or network failures that require rapid remediation. In immediate rollback mode, the rollback coordinator MUST issue rollback execution commands directly to all participating agents without waiting for preparation confirmations, accepting the risk of temporary inconsistency in favor of rapid recovery. Agents receiving immediate rollback commands SHALL prioritize rollback execution over normal operations and SHOULD complete rollback within the time bounds specified in the rollback request. The protocol defines fallback procedures for handling agents that cannot complete immediate rollback operations, including isolation mechanisms to prevent inconsistent agents from affecting the recovered system state.\n\nCoordinated rollback operations involve a more complex multi-phase protocol that ensures consistency across distributed agent systems through explicit consensus mechanisms. Following the preparation phase, agents that successfully validate the rollback request send confirmation messages to the rollback coordinator, while agents that detect conflicts or missing checkpoint data send abort messages with detailed error information. The coordinator implements a configurable consensus policy that determines whether to proceed with rollback based on the responses received - strict consensus requires all agents to confirm, while majority consensus allows rollback to proceed if a sufficient percentage of agents confirm readiness. If consensus is achieved, the coordinator broadcasts commit messages triggering simultaneous rollback execution; if consensus fails, the coordinator issues abort messages and logs the rollback attempt for administrative review.\n\nConflict resolution mechanisms address scenarios where multiple concurrent rollback requests or overlapping rollback scopes create coordination challenges. The protocol employs a priority-based conflict resolution system where rollback requests include priority levels, timestamps, and scope identifiers that enable coordinators to determine precedence when conflicts occur. Higher priority rollback operations, such as security-related rollbacks, automatically supersede lower priority operations, while rollback requests with overlapping scope are serialized based on timestamp ordering. Cross-domain rollback conflicts are resolved through gateway-mediated negotiation procedures that leverage the agent controller coordination mechanisms defined in [draft-jadoon-nmrg-agentic-ai-autonomous-networks] to ensure consistent rollback decisions across administrative boundaries.\n\nThe protocol includes comprehensive error handling and recovery procedures for rollback coordination failures, recognizing that rollback operations themselves may encounter system failures or network partitions. When rollback coordination fails due to network issues or coordinator failures, backup coordinators automatically assume responsibility for completing the rollback operation using persistent coordination state stored during the initial phases. Partial rollback failures, where some agents successfully rollback while others fail, trigger automatic reconciliation procedures that either retry the failed rollback operations or initiate compensating actions to restore system consistency. All rollback coordination activities are logged with sufficient detail to enable post-incident analysis and continuous improvement of rollback procedures in production autonomous network operations environments.", "RARP is designed to integrate seamlessly with existing agent communication frameworks and protocols, leveraging established mechanisms while extending them with rollback-specific capabilities. The protocol operates as an overlay service that can be bound to various underlying agent communication protocols, including those defined in [draft-fu-nmop-agent-communication-framework] and [draft-li-dmsc-macp]. Integration is achieved through protocol-specific binding specifications that map RARP operations to the message formats and coordination mechanisms of the underlying framework. This approach ensures that RARP can be deployed incrementally without requiring wholesale replacement of existing agent infrastructure.\n\nFor cross-domain scenarios, RARP extends the gateway mechanisms defined in [draft-han-rtgwg-agent-gateway-intercomm-framework] to support rollback coordination across administrative boundaries. Agent gateways MUST implement RARP-specific message translation and state synchronization functions when serving as intermediaries for cross-domain rollback operations. The gateway extensions include rollback capability negotiation during agent discovery, checkpoint metadata translation between domains, and coordination of distributed rollback timing. Gateways SHOULD maintain rollback context for active cross-domain agent transactions and MUST participate in checkpoint consistency verification procedures when coordinating multi-domain rollbacks.\n\nRARP bindings for common transport protocols are defined to ensure broad compatibility with existing deployments. For NETCONF-based agent communication [RFC6241], RARP operations are encapsulated within custom RPC operations that extend the base protocol capabilities. HTTP/2 and HTTP/3 [RFC9000] bindings utilize JSON-encoded messages [RFC8259] for rollback coordination, with TLS 1.3 [RFC8446] providing transport security. WebSocket connections MAY be used for real-time rollback notifications in environments requiring low-latency coordination. Each binding specification defines the mapping between RARP primitive operations and the specific message formats and error handling mechanisms of the underlying protocol.\n\nThe integration architecture supports both centralized and distributed coordination models as described in [draft-jadoon-nmrg-agentic-ai-autonomous-networks]. In centralized deployments, a single rollback coordinator interfaces with existing agent controllers to provide system-wide rollback capabilities. Distributed deployments utilize peer-to-peer coordination among agents while maintaining compatibility with hierarchical agent architectures. RARP implementations MUST support capability advertisement through existing agent discovery mechanisms, allowing agents to negotiate rollback support and identify compatible rollback coordinators during system initialization.\n\nAuthentication and authorization for RARP operations leverage existing agent security frameworks where possible. OAuth 2.0 [RFC6749] tokens MAY be used for cross-domain authorization when integrating with web-based agent platforms. The protocol defines extension points for integrating with domain-specific authentication mechanisms while maintaining consistent rollback authorization policies. Implementations SHOULD reuse existing agent identity management infrastructure to minimize operational complexity and ensure consistent security policies across normal operations and rollback scenarios.", "The rollback capabilities provided by RARP introduce several security considerations that must be addressed to ensure safe deployment in production autonomous network environments. Rollback operations inherently involve state manipulation and coordination across distributed systems, creating potential attack vectors that could be exploited to disrupt network operations or gain unauthorized access to sensitive network state information. The cross-domain nature of RARP operations, as described in [draft-han-rtgwg-agent-gateway-intercomm-framework], further amplifies these security concerns by introducing trust boundaries and protocol translation points where security policies may differ.