# generated_drafts

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"]}


gap_topic: Agent Provenance Assurance Ecosystem
draft_name: draft-ai-agent-provenance-assurance-ecosystem-00
title: Agent Provenance Assurance Ecosystem (APAE) Framework
family_name: agent-ecosystem
family_role: APAE
status: draft
created_at: 2026-03-04T00:38:30.174502+00:00
abstract:
  The proliferation of AI agents across distributed systems creates critical challenges for establishing verifiable chains of identity, capabilities, and operational history. This document defines the Agent Provenance Assurance Ecosystem (APAE) Framework, a comprehensive system for tracking, verifying, and assuring the provenance of AI agents throughout their lifecycle. The framework establishes a multi-layered architecture encompassing agent identity attestation, capability verification, operational audit trails, and cross-domain provenance tracking. APAE integrates with existing identity management systems, authorization protocols, and communication frameworks to provide end-to-end provenance assurance without disrupting current agent ecosystems. The framework supports both lightweight deployments for development environments and high-assurance configurations for regulated industries, enabling the same agent infrastructure to operate across varying trust requirements while maintaining verifiable provenance chains.

outline_json:
  {"title": "Agent Provenance Assurance Ecosystem (APAE) Framework", "abstract": "The proliferation of AI agents across distributed systems creates critical challenges for establishing verifiable chains of identity, capabilities, and operational history. This document defines the Agent Provenance Assurance Ecosystem (APAE) Framework, a comprehensive system for tracking, verifying, and assuring the provenance of AI agents throughout their lifecycle. The framework establishes a multi-layered architecture encompassing agent identity attestation, capability verification, operational audit trails, and cross-domain provenance tracking. APAE integrates with existing identity management systems, authorization protocols, and communication frameworks to provide end-to-end provenance assurance without disrupting current agent ecosystems. The framework supports both lightweight deployments for development environments and high-assurance configurations for regulated industries, enabling the same agent infrastructure to operate across varying trust requirements while maintaining verifiable provenance chains.", "sections": [{"title": "Introduction", "summary": "Introduces the growing need for agent provenance assurance in distributed AI systems. Establishes the scope of provenance tracking across agent creation, delegation, capability evolution, and operational history.", "key_ideas": ["Agent lifecycle provenance", "Cross-domain trust challenges", "Integration with existing ecosystems"]}, {"title": "Terminology", "summary": "Defines key terms used throughout the framework including provenance chains, attestation levels, and agent identity primitives. Establishes consistent vocabulary for multi-stakeholder environments.", "key_ideas": ["Provenance terminology", "Trust level definitions", "Agent identity concepts"]}, {"title": "Problem Statement and Requirements", "summary": "Articulates the specific challenges in agent provenance tracking including delegation chains, capability verification, and cross-ecosystem interoperability. Defines functional and security requirements for the framework.", "key_ideas": ["Traceable Identity and Provenance", "Human Oversight Requirements", "Cross-Domain Agent Identity Management"]}, {"title": "APAE Architecture Overview", "summary": "Presents the multi-layered APAE architecture including identity attestation, capability manifests, operational audit, and registry services. Defines the relationship between components and integration points.", "key_ideas": ["AI Agent Manifest Attestation", "Ecosystem Registries", "Notary Agent", "Working Memory"]}, {"title": "Agent Identity and Attestation Layer", "summary": "Specifies mechanisms for establishing and verifying agent identity including cryptographic attestation, delegation chain validation, and identity preservation across transactions. Integrates with existing identity management systems.", "key_ideas": ["Agent Identity Preservation", "Agent Delegation Chain Validation", "Structured Claims for Agent Context"]}, {"title": "Capability and Manifest Verification", "summary": "Defines protocols for verifying agent capabilities, loaded models, active policies, and operational constraints. Establishes manifest formats and validation procedures for capability assurance.", "key_ideas": ["Capability-based Discovery", "Oracle-free Agent Evaluation", "Built-in Prioritization for Agent Communication"]}, {"title": "Provenance Audit and Logging Framework", "summary": "Specifies audit trail generation, storage, and verification mechanisms for agent operations. Defines logging formats, retention policies, and cross-domain audit trail federation.", "key_ideas": ["Agent Communication Gateway (Agent-GW)", "Automated Protocol Adaptation", "QUIC-based Publish/Subscribe for AI Agents"]}, {"title": "Cross-Ecosystem Integration Patterns", "summary": "Describes integration patterns with existing authorization systems, identity providers, and communication protocols. Provides guidance for retrofitting provenance assurance into deployed agent systems.", "key_ideas": ["Agent Authorization Profile (AAP)", "Agent Resource Type", "User Schema Extensions for Agent Relationships"]}, {"title": "Deployment Profiles and Trust Models", "summary": "Defines deployment profiles ranging from lightweight development environments to high-assurance regulated systems. Specifies trust models, assurance levels, and configuration guidelines for different operational contexts.", "key_ideas": ["Group Schema Extensions for Agentic Constructs", "Human Oversight Requirements", "Cross-Domain Agent Identity Management"]}, {"title": "Security Considerations", "summary": "Addresses security aspects of provenance tracking including privacy preservation, tamper resistance, and attack mitigation. Discusses threat models and security controls for provenance data.", "key_ideas": ["Cryptographic attestation", "Privacy-preserving provenance", "Tamper-evident audit trails"]}, {"title": "IANA Considerations", "summary": "Specifies IANA registrations for new URI schemes, MIME types, and registry namespaces required by the framework.", "key_ideas": ["URI scheme registration", "MIME type registration", "Registry namespace allocation"]}], "normative_refs": ["RFC 2119", "RFC 8174", "RFC 6749", "RFC 7519", "RFC 5280", "RFC 9052", "draft-chen-ai-agent-auth-new-requirements", "draft-jiang-seat-dynamic-attestation", "draft-liu-oauth-a2a-profile", "draft-aap-oauth-profile"], "informative_refs": ["RFC 8446", "RFC 9110", "RFC 8126", "RFC 8259", "draft-narvaneni-agent-uri", "draft-wendt-stir-vesper", "draft-a2a-moqt-transport", "draft-abbey-scim-agent-extension", "draft-agent-gw", "draft-ainp-protocol", "draft-cowles-volt", "draft-aylward-daap-v2", "draft-guy-bary-stamp-protocol"], "terminology": {"Agent Provenance Chain": "A verifiable sequence of records documenting an agent's identity, capabilities, delegations, and operational history from creation through current state", "Attestation Level": "A classification of the strength and scope of cryptographic and procedural evidence supporting agent identity and capability claims", "Capability Manifest": "A signed document describing an agent's current models, tools, policies, and operational constraints with cryptographic integrity protection", "Provenance Anchor": "A cryptographically verifiable root of trust that establishes the initial identity and capabilities of an agent at creation time", "Cross-Domain Provenance": "The preservation and verification of agent provenance information across different administrative domains, trust boundaries, and protocol ecosystems", "Audit Trail Federation": "The controlled sharing and verification of agent operational logs across multiple systems while preserving privacy and security constraints"}, "target_wg": "lake", "intended_status": "standards-track"}