\n\nAuthorization and access control for rollback operations MUST be implemented using strong authentication mechanisms consistent with [RFC8446] for transport-layer security and [RFC6749] for authorization delegation across domains. Each rollback coordinator and participating agent MUST authenticate its identity before initiating or participating in rollback operations. The protocol MUST enforce role-based access control where only authorized entities can initiate rollback operations for specific network domains or agent systems. Cross-domain rollback operations MUST validate authorization chains through gateway intermediaries, ensuring that rollback requests are properly authenticated at each administrative boundary. Emergency or immediate rollback operations SHOULD maintain security requirements while providing expedited authorization paths for safety-critical scenarios.\n\nComprehensive audit trails MUST be maintained for all rollback operations to ensure accountability and enable forensic analysis of network incidents. The audit system MUST record rollback initiation events, participating agents, checkpoint identifiers, authorization decisions, and completion status using tamper-resistant logging mechanisms. These audit records MUST be synchronized across participating domains and stored with sufficient integrity protection to prevent unauthorized modification. The audit trail format SHOULD be compatible with existing network management audit systems and MUST include sufficient detail to reconstruct the sequence of events leading to and following rollback operations.\n\nProtection against malicious rollback attacks requires careful consideration of potential attack vectors including replay attacks, unauthorized rollback initiation, and checkpoint poisoning. The protocol MUST implement sequence numbers and timestamps to prevent replay of rollback messages, with verification of message freshness using techniques consistent with [RFC9000]. Rollback coordinators MUST validate checkpoint integrity before executing rollback operations and SHOULD implement rate limiting to prevent denial-of-service attacks through excessive rollback requests. The protocol MUST detect and mitigate attempts to rollback to compromised or maliciously modified checkpoints through cryptographic verification of checkpoint contents and metadata.\n\nCross-domain security implications require special consideration for trust establishment and security policy coordination between administrative domains. Gateway entities facilitating cross-domain rollback MUST enforce security policy translation and ensure that rollback operations comply with the security requirements of all participating domains. The protocol MUST support security policy negotiation to establish common security parameters for cross-domain rollback operations while maintaining the security standards of the most restrictive participating domain. Inter-domain rollback operations SHOULD implement additional verification steps and MAY require human authorization for operations that could significantly impact network stability across domain boundaries.", "This document requests the creation of several new IANA registries for the Real-Time Agent Rollback Protocol (RARP) and the registration of initial values. The registries are necessary to ensure consistent implementation and interoperability of RARP across different autonomous agent systems and administrative domains. These registries support the protocol's integration with existing agent communication frameworks as defined in [draft-fu-nmop-agent-communication-framework] and cross-domain coordination mechanisms specified in [draft-han-rtgwg-agent-gateway-intercomm-framework].\n\nIANA is requested to create a new registry group titled \"Real-Time Agent Rollback Protocol (RARP) Parameters\" with four sub-registries. The \"RARP Message Types\" registry MUST contain 16-bit unsigned integer values from 0 to 65535, with values 0-255 reserved for IANA allocation and 256-65535 designated for first-come, first-served registration following [RFC8126] guidelines. Initial registrations MUST include: ROLLBACK_REQUEST (1), ROLLBACK_RESPONSE (2), CHECKPOINT_CREATE (3), CHECKPOINT_VALIDATE (4), COORDINATION_INIT (5), and COORDINATION_COMPLETE (6). Each registration requires a message type name, numeric value, description, and reference to this specification or subsequent extensions.\n\nThe \"RARP Error Codes\" registry SHALL use 16-bit unsigned integer values with similar allocation policies. Initial error code registrations MUST include: CHECKPOINT_NOT_FOUND (1001), INSUFFICIENT_PERMISSIONS (1002), ROLLBACK_CONFLICT (1003), CROSS_DOMAIN_FAILURE (1004), STATE_INCONSISTENT (1005), and COORDINATION_TIMEOUT (1006). The \"RARP Capability Identifiers\" registry uses string-based identifiers following the reverse DNS naming convention to prevent namespace collisions. Initial capability identifiers SHOULD include \"rollback.immediate\", \"rollback.coordinated\", \"checkpoint.distributed\", and \"integration.gateway\" to support the core protocol functionality and integration patterns described in this specification.\n\nThe \"RARP Agent Transaction Types\" registry supports the classification and coordination of rollback operations across heterogeneous agent systems. This registry uses string-based identifiers and MUST include initial registrations for \"network.configuration\", \"routing.policy\", \"security.rule\", and \"service.deployment\" to align with common network operations use cases. Registration procedures for all RARP registries MUST require specification of the parameter name, value, description, security considerations if applicable, and reference document. Registrants SHOULD provide interoperability considerations when the parameter affects cross-domain operations or integration with existing protocols such as NETCONF [RFC6241] or agent gateway frameworks.\n\nAll RARP registry entries MUST be subject to expert review for values in the IANA allocation ranges, with designated experts evaluating technical soundness, potential conflicts with existing registrations, and alignment with RARP architectural principles. The expert review process SHALL consider the impact on cross-domain rollback coordination and compatibility with existing agent communication protocols. Registry updates affecting security-sensitive parameters such as authorization capabilities or cross-domain coordination mechanisms require additional security review to ensure consistency with the security considerations outlined in Section 8 of this specification and general security practices for autonomous network operations."]