sections_json:
  ["The deployment of AI agents across distributed systems has fundamentally transformed how automated decision-making and task execution occur in modern computing environments. As organizations increasingly rely on AI agents for critical operations ranging from customer service interactions to financial transaction processing and infrastructure management, the need for verifiable provenance tracking has become paramount. Unlike traditional software components with static capabilities, AI agents exhibit dynamic behavior through model updates, policy modifications, and capability evolution, creating complex challenges for establishing trust and accountability in distributed deployments [draft-chen-ai-agent-auth-new-requirements].\n\nCurrent approaches to agent identity and authorization, while addressing immediate authentication needs, fail to provide comprehensive visibility into agent provenance across their operational lifecycle. Organizations lack standardized mechanisms to verify not only who created an agent, but also what models it currently runs, which policies govern its behavior, how its capabilities have evolved over time, and what operations it has performed across distributed systems. This gap becomes particularly critical when agents operate across organizational boundaries, undergo delegation to other agents, or when regulatory compliance requires detailed audit trails of automated decision-making processes [draft-jiang-seat-dynamic-attestation].\n\nThe challenges of agent provenance extend beyond simple identity verification to encompass the full spectrum of agent lifecycle management. Agent creation involves establishing cryptographic roots of trust and initial capability manifests. Agent delegation requires preserving provenance chains while enabling authorized capability transfer between entities. Capability evolution demands continuous attestation as agents load new models, update policies, or modify their operational constraints. Operational history tracking necessitates comprehensive audit trails that maintain integrity across system boundaries while supporting privacy preservation and selective disclosure requirements [draft-liu-oauth-a2a-profile].\n\nExisting identity management systems, authorization protocols, and audit frameworks provide essential building blocks but lack the specialized constructs needed for comprehensive agent provenance assurance. Standard identity protocols such as OAuth 2.0 [RFC6749] and JWT [RFC7519] enable agent authentication but do not capture the dynamic nature of agent capabilities or provide mechanisms for tracking operational history. Certificate-based systems [RFC5280] can establish initial identity but fail to accommodate the continuous evolution of agent capabilities. Current audit systems focus on user actions rather than autonomous agent behavior, creating gaps in accountability for automated operations.\n\nThis document defines the Agent Provenance Assurance Ecosystem (APAE) Framework to address these challenges through a comprehensive, multi-layered approach to agent provenance tracking. APAE establishes standardized mechanisms for agent identity attestation, capability verification, operational audit trails, and cross-domain provenance preservation. The framework integrates with existing authentication and authorization systems while extending them with agent-specific provenance capabilities, enabling organizations to retrofit provenance assurance into deployed agent ecosystems without disrupting current operations.\n\nThe scope of APAE encompasses the complete agent lifecycle from initial creation through operational retirement. This includes establishing provenance anchors at agent creation, maintaining verifiable capability manifests as agents evolve, tracking delegation relationships and authority transfers, logging operational activities with cryptographic integrity, and federating provenance information across organizational and protocol boundaries. The framework supports flexible deployment models ranging from lightweight development environments to high-assurance configurations required by regulated industries, enabling the same underlying infrastructure to adapt to varying trust requirements while maintaining consistent provenance semantics across all deployment contexts.", "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 document defines the following terms for use within the Agent Provenance Assurance Ecosystem (APAE) Framework:\n\n**Agent**: An autonomous software entity that acts on behalf of users, services, or other agents, typically incorporating AI/ML models for decision-making and task execution as defined in [draft-chen-ai-agent-auth-new-requirements].\n\n**Agent Identity Primitive**: A fundamental cryptographic construct (such as a public key, certificate, or distributed identifier) that serves as the basis for agent authentication and identity verification throughout the agent's lifecycle.\n\n**Agent Provenance Chain**: A chronologically ordered, cryptographically linked sequence of records that documents an agent's complete history including initial creation, identity establishment, capability modifications, delegation relationships, and operational activities. Each link in the chain MUST be verifiable and tamper-evident, forming an immutable audit trail from the agent's provenance anchor through its current operational state.\n\n**Attestation Level**: A standardized classification system that quantifies the strength, scope, and trustworthiness of evidence supporting agent identity and capability claims. Attestation levels range from basic self-assertions suitable for development environments to high-assurance cryptographic attestations meeting regulatory compliance requirements. Higher attestation levels require stronger cryptographic evidence, more rigorous validation procedures, and enhanced tamper-resistance mechanisms.\n\n**Capability Manifest**: A structured, digitally signed document that comprehensively describes an agent's current operational configuration including loaded AI/ML models, available tools and APIs, active behavioral policies, operational constraints, and security boundaries. As specified in [draft-jiang-seat-dynamic-attestation], capability manifests provide cryptographically verifiable assurance about an agent's actual capabilities and constraints at the time of attestation.\n\n**Cross-Domain Provenance**: The preservation, validation, and verification of agent provenance information as agents operate across heterogeneous administrative domains, trust boundaries, protocol ecosystems, and organizational jurisdictions. Cross-domain provenance ensures that provenance chains remain intact and verifiable even when agents interact with systems using different identity management frameworks, authorization protocols, or security models as described in [draft-liu-oauth-a2a-profile].\n\n**Delegation Chain**: A verifiable sequence of authorization grants and capability transfers that documents how operational authority and permissions flow from an originating principal (user, service, or agent) through intermediary agents to the currently acting agent. Each delegation in the chain MUST be cryptographically verifiable and include scope limitations, temporal constraints, and revocation mechanisms.\n\n**Operational Audit Trail**: A tamper-evident log of agent activities, decisions, and state changes that provides accountability and forensic capabilities for agent operations. Audit trails include sufficient detail to reconstruct agent behavior while preserving privacy and confidentiality requirements through selective disclosure and zero-knowledge proof mechanisms.\n\n**Provenance Anchor**: The cryptographically verifiable root of trust that establishes the foundational identity and initial capabilities of an agent at creation time. Provenance anchors serve as the starting point for all subsequent provenance chain entries and MUST be issued by trusted authorities with appropriate key management and certificate lifecycle processes as defined in [RFC5280].\n\n**Provenance Registry**: A distributed system that stores, indexes, and provides query interfaces for agent provenance information while maintaining appropriate access controls and privacy protections. Registries enable cross-domain provenance lookup, verification, and audit trail federation across organizational boundaries.\n\n**Trust Boundary**: A logical or physical demarcation point where different security domains, administrative authorities, or trust models intersect, requiring explicit validation and translation of provenance information to maintain chain integrity and verifiability.\n\nThese terms are used consistently throughout this specification to ensure clarity and interoperability in multi-stakeholder environments deploying agent provenance assurance capabilities.", "The emergence of autonomous AI agents in distributed systems presents unprecedented challenges for establishing verifiable chains of identity, capabilities, and operational history. Unlike traditional software components with static configurations, AI agents exhibit dynamic behavior patterns, evolve their capabilities through learning and tool acquisition, and operate through complex delegation relationships that span multiple administrative domains. Current identity and authorization frameworks, while adequate for human users and simple service-to-service authentication, lack the semantic richness and temporal granularity required to track agent provenance across their full lifecycle. The absence of standardized provenance tracking mechanisms creates significant risks including unauthorized capability escalation, untraceable delegation chains, and inability to audit agent decision-making processes in regulated environments.\n\nAgent delegation presents particular challenges as agents frequently spawn sub-agents, delegate tasks to peer agents, or operate on behalf of other entities through complex authorization chains. Traditional OAuth 2.0 [RFC6749] delegation models assume relatively simple delegation relationships and static capability sets, whereas agent-to-agent delegation involves dynamic capability inheritance, constraint propagation, and context-dependent authorization decisions. The Agent Authorization Profile [draft-aap-oauth-profile] addresses some of these challenges through structured claims, but requires a comprehensive provenance framework to establish the trustworthiness of those claims across domain boundaries. Furthermore, agents may acquire new capabilities through model updates, tool integrations, or policy modifications during their operational lifetime, necessitating continuous provenance tracking rather than static identity attestation.\n\nCross-ecosystem interoperability compounds these challenges as agents must operate across heterogeneous systems with varying trust models, identity providers, and security requirements. An agent created in one organization's development environment may later operate in a partner's production system or a regulated cloud service, each requiring different levels of provenance assurance and audit granularity. Existing identity management protocols like SCIM [draft-abbey-scim-agent-extension] provide mechanisms for agent identity synchronization, but lack the semantic frameworks necessary to verify capability claims and operational constraints across trust boundaries. The absence of standardized provenance interchange formats prevents comprehensive audit trails and limits the ability to establish end-to-end accountability for agent actions.\n\nThe APAE framework MUST provide verifiable agent identity establishment and preservation mechanisms that support cryptographic attestation of agent creation, modification, and delegation events. The framework MUST enable tracking of capability evolution including model updates, tool acquisitions, and policy changes with tamper-evident audit trails. Agent delegation chains MUST be cryptographically verifiable with support for constraint inheritance and capability subset enforcement as specified in the authentication requirements for AI agents [draft-chen-ai-agent-auth-new-requirements]. The framework MUST support cross-domain provenance tracking that preserves verifiability while respecting privacy and security boundaries between administrative domains.\n\nThe framework MUST integrate with existing OAuth 2.0 [RFC6749] and JWT [RFC7519] infrastructure without requiring protocol modifications to deployed systems. Human oversight requirements MUST be traceable through provenance chains with verifiable enforcement of human-in-the-loop constraints for sensitive operations. The framework MUST support multiple assurance levels ranging from lightweight development environments to high-assurance regulated deployments with appropriate cryptographic strength and audit granularity. Operational audit trails MUST provide sufficient detail for regulatory compliance while supporting privacy-preserving federation across organizational boundaries.\n\nSecurity requirements include protection against provenance tampering, replay attacks, and unauthorized capability escalation through cryptographic integrity mechanisms. The framework MUST provide defense against agent impersonation, delegation chain forgery, and capability manifest manipulation while maintaining acceptable performance characteristics for high-volume agent interactions. Privacy preservation MUST be supported through selective disclosure mechanisms and zero-knowledge proofs where full provenance transparency conflicts with confidentiality requirements. The framework MUST support revocation and recovery mechanisms for compromised agent identities while preserving historical audit trail integrity.", "The Agent Provenance Assurance Ecosystem (APAE) Framework employs a multi-layered architecture designed to provide comprehensive provenance tracking while maintaining compatibility with existing agent infrastructures. The architecture consists of four primary layers: the Identity Attestation Layer, the Capability and Manifest Verification Layer, the Provenance Audit and Logging Layer, and the Registry and Discovery Layer. Each layer operates independently while providing well-defined interfaces for inter-layer communication and data exchange. This modular design enables selective deployment of framework components based on specific operational requirements and trust models, allowing organizations to implement provenance assurance incrementally without disrupting existing agent ecosystems.\n\nThe Identity Attestation Layer serves as the foundational trust anchor for the APAE framework, establishing and maintaining verifiable agent identities throughout their operational lifecycle. This layer implements cryptographic attestation mechanisms compatible with existing identity management systems as specified in [RFC6749] and emerging agent authentication requirements outlined in [draft-chen-ai-agent-auth-new-requirements]. Agent identities are established through Provenance Anchors that create immutable cryptographic bindings between agent instances and their initial capability sets. The layer maintains delegation chains that preserve identity continuity across agent spawning, capability updates, and cross-domain transfers, ensuring that each agent's lineage remains verifiable regardless of operational complexity or administrative boundaries.\n\nThe Capability and Manifest Verification Layer implements the AI Agent Manifest Attestation mechanism to provide continuous assurance of agent capabilities and operational constraints. This layer generates and validates Capability Manifests that cryptographically bind agent identities to their current models, tools, policies, and Working Memory configurations as described in [draft-jiang-seat-dynamic-attestation]. Manifest verification occurs through a combination of static analysis and runtime attestation, ensuring that declared capabilities match actual agent implementations. The layer integrates with authorization frameworks defined in [draft-liu-oauth-a2a-profile] to enforce capability-based access controls and prevent agents from exceeding their declared operational boundaries.\n\nThe Provenance Audit and Logging Layer captures comprehensive operational histories through tamper-evident audit trails that document agent actions, decisions, and state transitions. This layer implements Audit Trail Federation mechanisms that enable secure sharing of provenance information across administrative domains while preserving privacy and confidentiality requirements. The logging framework supports both real-time audit streaming for high-assurance environments and batch processing for performance-sensitive deployments. Audit records are cryptographically linked to agent identities and capability manifests, creating verifiable chains of operational evidence that support compliance, debugging, and security analysis requirements.\n\nThe Registry and Discovery Layer provides the infrastructure services necessary for cross-ecosystem provenance verification and agent lifecycle management. This layer implements Ecosystem Registries that resolve agent identifiers to concrete endpoints while maintaining provenance metadata for discovery and verification operations. The registry services support hierarchical trust models that enable both centralized and federated deployment patterns, allowing organizations to maintain local control over agent registration while participating in broader provenance ecosystems. Integration with existing DNS and PKI infrastructure ensures compatibility with current internet protocols while providing the specialized services required for agent provenance assurance.\n\nComponent integration occurs through standardized APIs that abstract the complexity of provenance operations from agent implementations and management systems. The framework defines integration points with existing OAuth 2.0 authorization servers [RFC6749], JWT token validation systems [RFC7519], and X.509 certificate authorities [RFC5280] to leverage established trust infrastructure. Cross-layer communication employs CBOR Web Token (CWT) formats [RFC9052] for efficient provenance data exchange, while maintaining compatibility with JSON-based systems through bidirectional transformation protocols. This integration approach enables the APAE framework to operate as both a standalone provenance system and as an enhancement layer for existing agent infrastructures, supporting gradual adoption paths that minimize deployment risk and operational disruption.", "The Agent Identity and Attestation Layer forms the foundational trust anchor for the APAE framework by establishing cryptographically verifiable agent identities and maintaining their integrity throughout distributed operations. This layer defines mechanisms for creating immutable Provenance Anchors at agent instantiation, enabling subsequent verification of agent authenticity and delegation chains across administrative boundaries. The attestation mechanisms MUST support multiple trust models ranging from self-signed certificates for development environments to hardware-backed attestation for high-assurance deployments, ensuring consistent identity semantics while accommodating diverse operational requirements.\n\nAgent identity establishment begins with the creation of a cryptographic identity pair and corresponding Provenance Anchor that binds the agent's initial state, capabilities, and authorized delegation scope. The Provenance Anchor MUST include the agent's public key, creation timestamp, initial Capability Manifest hash, and attestation metadata as defined in [RFC5280] certificate extensions. For agents created through delegation, the Provenance Anchor MUST reference the parent agent's identity and include a delegation certificate chain as specified in Section 4.3 of [draft-aap-oauth-profile]. The attestation layer SHOULD integrate with existing Public Key Infrastructure (PKI) systems and identity providers to leverage established trust relationships while maintaining agent-specific provenance requirements.\n\nIdentity preservation across transactions requires embedding agent identity information within protocol tokens to maintain provenance visibility throughout distributed agent interactions. Following the patterns established in [draft-liu-oauth-a2a-profile], the attestation layer MUST encode agent identity claims within JWT tokens [RFC7519] using structured claims that capture agent context, delegation chains, and operational constraints. The \"sub\" claim MUST identify the agent using a stable identifier, while custom claims including \"agent_id\", \"delegation_chain\", and \"capability_hash\" provide machine-readable context for authorization decisions. Token validation procedures MUST verify both the cryptographic integrity of the token and the validity of embedded delegation chains against current agent registry state.\n\nDelegation chain validation ensures that agents acting on behalf of humans or other agents maintain verifiable authorization inheritance throughout multi-hop delegation scenarios. The attestation layer MUST implement validation rules that verify each delegation link's cryptographic signature, temporal validity, and scope constraints as defined in Section 5.2 of [draft-aap-oauth-profile]. Validation procedures MUST check that each delegation in the chain is properly authorized by its parent and that the cumulative delegation scope does not exceed the original authorization grant. For cross-domain delegations, the layer MUST support federation mechanisms that enable delegation chain verification across different administrative domains while preserving local policy enforcement.\n\nIntegration with existing identity management systems requires protocol adapters that bridge APAE agent identity concepts with established authentication and authorization frameworks including OAuth 2.0 [RFC6749], SAML, and enterprise identity providers. The attestation layer MUST provide standardized interfaces for identity provider integration that preserve agent provenance information while leveraging existing user directories and policy engines. For environments requiring hardware-backed attestation, the layer SHOULD integrate with Trusted Platform Module (TPM) attestation mechanisms and secure enclave technologies to provide cryptographic proof of agent execution environment integrity as specified in [draft-jiang-seat-dynamic-attestation].\n\nThe attestation layer maintains agent identity lifecycle management including identity renewal, revocation, and migration scenarios that preserve provenance chain integrity across operational changes. Identity renewal procedures MUST generate new cryptographic material while maintaining linkage to previous identity states through signed transition records. Revocation mechanisms MUST support both immediate revocation for security incidents and scheduled revocation for planned agent retirement, with appropriate notification to dependent systems. For agent migration scenarios, the layer MUST provide protocols for securely transferring agent identity and associated provenance data between execution environments while maintaining cryptographic integrity and audit trail continuity.", "The APAE framework establishes comprehensive mechanisms for verifying agent capabilities, loaded models, active policies, and operational constraints through structured capability manifests and cryptographic validation procedures. Agents MUST maintain up-to-date capability manifests that accurately reflect their current operational state, including all loaded models, available tools, active security policies, and operational constraints. These manifests serve as authoritative declarations of agent capabilities that can be independently verified by other agents, infrastructure components, and human operators. The framework supports both static capability declarations established at agent creation time and dynamic capability updates that reflect runtime changes to agent configuration.\n\nCapability manifests MUST be structured as JSON documents conforming to a standardized schema that includes sections for model identification, tool descriptions, policy specifications, and operational metadata. Each manifest MUST include cryptographic signatures from the agent's identity key as defined in Section 5, enabling verification of manifest authenticity and integrity. The manifest format incorporates semantic capability descriptions that support capability-based discovery mechanisms, allowing agents to locate and interact with other agents based on functional requirements rather than network topology. Manifests MAY include capability hierarchies and dependency relationships that describe how complex capabilities are composed from simpler primitives, enabling fine-grained capability matching and verification.