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


           Real-Time Agent Rollback Protocol (RARP) for Autonomous Network Operations
           draft-agent-ecosystem-agent-rollback-protocol-00

  Abstract

     Autonomous agents in network operations environments require the
     ability to quickly and safely rollback actions when incorrect
     decisions are made. While existing protocols enable agent
     communication and coordination, no standardized mechanism exists
     for distributed rollback operations across heterogeneous agent
     systems. This document specifies the Real-Time Agent Rollback
     Protocol (RARP), which provides coordinated rollback mechanisms
     for autonomous network agents. RARP defines checkpoint creation,
     rollback initiation procedures, state consistency verification,
     and cross-domain rollback coordination through agent gateways. The
     protocol integrates with existing agent communication frameworks
     and supports both immediate rollback for safety-critical scenarios
     and delayed rollback for complex distributed operations. RARP
     enables production deployment of autonomous network operations by
     providing the safety mechanisms necessary for agent decision
     reversal across distributed systems.

  Status of This Memo

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

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

  Terminology

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

     Rollback Point
        A consistent state snapshot across distributed agents from
        which rollback operations can be initiated

     Agent Transaction
        A coordinated set of actions performed by one or more agents
        that can be treated as an atomic unit for rollback purposes

     Rollback Coordinator
        An entity responsible for orchestrating rollback operations
        across multiple agents and domains

     Checkpoint Consistency
        The property that all agents participating in a rollback point
        have synchronized their state at the same logical time

     Cross-Domain Rollback
        A rollback operation that spans multiple administrative or
        protocol domains requiring gateway-mediated coordination

     Immediate Rollback
        A rollback operation initiated without coordination delays for
        safety-critical scenarios

     Coordinated Rollback
        A rollback operation that requires multi-agent coordination and
        consensus before execution


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement  ...........................................  5
     4.  RARP Architecture and Components  ............................  6
     5.  Checkpoint Creation and Management  ..........................  7
     6.  Rollback Initiation and Coordination  ........................  8
     7.  Integration with Existing Agent Protocols  ...................  9
     8.  Security Considerations  .....................................  10
     9.  IANA Considerations  .........................................  11
     10.  References  ..................................................  12

  1.  Introduction

     The proliferation of autonomous agents in network operations has
     introduced unprecedented capabilities for self-healing,
     optimization, and adaptive management across complex distributed
     systems. As described in [draft-chuyi-nmrg-ai-agent-network], AI-
     powered agents can now perform sophisticated reasoning and
     decision-making across previously isolated network management
     domains. However, the autonomous nature of these systems
     introduces a critical challenge: when agents make incorrect
     decisions or encounter unexpected conditions, there exists no
     standardized mechanism to safely and efficiently reverse their
     actions across distributed environments.

     Current agent communication frameworks, including those specified
     in [draft-fu-nmop-agent-communication-framework] and [draft-li-
     dmsc-macp], provide robust mechanisms for agent coordination and
     message exchange but do not address the fundamental requirement
     for transaction-like rollback capabilities. While traditional
     network management protocols such as NETCONF [RFC6241] include
     rollback mechanisms for configuration changes, these operate
     within single administrative domains and cannot coordinate complex
     rollback operations across heterogeneous agent systems spanning
     multiple domains and protocol layers.

     The Real-Time Agent Rollback Protocol (RARP) addresses this gap by
     providing a standardized framework for coordinated rollback
     operations in autonomous network environments. RARP builds upon
     existing agent communication protocols and extends the cross-
     domain collaboration mechanisms outlined in [draft-han-rtgwg-
     agent-gateway-intercomm-framework] to enable rollback coordination
     through gateway intermediaries. The protocol supports both
     immediate rollback for safety-critical scenarios where agent
     actions must be reversed without delay, and coordinated rollback
     for complex distributed operations requiring multi-agent consensus
     and state synchronization.

     The architecture defined in this document integrates with existing
     agent controller coordination mechanisms [draft-jadoon-nmrg-
     agentic-ai-autonomous-networks] while introducing specialized
     rollback coordinators and checkpoint managers that operate
     alongside current agent communication infrastructure. RARP
     leverages established security frameworks including TLS 1.3
     [RFC8446] and OAuth 2.0 [RFC6749] to ensure authenticated and
     authorized rollback operations across administrative boundaries.
     By providing these safety mechanisms, RARP enables the production
     deployment of autonomous network operations with the confidence
     that agent decisions can be safely reversed when necessary.

     This specification defines the protocol semantics, message formats
     using JSON [RFC8259] encoding, and integration patterns necessary
     for implementing RARP across diverse agent ecosystems. 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.

  2.  Terminology

     This document uses terminology consistent with existing agent
     communication and network management 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.

     The following terms are defined for use throughout this
     specification:

     Agent: An autonomous software entity capable of making decisions
     and performing actions in network operations environments, as
     defined in [draft-fu-nmop-agent-communication-framework]. Agents
     operate with varying degrees of autonomy and may collaborate
     through standardized communication protocols.

     Agent Gateway: A protocol intermediary that enables communication
     and coordination between agents operating in different
     administrative domains or using different communication protocols,
     as specified in [draft-han-rtgwg-agent-gateway-intercomm-
     framework]. Agent gateways provide protocol translation and policy
     enforcement for cross-domain agent interactions.

     Agent Transaction: A coordinated set of actions performed by one
     or more agents that can be treated as an atomic unit for rollback
     purposes. Agent transactions may span multiple network devices,
     protocol domains, or administrative boundaries and maintain
     consistency properties across distributed operations.

     Checkpoint: A persistent snapshot of agent state and network
     configuration that serves as a potential rollback target.
     Checkpoints contain sufficient information to restore agents and
     affected network elements to a previously known consistent state.