\n\nThe framework defines validation procedures that enable both local and remote verification of agent capabilities through multiple assurance mechanisms. Agents MUST implement self-attestation procedures that generate cryptographic proofs of their current capability state, including verification that loaded models match declared specifications and that active policies are properly enforced. The framework supports oracle-free evaluation mechanisms that allow agents to demonstrate their capabilities through standardized test interactions without requiring external ground truth verification systems. For higher assurance levels, the framework MAY utilize remote attestation procedures where trusted third parties verify agent capabilities through controlled testing environments or hardware-based attestation mechanisms.\n\nCapability verification integrates with the APAE audit framework to maintain comprehensive records of capability changes and verification events throughout an agent's operational lifecycle. All capability updates MUST be logged to the provenance audit trail with cryptographic timestamps and verification evidence. The framework supports incremental capability verification where only modified capabilities require re-validation, reducing computational overhead while maintaining security properties. Verification procedures MUST account for capability delegation scenarios where agents operate with restricted or derived capabilities from their base configuration, ensuring that delegation constraints are properly enforced and documented.\n\nThe framework establishes integration patterns with existing authorization and policy systems through standardized capability assertion formats compatible with OAuth 2.0 scopes [RFC6749] and JWT claims [RFC7519]. Capability manifests MAY be embedded in access tokens or presented as separate attestation documents depending on the integration requirements of the target system. The framework supports prioritization mechanisms that ensure critical capability verification traffic receives appropriate network and processing priority, particularly in high-throughput agent communication scenarios. Capability verification procedures MUST preserve privacy by supporting selective disclosure of capability information while maintaining the ability to verify relevant capabilities for specific interactions.", "The provenance audit and logging framework establishes comprehensive mechanisms for recording, storing, and verifying the operational history of AI agents throughout their lifecycle. This framework builds upon the identity attestation and capability verification layers to create tamper-evident audit trails that preserve agent provenance across administrative domains and protocol boundaries. The audit framework integrates with existing logging infrastructure while providing specialized capabilities for agent-specific events including delegation chains, capability modifications, and cross-domain interactions.\n\nAgent operations MUST generate structured audit records using a standardized format that includes cryptographic integrity protection and temporal ordering guarantees. Each audit record contains the agent identity from the attestation layer, operation type, relevant capability manifest references, timestamp with cryptographic time-binding, and operation-specific metadata. The audit record format builds upon JSON Web Signature [RFC7515] structures to ensure cryptographic integrity while maintaining human readability for operational troubleshooting. Records MUST include sufficient context to reconstruct the agent's state and decision-making process at the time of the logged operation, enabling comprehensive provenance verification.\n\nThe framework defines multiple audit trail storage models to accommodate different deployment profiles and trust requirements. Local audit storage provides high-performance logging for single-domain deployments, while distributed audit architectures enable cross-domain provenance tracking through secure federation protocols. Audit trails MAY be stored in blockchain-based systems for maximum tamper resistance, traditional database systems with cryptographic integrity checking, or hybrid approaches that balance performance and assurance requirements. The storage layer MUST implement retention policies that comply with regulatory requirements while supporting efficient querying and verification operations across potentially large audit datasets.\n\nCross-domain audit trail federation enables provenance tracking as agents operate across multiple systems and administrative boundaries. The federation protocol builds upon the Agent Communication Gateway architecture [draft-agent-gw] to establish secure channels for audit trail sharing while preserving privacy and confidentiality constraints. Federation participants MUST implement access control policies that govern which audit information can be shared across domain boundaries, ensuring that sensitive operational details remain protected while enabling necessary provenance verification. The federation protocol supports both real-time audit streaming for active monitoring and batch synchronization for historical audit trail reconstruction.\n\nAudit trail verification mechanisms provide cryptographic assurance of audit record integrity and completeness across distributed storage systems. Verification protocols MUST detect tampering, insertion attacks, and selective deletion of audit records while providing efficient proof generation for compliance reporting. The framework integrates with QUIC-based publish/subscribe systems [draft-a2a-moqt-transport] to enable real-time audit verification and alerting when provenance violations are detected. Verification results are themselves logged to create a complete audit chain that can demonstrate the integrity of the provenance tracking system to external auditors and regulatory authorities.\n\nThe audit framework defines standard retention policies and archival procedures that balance operational requirements with storage efficiency and regulatory compliance. Short-term audit data SHOULD be stored with high availability and low latency access to support real-time monitoring and incident response, while long-term archival systems focus on integrity preservation and cost-effective storage. Automated policy enforcement ensures that audit data transitions through appropriate lifecycle stages while maintaining cryptographic integrity and chain-of-custody documentation required for legal and regulatory purposes.", "The APAE framework is designed to integrate seamlessly with existing identity, authorization, and communication ecosystems through well-defined integration patterns. These patterns enable organizations to retrofit provenance assurance capabilities into deployed agent systems without requiring wholesale replacement of existing infrastructure. The integration approach leverages established protocols and standards while extending them with agent-specific provenance metadata and verification mechanisms.\n\nIntegration with OAuth 2.0 and OpenID Connect ecosystems follows the Agent Authorization Profile (AAP) [draft-aap-oauth-profile] extension pattern. Existing OAuth 2.0 authorization servers can be extended to issue agent-specific access tokens that include provenance claims alongside traditional authorization claims. The APAE framework defines standardized JWT [RFC7519] claim extensions that carry provenance chain identifiers, attestation levels, and capability manifest references within OAuth access tokens. Authorization servers MUST validate provenance claims during token issuance and MAY enforce provenance-based authorization policies. Resource servers can extract provenance information from access tokens to make fine-grained authorization decisions based on agent identity, delegation chains, and operational constraints without requiring separate provenance lookups.\n\nIdentity management systems integration leverages the SCIM Agent Extension [draft-abbey-scim-agent-extension] to represent agents as first-class identity resources. Existing SCIM [RFC7644] deployments can be extended with agent resource types and user schema extensions for agent relationships, enabling centralized management of agent identities alongside human users. The APAE framework specifies how provenance anchors and capability manifests are stored as extended attributes within SCIM agent resources. Identity providers MUST maintain provenance metadata consistency when provisioning or updating agent identities across connected systems. User schema extensions enable tracking of delegation relationships between human users and their associated agents, supporting organizational governance requirements for agent oversight.\n\nCommunication protocol integration patterns address both synchronous and asynchronous agent interactions across different transport mechanisms. For HTTP-based communications, the framework defines standardized headers that carry provenance attestations and audit trail references alongside existing authentication headers. Message-based systems can embed provenance metadata within message headers or signed payloads using COSE [RFC9052] structures for cryptographic integrity. The framework specifies how existing API gateways and service meshes can be configured to extract, validate, and propagate provenance information across service boundaries. Protocol adapters SHOULD preserve provenance chains when translating between different communication protocols or message formats.\n\nRetrofitting guidance for deployed systems emphasizes incremental adoption patterns that minimize operational disruption. Organizations can begin by deploying APAE registry services alongside existing infrastructure to collect provenance data without enforcing verification policies. Existing agents can be gradually onboarded by adding provenance metadata to their identity records and configuring audit logging to feed into APAE audit trail storage. The framework supports hybrid deployments where some agents participate in full provenance tracking while others operate with reduced assurance levels during transition periods. Migration tooling can extract existing audit logs and identity information to bootstrap provenance chains for previously deployed agents.\n\nLegacy system integration addresses scenarios where existing agent systems cannot be directly modified to support APAE protocols. Proxy components can be deployed to intercept agent communications and inject provenance metadata based on static configuration or external attestation sources. Gateway services can perform provenance validation on behalf of legacy systems while presenting traditional authentication and authorization interfaces to unchanged applications. The framework defines compatibility modes that allow APAE-enabled systems to interact safely with non-APAE systems while maintaining provenance tracking for supported interactions. Organizations SHOULD document provenance gaps when integrating with legacy systems and implement compensating controls where full provenance assurance cannot be achieved.", "The APAE framework defines three primary deployment profiles to accommodate varying operational requirements, risk tolerances, and regulatory constraints across different environments. Each profile represents a distinct configuration of trust anchors, verification requirements, and assurance mechanisms while maintaining architectural compatibility to enable agent mobility between environments.\n\nThe Development Profile provides minimal provenance overhead suitable for development, testing, and low-risk operational environments. In this configuration, agent identity MAY be established through self-signed certificates or local trust anchors, capability manifests SHOULD be cryptographically signed but MAY use development keys, and audit trails MAY be stored locally with basic integrity protection. The Development Profile enables rapid iteration and testing while introducing core provenance concepts without the infrastructure overhead of higher assurance levels. Cross-domain provenance tracking in Development Profile deployments SHOULD preserve chain integrity but MAY accept lower verification standards for external attestation anchors.