     Checkpoint Consistency: The property that all agents participating
     in a rollback point have synchronized their state at the same
     logical time. Consistency verification ensures that rollback
     operations restore the system to a coherent state across all
     participating entities.

     Checkpoint Manager: A system component responsible for creating,
     storing, validating, and managing rollback checkpoints. Checkpoint
     managers coordinate with agents to capture state snapshots and
     maintain checkpoint metadata required for rollback operations.

     Coordination State: The current status of multi-agent
     collaboration activities, including pending transactions, active
     rollback operations, and inter-agent dependencies. Coordination
     states are maintained by rollback coordinators to ensure proper
     sequencing of rollback operations.

     Cross-Domain Rollback: A rollback operation that spans multiple
     administrative or protocol domains requiring gateway-mediated
     coordination. Cross-domain rollbacks involve additional complexity
     for authentication, authorization, and state synchronization
     across domain boundaries.

     Coordinated Rollback: A rollback operation that requires multi-
     agent coordination and consensus before execution. Coordinated
     rollbacks involve explicit agreement protocols to ensure all
     affected agents participate in the rollback operation and reach
     consistent post-rollback states.

     Immediate Rollback: A rollback operation initiated without
     coordination delays for safety-critical scenarios. Immediate
     rollbacks prioritize rapid response over coordination completeness
     and are typically used when network safety or security is at
     immediate risk.

     Rollback Coordinator: An entity responsible for orchestrating
     rollback operations across multiple agents and domains. Rollback
     coordinators implement the consensus and coordination protocols
     required for distributed rollback operations and may operate in
     hierarchical configurations for scalability.

     Rollback Point: A consistent state snapshot across distributed
     agents from which rollback operations can be initiated. Rollback
     points represent verified consistent states that can be safely
     restored through coordinated agent actions.

  3.  Problem Statement

     The deployment of autonomous agents in network operations
     environments introduces fundamental challenges in ensuring
     operational safety through reliable rollback mechanisms. Current
     agent communication protocols, including those specified in
     [draft-fu-nmop-agent-communication-framework] and [draft-han-
     rtgwg-agent-gateway-intercomm-framework], provide sophisticated
     mechanisms for agent coordination and cross-domain collaboration
     but lack standardized approaches for distributed rollback
     operations. When autonomous agents make incorrect decisions or
     encounter unexpected failure conditions, the ability to quickly
     and consistently revert to a known-good state becomes critical for
     maintaining network stability and service availability.

     State consistency across distributed agent systems presents the
     most significant challenge in implementing effective rollback
     mechanisms. Unlike traditional centralized systems where rollback
     operations can be performed atomically, autonomous network agents
     operate across multiple administrative domains, protocol layers,
     and time scales as described in [draft-jadoon-nmrg-agentic-ai-
     autonomous-networks]. Each agent maintains its own local state and
     interacts with network infrastructure through different
     interfaces, including NETCONF [RFC6241], RESTful APIs, and
     proprietary management protocols. Ensuring that all participating
     agents can synchronously return to a consistent checkpoint state
     requires sophisticated coordination mechanisms that current agent
     communication frameworks do not provide. The distributed nature of
     these systems means that network partitions, communication delays,
     and partial failures can result in inconsistent rollback states
     where some agents successfully revert while others remain in post-
     action states.

     Cross-domain coordination introduces additional complexity as
     agents operating in different administrative domains must
     coordinate rollback operations through gateway intermediaries. The
     agent gateway framework specified in [draft-han-rtgwg-agent-
     gateway-intercomm-framework] enables cross-domain agent
     collaboration but does not address the specific requirements for
     propagating rollback requests, maintaining checkpoint consistency
     across domain boundaries, or handling authorization and security
     constraints in multi-domain rollback scenarios. Different domains
     may have varying rollback policies, checkpoint retention
     requirements, and security constraints that must be negotiated and
     enforced during cross-domain rollback operations. Furthermore, the
     hierarchical nature of network operations means that rollback
     decisions made at higher levels may cascade to multiple lower-
     level domains, requiring sophisticated dependency tracking and
     coordination protocols.

     Timing constraints in network operations environments create
     additional challenges for rollback protocol design. Safety-
     critical scenarios, such as security incidents or cascading
     failures, require immediate rollback capabilities that cannot wait
     for full distributed coordination to complete. However, immediate
     rollback operations risk creating inconsistent states if not all
     participating agents can execute the rollback synchronously.
     Conversely, complex distributed operations may require coordinated
     rollback procedures that involve extensive negotiation and
     validation phases, but network conditions may change during these
     coordination periods, potentially invalidating the target rollback
     state. Current agent communication protocols lack mechanisms for
     expressing these timing constraints and do not provide
     differentiated handling for immediate versus coordinated rollback
     scenarios.

     Existing agent communication frameworks also lack adequate
     mechanisms for rollback-specific concerns including checkpoint
     metadata management, rollback authorization, and audit trail
     generation. The multi-agent coordination protocols specified in
     [draft-li-dmsc-macp] provide general coordination primitives but
     do not address the specific state management requirements for
     maintaining consistent checkpoint data across distributed systems.
     Additionally, current protocols do not define standardized
     approaches for validating checkpoint integrity, handling rollback
     conflicts when multiple agents attempt simultaneous rollback
     operations, or providing the detailed audit capabilities required
     for post-rollback analysis and compliance reporting in production
     network environments.

  4.  RARP Architecture and Components

     The Real-Time Agent Rollback Protocol architecture is designed to
     integrate seamlessly with existing autonomous agent
     infrastructures while providing coordinated rollback capabilities
     across distributed network operations environments. The
     architecture follows a layered approach that separates rollback
     coordination logic from agent-specific implementations, enabling
     deployment across heterogeneous agent systems. RARP components
     leverage existing agent communication frameworks defined in
     [draft-fu-nmop-agent-communication-framework] and integrate with
     agent gateway mechanisms specified in [draft-han-rtgwg-agent-
     gateway-intercomm-framework] to provide cross-domain rollback
     coordination capabilities.

     The core RARP architecture consists of three primary component
     types: Rollback Coordinators, Checkpoint Managers, and Agent
     Rollback Interfaces. Rollback Coordinators serve as the
     orchestration layer for rollback operations and MUST implement
     coordination protocols for both immediate and delayed rollback
     scenarios. These coordinators maintain awareness of agent
     relationships, transaction boundaries, and rollback dependencies
     across the distributed system. Checkpoint Managers handle the
     creation, storage, validation, and retrieval of rollback points,
     implementing consistency verification procedures to ensure
     distributed state coherence. Agent Rollback Interfaces provide the
     integration layer between RARP components and existing agent
     systems, translating rollback operations into agent-specific state
     restoration procedures while maintaining compatibility with
     established agent communication protocols.

     RARP supports both hierarchical and distributed deployment models
     to accommodate varying network topologies and administrative
     requirements. In hierarchical deployments, a primary Rollback
     Coordinator oversees subordinate coordinators within each
     administrative domain, providing centralized rollback decision-
     making while delegating local coordination to domain-specific
     components. This model aligns with the centralized agent
     controller coordination patterns described in [draft-jadoon-nmrg-
     agentic-ai-autonomous-networks] and enables efficient rollback
     operations across large-scale autonomous network deployments.
     Distributed deployments eliminate single points of failure by
     implementing peer-to-peer coordination among Rollback
     Coordinators, using consensus mechanisms to ensure consistent
     rollback decisions across all participating domains.

     Integration with existing agent gateway infrastructure enables
     RARP to operate across heterogeneous agent systems without
     requiring modifications to established communication protocols.
     Agent gateways specified in [draft-han-rtgwg-agent-gateway-
     intercomm-framework] are extended with RARP capability negotiation
     and rollback message translation functions, allowing rollback
     coordination between agents using different communication
     frameworks. The architecture maintains protocol compatibility by
     implementing rollback operations as extensions to existing agent
     collaboration protocols rather than replacing established
     communication mechanisms. This approach ensures that RARP can be
     incrementally deployed in production environments without
     disrupting existing agent operations.

     The RARP architecture incorporates checkpoint consistency
     verification mechanisms that operate independently of agent-
     specific state representations. Checkpoint Managers implement
     distributed timestamp synchronization and state validation
     procedures to ensure that rollback points represent truly
     consistent distributed states across all participating agents. The
     architecture supports integration with AI Agent Network systems as
     described in [draft-chuyi-nmrg-ai-agent-network] by providing
     rollback interfaces that can reverse automated reasoning and
     decision-making operations performed by large language model-based
     agents. Component communication within the RARP architecture
     utilizes secure transport mechanisms including TLS 1.3 [RFC8446]
     and QUIC [RFC9000] to ensure rollback coordination messages are
     protected against tampering and unauthorized access during
     transmission between distributed components.

  5.  Checkpoint Creation and Management

     Checkpoint creation in RARP enables autonomous agents to establish
     consistent state snapshots that serve as restoration points for
     rollback operations. Agents MUST implement checkpoint creation
     capabilities that capture both local state information and
     coordination metadata necessary for distributed rollback
     operations. The checkpoint creation process involves state
     serialization, metadata generation, and consistency coordination
     with peer agents participating in the same logical transaction
     scope. Agents SHOULD create checkpoints at natural transaction
     boundaries and MAY create additional checkpoints based on risk
     assessment algorithms or external triggers.

     The checkpoint data structure MUST include agent state
     information, transaction identifiers, temporal consistency
     markers, and dependency relationships with other agents as
     specified in [draft-han-rtgwg-agent-gateway-intercomm-framework].
     Checkpoint metadata MUST conform to the JSON format specified in
     [RFC8259] and include fields for checkpoint identifier, creation
     timestamp, agent identifier, transaction scope, dependency list,
     and integrity verification data. Cross-domain checkpoints MUST
     additionally include gateway coordination information and domain-
     specific authorization tokens as defined in [draft-fu-nmop-agent-
     communication-framework]. The checkpoint identifier MUST be
     globally unique and SHOULD incorporate both temporal and spatial
     components to ensure uniqueness across distributed deployments.

     Checkpoint storage mechanisms MUST provide durability guarantees
     appropriate for the operational context and SHOULD implement
     redundancy strategies to prevent single points of failure. Agents
     MAY utilize local storage, distributed storage systems, or
     centralized checkpoint repositories depending on deployment
     constraints and consistency requirements. Storage implementations
     MUST support atomic write operations and SHOULD provide integrity
     verification through cryptographic mechanisms as specified in
     [RFC8446]. Cross-domain checkpoint storage MUST implement access
     control mechanisms that respect administrative boundaries while
     enabling authorized rollback operations.

     Checkpoint consistency verification ensures that distributed
     checkpoints represent a globally consistent state across all
     participating agents. The consistency verification process MUST
     implement logical clock synchronization or vector clock mechanisms
     to establish temporal relationships between distributed
     checkpoints. Agents MUST validate checkpoint consistency before
     committing checkpoint data and SHOULD implement timeout mechanisms
     to handle non-responsive participants. For cross-domain scenarios,
     consistency verification MUST account for network partitions and
     administrative policy constraints that may affect coordination
     capabilities.