\n\nThe Enterprise Profile establishes intermediate assurance levels appropriate for business-critical applications, regulated industries with moderate risk profiles, and multi-organizational collaborations. This profile MUST implement cryptographically verifiable provenance anchors rooted in organizational PKI or approved certificate authorities, capability manifests MUST be signed by authorized capability authorities, and audit trails MUST be protected against tampering through cryptographic mechanisms such as hash chains or digital signatures per [RFC5280]. The Enterprise Profile MUST support human oversight requirements as defined in [draft-aap-oauth-profile], enabling enforcement of human-in-the-loop controls based on agent capabilities, operational context, or regulatory requirements. Cross-domain provenance federation in Enterprise deployments MUST implement bilateral or multilateral trust agreements with explicit policy mappings for attestation level recognition.\n\nThe High-Assurance Profile provides maximum security and verification suitable for national security applications, critical infrastructure, and highly regulated environments with stringent compliance requirements. This profile MUST implement hardware-backed provenance anchors using mechanisms consistent with [draft-jiang-seat-dynamic-attestation], all capability manifests MUST undergo formal verification processes before deployment, and audit trails MUST be stored in tamper-evident systems with distributed verification. High-Assurance deployments MUST implement real-time capability verification, continuous operational monitoring, and mandatory human oversight for all agent actions exceeding predefined risk thresholds. The High-Assurance Profile MUST support air-gapped operation modes where cross-domain provenance verification occurs through offline cryptographic proof exchange rather than real-time federation protocols.\n\nTrust models within APAE deployments establish the foundation for provenance verification and cross-domain interoperability while accommodating different organizational risk appetites and regulatory frameworks. The framework defines three trust model categories: Direct Trust, where all agents operate within a single administrative domain with unified trust anchors; Federated Trust, where multiple domains establish bilateral or multilateral agreements for provenance recognition with explicit policy mappings; and Zero Trust, where each provenance claim undergoes independent verification regardless of source domain. Zero Trust configurations MUST implement continuous verification of agent identity, capabilities, and operational constraints, treating all provenance claims as potentially compromised until cryptographically verified against current trust anchors. Federated Trust models SHOULD implement policy translation mechanisms to map attestation levels and capability classifications between domains with different trust frameworks.\n\nConfiguration guidelines for each deployment profile specify minimum cryptographic requirements, key management practices, and operational procedures necessary to achieve the target assurance level. Development Profile deployments MAY use simplified key management with local certificate authorities and reduced audit granularity, while Enterprise Profile deployments MUST implement organizational key management policies consistent with business risk assessments and applicable regulatory requirements. High-Assurance Profile configurations MUST implement defense-in-depth approaches including hardware security modules for key protection, formal verification of configuration integrity, and continuous monitoring for provenance chain anomalies or attacks. All deployment profiles MUST support migration paths to higher assurance levels, enabling the same agent infrastructure to operate across varying trust requirements while maintaining verifiable provenance chains through configuration upgrades rather than architectural changes.", "The APAE framework introduces novel security challenges beyond traditional identity and authentication systems due to the dynamic, autonomous nature of AI agents and the distributed character of their operational environments. The framework MUST address threats across multiple attack vectors including identity spoofing, provenance chain manipulation, capability misrepresentation, and audit trail tampering. Unlike static systems, AI agents can modify their behavior, delegate authority, and evolve capabilities during runtime, creating attack surfaces that extend beyond initial deployment security assumptions. The security model MUST account for the fact that agents operate across trust boundaries, potentially in adversarial environments, while maintaining verifiable provenance chains that can be validated by third parties without exposing sensitive operational details.\n\nPrivacy preservation represents a critical security requirement as provenance chains inherently contain sensitive information about agent capabilities, operational patterns, and delegation relationships. The framework MUST implement selective disclosure mechanisms that allow verification of specific provenance claims without revealing the complete operational history, as specified in the Capability Manifest attestation procedures [draft-jiang-seat-dynamic-attestation]. Privacy-preserving techniques such as zero-knowledge proofs, selective redaction, and differential privacy SHOULD be employed to protect sensitive provenance data while maintaining verification capabilities. Cross-domain provenance sharing MUST implement privacy controls that respect jurisdictional boundaries and organizational policies, ensuring that provenance verification can occur without inappropriate information disclosure between administrative domains.\n\nTamper resistance mechanisms are essential for maintaining the integrity of provenance chains throughout the agent lifecycle. The framework MUST employ cryptographic techniques including digital signatures, hash chains, and merkle trees to ensure that provenance records cannot be modified or deleted without detection. Agent Identity Preservation patterns MUST be implemented to maintain cryptographic binding between agent identity and provenance records across delegation chains and cross-domain operations [draft-liu-oauth-a2a-profile]. The framework SHOULD implement distributed ledger or consensus mechanisms for high-assurance deployments where centralized tamper resistance is insufficient. All provenance anchors MUST be cryptographically bound to hardware security modules or trusted execution environments in high-assurance configurations to prevent root-level compromise of the provenance chain.\n\nThe threat model encompasses both external adversaries attempting to compromise agent provenance and insider threats from compromised agents or malicious operators within trusted domains. External threats include provenance spoofing attacks where adversaries attempt to forge agent identities or capability claims, replay attacks using captured provenance tokens, and man-in-the-middle attacks during cross-domain provenance verification. The framework MUST implement robust authentication and authorization controls as defined in the Traceable Identity and Provenance requirements [draft-chen-ai-agent-auth-new-requirements], including time-bound attestations, nonce-based replay protection, and mutual authentication during provenance exchanges. Insider threat mitigation MUST include separation of duties for provenance administration, cryptographic audit logs that cannot be modified by system administrators, and anomaly detection for unusual provenance patterns that may indicate compromise.\n\nAttack mitigation strategies MUST address the unique characteristics of AI agent systems including their ability to rapidly scale, adapt, and potentially collaborate in unexpected ways. The framework SHOULD implement rate limiting and reputation mechanisms to prevent provenance flooding attacks where adversaries attempt to overwhelm verification systems with invalid provenance claims. Behavioral analysis MUST be employed to detect agents operating outside their declared capabilities or exhibiting patterns inconsistent with their provenance claims. The audit trail federation mechanisms MUST include Byzantine fault tolerance to ensure that provenance verification remains reliable even when some participating systems are compromised or behaving maliciously.\n\nSecurity controls for provenance data MUST include encryption at rest and in transit, access controls based on least privilege principles, and secure key management for all cryptographic operations. The framework MUST integrate with existing PKI infrastructure [RFC5280] and modern cryptographic standards [RFC9052] while supporting post-quantum cryptographic algorithms for long-term provenance chain protection. High-assurance deployments MUST implement hardware security modules for key storage and cryptographic operations, while lightweight deployments MAY use software-based cryptographic implementations with appropriate security controls. All security controls MUST be configurable based on deployment profiles to ensure that the same framework can support varying security requirements from development environments to regulated production systems.", "This document requires several IANA registrations to establish the necessary protocol elements for the Agent Provenance Assurance Ecosystem (APAE) Framework. The registrations encompass URI schemes for agent identification and provenance tracking, MIME types for provenance data formats, and registry namespaces for standardized provenance attributes and attestation levels.\n\nThe framework introduces new URI schemes that MUST be registered with IANA to enable consistent agent identification and provenance chain references across implementations. The \"agent-id\" URI scheme provides a standardized format for globally unique agent identifiers that preserve identity across delegation chains and administrative domains. The \"provenance-chain\" URI scheme enables direct references to specific provenance records and audit trail segments, supporting cross-domain provenance verification as specified in Section 8. These URI schemes follow the registration procedures defined in [RFC7595] and include comprehensive security considerations for preventing identifier collision and spoofing attacks.\n\nNew MIME types are required for the structured data formats defined by the APAE framework, particularly for capability manifests and provenance audit records. The \"application/agent-manifest+json\" MIME type identifies capability manifest documents as specified in Section 6, enabling proper content negotiation and validation of agent capability attestations. The \"application/provenance-audit+cbor\" MIME type identifies binary-encoded audit trail records that provide tamper-evident operational history logging. These MIME types support both human-readable JSON formats for development environments and compact CBOR encodings for high-throughput production deployments, aligning with the deployment profiles described in Section 9.\n\nThe framework establishes a new IANA registry for standardized provenance attributes and attestation levels to ensure interoperability across different APAE implementations. The \"Agent Provenance Attributes\" registry defines canonical attribute names for agent identity properties, capability descriptors, and operational metadata that appear in provenance chains and audit records. The \"Agent Attestation Levels\" registry provides standardized identifiers for different levels of cryptographic and procedural assurance, ranging from self-signed development attestations to hardware-backed high-assurance certifications required in regulated environments.\n\nRegistration of the \"apae\" namespace is requested for use in JSON Web Token (JWT) claims [RFC7519] and CBOR Web Token (CWT) claims [RFC9052] that carry agent provenance information within existing token-based authentication and authorization flows. This namespace ensures that APAE-specific claims do not conflict with existing JWT/CWT claim names while enabling seamless integration with OAuth 2.0 [RFC6749] and related authorization frameworks. The namespace registration includes provisions for hierarchical claim organization to support future extensions of the framework without requiring additional IANA actions."]