     Checkpoint lifecycle management encompasses creation, validation,
     storage, retrieval, and cleanup operations across the distributed
     agent system. Agents MUST implement checkpoint retention policies
     that balance storage costs with rollback capability requirements
     and SHOULD provide configuration mechanisms for policy
     customization. Checkpoint cleanup operations MUST respect
     dependency relationships and transaction boundaries to prevent
     premature deletion of required rollback data. The checkpoint
     manager component SHOULD implement background processes for
     checkpoint optimization, compression, and garbage collection to
     maintain system performance over extended operational periods.

  6.  Rollback Initiation and Coordination

     Rollback operations in RARP are initiated through a well-defined
     trigger and coordination mechanism that ensures consistent state
     recovery across distributed agent systems. Rollback initiation can
     occur through multiple pathways: explicit administrative commands,
     automated safety triggers when agents detect anomalous conditions,
     or cascade triggers when dependent agent operations fail. The
     protocol defines two primary rollback modes - immediate rollback
     for safety-critical scenarios where rapid state recovery is
     essential, and coordinated rollback for complex distributed
     operations requiring multi-agent consensus. All rollback
     operations MUST specify a target rollback point identifier and
     include sufficient context information to enable receiving agents
     to validate the rollback request against their local checkpoint
     metadata.

     The coordination messaging framework builds upon the Cross-Domain
     Agent Collaboration Protocol [draft-han-rtgwg-agent-gateway-
     intercomm-framework] to enable rollback operations across
     heterogeneous agent systems and administrative boundaries. When a
     rollback coordinator receives a rollback initiation request, it
     MUST first validate the requesting entity's authorization and
     verify that the target rollback point exists across all
     participating agents. The coordinator then broadcasts a rollback
     preparation message to all agents within the rollback scope,
     allowing each agent to perform local consistency checks and report
     any conflicts or dependencies that might prevent successful
     rollback. This two-phase approach ensures that rollback operations
     only proceed when all participating agents can successfully return
     to the specified rollback point without creating inconsistent
     intermediate states.

     Immediate rollback scenarios bypass the standard coordination
     phase when safety-critical conditions are detected, such as
     security breaches or network failures that require rapid
     remediation. In immediate rollback mode, the rollback coordinator
     MUST issue rollback execution commands directly to all
     participating agents without waiting for preparation
     confirmations, accepting the risk of temporary inconsistency in
     favor of rapid recovery. Agents receiving immediate rollback
     commands SHALL prioritize rollback execution over normal
     operations and SHOULD complete rollback within the time bounds
     specified in the rollback request. The protocol defines fallback
     procedures for handling agents that cannot complete immediate
     rollback operations, including isolation mechanisms to prevent
     inconsistent agents from affecting the recovered system state.

     Coordinated rollback operations involve a more complex multi-phase
     protocol that ensures consistency across distributed agent systems
     through explicit consensus mechanisms. Following the preparation
     phase, agents that successfully validate the rollback request send
     confirmation messages to the rollback coordinator, while agents
     that detect conflicts or missing checkpoint data send abort
     messages with detailed error information. The coordinator
     implements a configurable consensus policy that determines whether
     to proceed with rollback based on the responses received - strict
     consensus requires all agents to confirm, while majority consensus
     allows rollback to proceed if a sufficient percentage of agents
     confirm readiness. If consensus is achieved, the coordinator
     broadcasts commit messages triggering simultaneous rollback
     execution; if consensus fails, the coordinator issues abort
     messages and logs the rollback attempt for administrative review.

     Conflict resolution mechanisms address scenarios where multiple
     concurrent rollback requests or overlapping rollback scopes create
     coordination challenges. The protocol employs a priority-based
     conflict resolution system where rollback requests include
     priority levels, timestamps, and scope identifiers that enable
     coordinators to determine precedence when conflicts occur. Higher
     priority rollback operations, such as security-related rollbacks,
     automatically supersede lower priority operations, while rollback
     requests with overlapping scope are serialized based on timestamp
     ordering. Cross-domain rollback conflicts are resolved through
     gateway-mediated negotiation procedures that leverage the agent
     controller coordination mechanisms defined in [draft-jadoon-nmrg-
     agentic-ai-autonomous-networks] to ensure consistent rollback
     decisions across administrative boundaries.

     The protocol includes comprehensive error handling and recovery
     procedures for rollback coordination failures, recognizing that
     rollback operations themselves may encounter system failures or
     network partitions. When rollback coordination fails due to
     network issues or coordinator failures, backup coordinators
     automatically assume responsibility for completing the rollback
     operation using persistent coordination state stored during the
     initial phases. Partial rollback failures, where some agents
     successfully rollback while others fail, trigger automatic
     reconciliation procedures that either retry the failed rollback
     operations or initiate compensating actions to restore system
     consistency. All rollback coordination activities are logged with
     sufficient detail to enable post-incident analysis and continuous
     improvement of rollback procedures in production autonomous
     network operations environments.

  7.  Integration with Existing Agent Protocols

     RARP is designed to integrate seamlessly with existing agent
     communication frameworks and protocols, leveraging established
     mechanisms while extending them with rollback-specific
     capabilities. The protocol operates as an overlay service that can
     be bound to various underlying agent communication protocols,
     including those defined in [draft-fu-nmop-agent-communication-
     framework] and [draft-li-dmsc-macp]. Integration is achieved
     through protocol-specific binding specifications that map RARP
     operations to the message formats and coordination mechanisms of
     the underlying framework. This approach ensures that RARP can be
     deployed incrementally without requiring wholesale replacement of
     existing agent infrastructure.