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


           Agent Provenance Assurance Ecosystem (APAE) Framework
           draft-agent-ecosystem-agent-provenance-assurance-ecosystem-00

  Abstract

     The proliferation of AI agents across distributed systems creates
     critical challenges for establishing verifiable chains of
     identity, capabilities, and operational history. This document
     defines the Agent Provenance Assurance Ecosystem (APAE) Framework,
     a comprehensive system for tracking, verifying, and assuring the
     provenance of AI agents throughout their lifecycle. The framework
     establishes a multi-layered architecture encompassing agent
     identity attestation, capability verification, operational audit
     trails, and cross-domain provenance tracking. APAE integrates with
     existing identity management systems, authorization protocols, and
     communication frameworks to provide end-to-end provenance
     assurance without disrupting current agent ecosystems. The
     framework supports both lightweight deployments for development
     environments and high-assurance configurations for regulated
     industries, enabling the same agent infrastructure to operate
     across varying trust requirements while maintaining verifiable
     provenance chains.

  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 Provenance Chain
        A verifiable sequence of records documenting an agent's
        identity, capabilities, delegations, and operational history
        from creation through current state

     Attestation Level
        A classification of the strength and scope of cryptographic and
        procedural evidence supporting agent identity and capability
        claims

     Capability Manifest
        A signed document describing an agent's current models, tools,
        policies, and operational constraints with cryptographic
        integrity protection

     Provenance Anchor
        A cryptographically verifiable root of trust that establishes
        the initial identity and capabilities of an agent at creation
        time

     Cross-Domain Provenance
        The preservation and verification of agent provenance
        information across different administrative domains, trust
        boundaries, and protocol ecosystems

     Audit Trail Federation
        The controlled sharing and verification of agent operational
        logs across multiple systems while preserving privacy and
        security constraints


  Table of Contents

     1.  Introduction  ................................................  3
     2.  Terminology  .................................................  4
     3.  Problem Statement and Requirements  ..........................  5
     4.  APAE Architecture Overview  ..................................  6
     5.  Agent Identity and Attestation Layer  ........................  7
     6.  Capability and Manifest Verification  ........................  8
     7.  Provenance Audit and Logging Framework  ......................  9
     8.  Cross-Ecosystem Integration Patterns  ........................  10
     9.  Deployment Profiles and Trust Models  ........................  11
     10.  Security Considerations  .....................................  12
     11.  IANA Considerations  .........................................  13
     12.  References  ..................................................  14

  1.  Introduction

     The deployment of AI agents across distributed systems has
     fundamentally transformed how automated decision-making and task
     execution occur in modern computing environments. As organizations
     increasingly rely on AI agents for critical operations ranging
     from customer service interactions to financial transaction
     processing and infrastructure management, the need for verifiable
     provenance tracking has become paramount. Unlike traditional
     software components with static capabilities, AI agents exhibit
     dynamic behavior through model updates, policy modifications, and
     capability evolution, creating complex challenges for establishing
     trust and accountability in distributed deployments [draft-chen-
     ai-agent-auth-new-requirements].