     For cross-domain scenarios, RARP extends the gateway mechanisms
     defined in [draft-han-rtgwg-agent-gateway-intercomm-framework] to
     support rollback coordination across administrative boundaries.
     Agent gateways MUST implement RARP-specific message translation
     and state synchronization functions when serving as intermediaries
     for cross-domain rollback operations. The gateway extensions
     include rollback capability negotiation during agent discovery,
     checkpoint metadata translation between domains, and coordination
     of distributed rollback timing. Gateways SHOULD maintain rollback
     context for active cross-domain agent transactions and MUST
     participate in checkpoint consistency verification procedures when
     coordinating multi-domain rollbacks.

     RARP bindings for common transport protocols are defined to ensure
     broad compatibility with existing deployments. For NETCONF-based
     agent communication [RFC6241], RARP operations are encapsulated
     within custom RPC operations that extend the base protocol
     capabilities. HTTP/2 and HTTP/3 [RFC9000] bindings utilize JSON-
     encoded messages [RFC8259] for rollback coordination, with TLS 1.3
     [RFC8446] providing transport security. WebSocket connections MAY
     be used for real-time rollback notifications in environments
     requiring low-latency coordination. Each binding specification
     defines the mapping between RARP primitive operations and the
     specific message formats and error handling mechanisms of the
     underlying protocol.

     The integration architecture supports both centralized and
     distributed coordination models as described in [draft-jadoon-
     nmrg-agentic-ai-autonomous-networks]. In centralized deployments,
     a single rollback coordinator interfaces with existing agent
     controllers to provide system-wide rollback capabilities.
     Distributed deployments utilize peer-to-peer coordination among
     agents while maintaining compatibility with hierarchical agent
     architectures. RARP implementations MUST support capability
     advertisement through existing agent discovery mechanisms,
     allowing agents to negotiate rollback support and identify
     compatible rollback coordinators during system initialization.

     Authentication and authorization for RARP operations leverage
     existing agent security frameworks where possible. OAuth 2.0
     [RFC6749] tokens MAY be used for cross-domain authorization when
     integrating with web-based agent platforms. The protocol defines
     extension points for integrating with domain-specific
     authentication mechanisms while maintaining consistent rollback
     authorization policies. Implementations SHOULD reuse existing
     agent identity management infrastructure to minimize operational
     complexity and ensure consistent security policies across normal
     operations and rollback scenarios.

  8.  Security Considerations

     The rollback capabilities provided by RARP introduce several
     security considerations that must be addressed to ensure safe
     deployment in production autonomous network environments. Rollback
     operations inherently involve state manipulation and coordination
     across distributed systems, creating potential attack vectors that
     could be exploited to disrupt network operations or gain
     unauthorized access to sensitive network state information. The
     cross-domain nature of RARP operations, as described in [draft-
     han-rtgwg-agent-gateway-intercomm-framework], further amplifies
     these security concerns by introducing trust boundaries and
     protocol translation points where security policies may differ.

     Authorization and access control for rollback operations MUST be
     implemented using strong authentication mechanisms consistent with
     [RFC8446] for transport-layer security and [RFC6749] for
     authorization delegation across domains. Each rollback coordinator
     and participating agent MUST authenticate its identity before
     initiating or participating in rollback operations. The protocol
     MUST enforce role-based access control where only authorized
     entities can initiate rollback operations for specific network
     domains or agent systems. Cross-domain rollback operations MUST
     validate authorization chains through gateway intermediaries,
     ensuring that rollback requests are properly authenticated at each
     administrative boundary. Emergency or immediate rollback
     operations SHOULD maintain security requirements while providing
     expedited authorization paths for safety-critical scenarios.

     Comprehensive audit trails MUST be maintained for all rollback
     operations to ensure accountability and enable forensic analysis
     of network incidents. The audit system MUST record rollback
     initiation events, participating agents, checkpoint identifiers,
     authorization decisions, and completion status using tamper-
     resistant logging mechanisms. These audit records MUST be
     synchronized across participating domains and stored with
     sufficient integrity protection to prevent unauthorized
     modification. The audit trail format SHOULD be compatible with
     existing network management audit systems and MUST include
     sufficient detail to reconstruct the sequence of events leading to
     and following rollback operations.

     Protection against malicious rollback attacks requires careful
     consideration of potential attack vectors including replay
     attacks, unauthorized rollback initiation, and checkpoint
     poisoning. The protocol MUST implement sequence numbers and
     timestamps to prevent replay of rollback messages, with
     verification of message freshness using techniques consistent with
     [RFC9000]. Rollback coordinators MUST validate checkpoint
     integrity before executing rollback operations and SHOULD
     implement rate limiting to prevent denial-of-service attacks
     through excessive rollback requests. The protocol MUST detect and
     mitigate attempts to rollback to compromised or maliciously
     modified checkpoints through cryptographic verification of
     checkpoint contents and metadata.

     Cross-domain security implications require special consideration
     for trust establishment and security policy coordination between
     administrative domains. Gateway entities facilitating cross-domain
     rollback MUST enforce security policy translation and ensure that
     rollback operations comply with the security requirements of all
     participating domains. The protocol MUST support security policy
     negotiation to establish common security parameters for cross-
     domain rollback operations while maintaining the security
     standards of the most restrictive participating domain. Inter-
     domain rollback operations SHOULD implement additional
     verification steps and MAY require human authorization for
     operations that could significantly impact network stability
     across domain boundaries.

  9.  IANA Considerations

     This document requests the creation of several new IANA registries
     for the Real-Time Agent Rollback Protocol (RARP) and the
     registration of initial values. The registries are necessary to
     ensure consistent implementation and interoperability of RARP
     across different autonomous agent systems and administrative
     domains. These registries support the protocol's integration with
     existing agent communication frameworks as defined in [draft-fu-
     nmop-agent-communication-framework] and cross-domain coordination
     mechanisms specified in [draft-han-rtgwg-agent-gateway-intercomm-
     framework].

     IANA is requested to create a new registry group titled "Real-Time
     Agent Rollback Protocol (RARP) Parameters" with four sub-
     registries. The "RARP Message Types" registry MUST contain 16-bit
     unsigned integer values from 0 to 65535, with values 0-255
     reserved for IANA allocation and 256-65535 designated for first-
     come, first-served registration following [RFC8126] guidelines.
     Initial registrations MUST include: ROLLBACKREQUEST (1),
     ROLLBACKRESPONSE (2), CHECKPOINTCREATE (3), CHECKPOINTVALIDATE
     (4), COORDINATIONINIT (5), and COORDINATIONCOMPLETE (6). Each
     registration requires a message type name, numeric value,
     description, and reference to this specification or subsequent
     extensions.

     The "RARP Error Codes" registry SHALL use 16-bit unsigned integer
     values with similar allocation policies. Initial error code
     registrations MUST include: CHECKPOINTNOTFOUND (1001),
     INSUFFICIENTPERMISSIONS (1002), ROLLBACKCONFLICT (1003),
     CROSSDOMAINFAILURE (1004), STATEINCONSISTENT (1005), and
     COORDINATIONTIMEOUT (1006). The "RARP Capability Identifiers"
     registry uses string-based identifiers following the reverse DNS
     naming convention to prevent namespace collisions. Initial
     capability identifiers SHOULD include "rollback.immediate",
     "rollback.coordinated", "checkpoint.distributed", and
     "integration.gateway" to support the core protocol functionality
     and integration patterns described in this specification.

     The "RARP Agent Transaction Types" registry supports the
     classification and coordination of rollback operations across
     heterogeneous agent systems. This registry uses string-based
     identifiers and MUST include initial registrations for
     "network.configuration", "routing.policy", "security.rule", and
     "service.deployment" to align with common network operations use
     cases. Registration procedures for all RARP registries MUST
     require specification of the parameter name, value, description,
     security considerations if applicable, and reference document.
     Registrants SHOULD provide interoperability considerations when
     the parameter affects cross-domain operations or integration with
     existing protocols such as NETCONF [RFC6241] or agent gateway
     frameworks.

     All RARP registry entries MUST be subject to expert review for
     values in the IANA allocation ranges, with designated experts
     evaluating technical soundness, potential conflicts with existing
     registrations, and alignment with RARP architectural principles.
     The expert review process SHALL consider the impact on cross-
     domain rollback coordination and compatibility with existing agent
     communication protocols. Registry updates affecting security-
     sensitive parameters such as authorization capabilities or cross-
     domain coordination mechanisms require additional security review
     to ensure consistency with the security considerations outlined in
     Section 8 of this specification and general security practices for
     autonomous network operations.

  10.  References

  10.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 8259]
           RFC 8259

     [RFC 6241]
           RFC 6241

     [draft-han-rtgwg-agent-gateway-intercomm-framework]
           draft-han-rtgwg-agent-gateway-intercomm-framework

     [draft-li-dmsc-macp]
           draft-li-dmsc-macp

     [draft-fu-nmop-agent-communication-framework]
           draft-fu-nmop-agent-communication-framework

  10.2.  Informative References

     [RFC 8446]
           RFC 8446

     [RFC 9000]
           RFC 9000

     [RFC 6749]
           RFC 6749

     [draft-chuyi-nmrg-ai-agent-network]
           draft-chuyi-nmrg-ai-agent-network

     [draft-jadoon-nmrg-agentic-ai-autonomous-networks]
           draft-jadoon-nmrg-agentic-ai-autonomous-networks

     [draft-vandoulas-aidp]
           draft-vandoulas-aidp

     [draft-cui-ai-agent-discovery-invocation]
           draft-cui-ai-agent-discovery-invocation

     [draft-wang-nmrg-magent-im]
           draft-wang-nmrg-magent-im

     [draft-cui-nmrg-llm-benchmark]
           draft-cui-nmrg-llm-benchmark

     [draft-yue-anima-agent-recovery-networks]
           draft-yue-anima-agent-recovery-networks


  Author's Address

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

rating_json:
  {"s": "RARP proposes a standardized protocol for distributed rollback of autonomous agent actions in network operations, enabling safe reversal of incorrect decisions through checkpoint creation, state verification, and cross-domain coordination. The draft addresses a real gap in autonomous netops safety by providing mechanisms for both immediate and delayed rollback scenarios.", "n": 3, "nn": "Rollback protocols for distributed systems exist, but application to autonomous agents in netops is relatively novel; however, the core concepts (checkpointing, state consistency) are well-established in database and systems literature.", "m": 2, "mn": "Early draft status (00) with minimal implementation evidence or deployment experience cited; lacks concrete protocol specifications, message formats, and interoperability considerations needed for IETF standardization.", "o": 4, "on": "Significant overlap with existing transaction rollback mechanisms, distributed consensus protocols, and agent communication frameworks; the novelty is primarily in the application domain rather than fundamental innovations.", "mo": 2, "mon": "Limited evidence of community interest, no reference implementations, no deployment feedback, and positioned as forward-looking for autonomous netops adoption that remains nascent.", "r": 3, "rn": "Relevant to emerging autonomous network operations but addresses a somewhat narrow and premature use case; safety mechanisms are important but protocol standardization may be premature given current autonomous agent deployment maturity.", "c": ["Autonomous netops", "AI safety/alignment", "A2A protocols", "Policy/governance"]}