     Current approaches to agent identity and authorization, while
     addressing immediate authentication needs, fail to provide
     comprehensive visibility into agent provenance across their
     operational lifecycle. Organizations lack standardized mechanisms
     to verify not only who created an agent, but also what models it
     currently runs, which policies govern its behavior, how its
     capabilities have evolved over time, and what operations it has
     performed across distributed systems. This gap becomes
     particularly critical when agents operate across organizational
     boundaries, undergo delegation to other agents, or when regulatory
     compliance requires detailed audit trails of automated decision-
     making processes [draft-jiang-seat-dynamic-attestation].

     The challenges of agent provenance extend beyond simple identity
     verification to encompass the full spectrum of agent lifecycle
     management. Agent creation involves establishing cryptographic
     roots of trust and initial capability manifests. Agent delegation
     requires preserving provenance chains while enabling authorized
     capability transfer between entities. Capability evolution demands
     continuous attestation as agents load new models, update policies,
     or modify their operational constraints. Operational history
     tracking necessitates comprehensive audit trails that maintain
     integrity across system boundaries while supporting privacy
     preservation and selective disclosure requirements [draft-liu-
     oauth-a2a-profile].

     Existing identity management systems, authorization protocols, and
     audit frameworks provide essential building blocks but lack the
     specialized constructs needed for comprehensive agent provenance
     assurance. Standard identity protocols such as OAuth 2.0 [RFC6749]
     and JWT [RFC7519] enable agent authentication but do not capture
     the dynamic nature of agent capabilities or provide mechanisms for
     tracking operational history. Certificate-based systems [RFC5280]
     can establish initial identity but fail to accommodate the
     continuous evolution of agent capabilities. Current audit systems
     focus on user actions rather than autonomous agent behavior,
     creating gaps in accountability for automated operations.

     This document defines the Agent Provenance Assurance Ecosystem
     (APAE) Framework to address these challenges through a
     comprehensive, multi-layered approach to agent provenance
     tracking. APAE establishes standardized mechanisms for agent
     identity attestation, capability verification, operational audit
     trails, and cross-domain provenance preservation. The framework
     integrates with existing authentication and authorization systems
     while extending them with agent-specific provenance capabilities,
     enabling organizations to retrofit provenance assurance into
     deployed agent ecosystems without disrupting current operations.

     The scope of APAE encompasses the complete agent lifecycle from
     initial creation through operational retirement. This includes
     establishing provenance anchors at agent creation, maintaining
     verifiable capability manifests as agents evolve, tracking
     delegation relationships and authority transfers, logging
     operational activities with cryptographic integrity, and
     federating provenance information across organizational and
     protocol boundaries. The framework supports flexible deployment
     models ranging from lightweight development environments to high-
     assurance configurations required by regulated industries,
     enabling the same underlying infrastructure to adapt to varying
     trust requirements while maintaining consistent provenance
     semantics across all deployment contexts.

  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 document defines the following terms for use within the Agent
     Provenance Assurance Ecosystem (APAE) Framework:

     Agent: An autonomous software entity that acts on behalf of users,
     services, or other agents, typically incorporating AI/ML models
     for decision-making and task execution as defined in [draft-chen-
     ai-agent-auth-new-requirements].

     Agent Identity Primitive: A fundamental cryptographic construct
     (such as a public key, certificate, or distributed identifier)
     that serves as the basis for agent authentication and identity
     verification throughout the agent's lifecycle.

     Agent Provenance Chain: A chronologically ordered,
     cryptographically linked sequence of records that documents an
     agent's complete history including initial creation, identity
     establishment, capability modifications, delegation relationships,
     and operational activities. Each link in the chain MUST be
     verifiable and tamper-evident, forming an immutable audit trail
     from the agent's provenance anchor through its current operational
     state.

     Attestation Level: A standardized classification system that
     quantifies the strength, scope, and trustworthiness of evidence
     supporting agent identity and capability claims. Attestation
     levels range from basic self-assertions suitable for development
     environments to high-assurance cryptographic attestations meeting
     regulatory compliance requirements. Higher attestation levels
     require stronger cryptographic evidence, more rigorous validation
     procedures, and enhanced tamper-resistance mechanisms.

     Capability Manifest: A structured, digitally signed document that
     comprehensively describes an agent's current operational
     configuration including loaded AI/ML models, available tools and
     APIs, active behavioral policies, operational constraints, and
     security boundaries. As specified in [draft-jiang-seat-dynamic-
     attestation], capability manifests provide cryptographically
     verifiable assurance about an agent's actual capabilities and
     constraints at the time of attestation.

     Cross-Domain Provenance: The preservation, validation, and
     verification of agent provenance information as agents operate
     across heterogeneous administrative domains, trust boundaries,
     protocol ecosystems, and organizational jurisdictions. Cross-
     domain provenance ensures that provenance chains remain intact and
     verifiable even when agents interact with systems using different
     identity management frameworks, authorization protocols, or
     security models as described in [draft-liu-oauth-a2a-profile].

     Delegation Chain: A verifiable sequence of authorization grants
     and capability transfers that documents how operational authority
     and permissions flow from an originating principal (user, service,
     or agent) through intermediary agents to the currently acting
     agent. Each delegation in the chain MUST be cryptographically
     verifiable and include scope limitations, temporal constraints,
     and revocation mechanisms.

     Operational Audit Trail: A tamper-evident log of agent activities,
     decisions, and state changes that provides accountability and
     forensic capabilities for agent operations. Audit trails include
     sufficient detail to reconstruct agent behavior while preserving
     privacy and confidentiality requirements through selective
     disclosure and zero-knowledge proof mechanisms.

     Provenance Anchor: The cryptographically verifiable root of trust
     that establishes the foundational identity and initial
     capabilities of an agent at creation time. Provenance anchors
     serve as the starting point for all subsequent provenance chain
     entries and MUST be issued by trusted authorities with appropriate
     key management and certificate lifecycle processes as defined in
     [RFC5280].

     Provenance Registry: A distributed system that stores, indexes,
     and provides query interfaces for agent provenance information
     while maintaining appropriate access controls and privacy
     protections. Registries enable cross-domain provenance lookup,
     verification, and audit trail federation across organizational
     boundaries.

     Trust Boundary: A logical or physical demarcation point where
     different security domains, administrative authorities, or trust
     models intersect, requiring explicit validation and translation of
     provenance information to maintain chain integrity and
     verifiability.

     These terms are used consistently throughout this specification to
     ensure clarity and interoperability in multi-stakeholder
     environments deploying agent provenance assurance capabilities.

  3.  Problem Statement and Requirements

     The emergence of autonomous AI agents in distributed systems
     presents unprecedented challenges for establishing verifiable
     chains of identity, capabilities, and operational history. Unlike
     traditional software components with static configurations, AI
     agents exhibit dynamic behavior patterns, evolve their
     capabilities through learning and tool acquisition, and operate
     through complex delegation relationships that span multiple
     administrative domains. Current identity and authorization
     frameworks, while adequate for human users and simple service-to-
     service authentication, lack the semantic richness and temporal
     granularity required to track agent provenance across their full
     lifecycle. The absence of standardized provenance tracking
     mechanisms creates significant risks including unauthorized
     capability escalation, untraceable delegation chains, and
     inability to audit agent decision-making processes in regulated
     environments.

     Agent delegation presents particular challenges as agents
     frequently spawn sub-agents, delegate tasks to peer agents, or
     operate on behalf of other entities through complex authorization
     chains. Traditional OAuth 2.0 [RFC6749] delegation models assume
     relatively simple delegation relationships and static capability
     sets, whereas agent-to-agent delegation involves dynamic
     capability inheritance, constraint propagation, and context-
     dependent authorization decisions. The Agent Authorization Profile
     [draft-aap-oauth-profile] addresses some of these challenges
     through structured claims, but requires a comprehensive provenance
     framework to establish the trustworthiness of those claims across
     domain boundaries. Furthermore, agents may acquire new
     capabilities through model updates, tool integrations, or policy
     modifications during their operational lifetime, necessitating
     continuous provenance tracking rather than static identity
     attestation.

     Cross-ecosystem interoperability compounds these challenges as
     agents must operate across heterogeneous systems with varying
     trust models, identity providers, and security requirements. An
     agent created in one organization's development environment may
     later operate in a partner's production system or a regulated
     cloud service, each requiring different levels of provenance
     assurance and audit granularity. Existing identity management
     protocols like SCIM [draft-abbey-scim-agent-extension] provide
     mechanisms for agent identity synchronization, but lack the
     semantic frameworks necessary to verify capability claims and
     operational constraints across trust boundaries. The absence of
     standardized provenance interchange formats prevents comprehensive
     audit trails and limits the ability to establish end-to-end
     accountability for agent actions.

     The APAE framework MUST provide verifiable agent identity
     establishment and preservation mechanisms that support
     cryptographic attestation of agent creation, modification, and
     delegation events. The framework MUST enable tracking of
     capability evolution including model updates, tool acquisitions,
     and policy changes with tamper-evident audit trails. Agent
     delegation chains MUST be cryptographically verifiable with
     support for constraint inheritance and capability subset
     enforcement as specified in the authentication requirements for AI
     agents [draft-chen-ai-agent-auth-new-requirements]. The framework
     MUST support cross-domain provenance tracking that preserves
     verifiability while respecting privacy and security boundaries
     between administrative domains.

     The framework MUST integrate with existing OAuth 2.0 [RFC6749] and
     JWT [RFC7519] infrastructure without requiring protocol
     modifications to deployed systems. Human oversight requirements
     MUST be traceable through provenance chains with verifiable
     enforcement of human-in-the-loop constraints for sensitive
     operations. The framework MUST support multiple assurance levels
     ranging from lightweight development environments to high-
     assurance regulated deployments with appropriate cryptographic
     strength and audit granularity. Operational audit trails MUST
     provide sufficient detail for regulatory compliance while
     supporting privacy-preserving federation across organizational
     boundaries.

     Security requirements include protection against provenance
     tampering, replay attacks, and unauthorized capability escalation
     through cryptographic integrity mechanisms. The framework MUST
     provide defense against agent impersonation, delegation chain
     forgery, and capability manifest manipulation while maintaining
     acceptable performance characteristics for high-volume agent
     interactions. Privacy preservation MUST be supported through
     selective disclosure mechanisms and zero-knowledge proofs where
     full provenance transparency conflicts with confidentiality
     requirements. The framework MUST support revocation and recovery
     mechanisms for compromised agent identities while preserving
     historical audit trail integrity.

  4.  APAE Architecture Overview

     The Agent Provenance Assurance Ecosystem (APAE) Framework employs
     a multi-layered architecture designed to provide comprehensive
     provenance tracking while maintaining compatibility with existing
     agent infrastructures. The architecture consists of four primary
     layers: the Identity Attestation Layer, the Capability and
     Manifest Verification Layer, the Provenance Audit and Logging
     Layer, and the Registry and Discovery Layer. Each layer operates
     independently while providing well-defined interfaces for inter-
     layer communication and data exchange. This modular design enables
     selective deployment of framework components based on specific
     operational requirements and trust models, allowing organizations
     to implement provenance assurance incrementally without disrupting
     existing agent ecosystems.

     The Identity Attestation Layer serves as the foundational trust
     anchor for the APAE framework, establishing and maintaining
     verifiable agent identities throughout their operational
     lifecycle. This layer implements cryptographic attestation
     mechanisms compatible with existing identity management systems as
     specified in [RFC6749] and emerging agent authentication
     requirements outlined in [draft-chen-ai-agent-auth-new-
     requirements]. Agent identities are established through Provenance
     Anchors that create immutable cryptographic bindings between agent
     instances and their initial capability sets. The layer maintains
     delegation chains that preserve identity continuity across agent
     spawning, capability updates, and cross-domain transfers, ensuring
     that each agent's lineage remains verifiable regardless of
     operational complexity or administrative boundaries.

     The Capability and Manifest Verification Layer implements the AI
     Agent Manifest Attestation mechanism to provide continuous
     assurance of agent capabilities and operational constraints. This
     layer generates and validates Capability Manifests that
     cryptographically bind agent identities to their current models,
     tools, policies, and Working Memory configurations as described in
     [draft-jiang-seat-dynamic-attestation]. Manifest verification
     occurs through a combination of static analysis and runtime
     attestation, ensuring that declared capabilities match actual
     agent implementations. The layer integrates with authorization
     frameworks defined in [draft-liu-oauth-a2a-profile] to enforce
     capability-based access controls and prevent agents from exceeding
     their declared operational boundaries.

     The Provenance Audit and Logging Layer captures comprehensive
     operational histories through tamper-evident audit trails that
     document agent actions, decisions, and state transitions. This
     layer implements Audit Trail Federation mechanisms that enable
     secure sharing of provenance information across administrative
     domains while preserving privacy and confidentiality requirements.
     The logging framework supports both real-time audit streaming for
     high-assurance environments and batch processing for performance-
     sensitive deployments. Audit records are cryptographically linked
     to agent identities and capability manifests, creating verifiable
     chains of operational evidence that support compliance, debugging,
     and security analysis requirements.

     The Registry and Discovery Layer provides the infrastructure
     services necessary for cross-ecosystem provenance verification and
     agent lifecycle management. This layer implements Ecosystem
     Registries that resolve agent identifiers to concrete endpoints
     while maintaining provenance metadata for discovery and
     verification operations. The registry services support
     hierarchical trust models that enable both centralized and
     federated deployment patterns, allowing organizations to maintain
     local control over agent registration while participating in
     broader provenance ecosystems. Integration with existing DNS and
     PKI infrastructure ensures compatibility with current internet
     protocols while providing the specialized services required for
     agent provenance assurance.

     Component integration occurs through standardized APIs that
     abstract the complexity of provenance operations from agent
     implementations and management systems. The framework defines
     integration points with existing OAuth 2.0 authorization servers
     [RFC6749], JWT token validation systems [RFC7519], and X.509
     certificate authorities [RFC5280] to leverage established trust
     infrastructure. Cross-layer communication employs CBOR Web Token
     (CWT) formats [RFC9052] for efficient provenance data exchange,
     while maintaining compatibility with JSON-based systems through
     bidirectional transformation protocols. This integration approach
     enables the APAE framework to operate as both a standalone
     provenance system and as an enhancement layer for existing agent
     infrastructures, supporting gradual adoption paths that minimize
     deployment risk and operational disruption.

  5.  Agent Identity and Attestation Layer

     The Agent Identity and Attestation Layer forms the foundational
     trust anchor for the APAE framework by establishing
     cryptographically verifiable agent identities and maintaining
     their integrity throughout distributed operations. This layer
     defines mechanisms for creating immutable Provenance Anchors at
     agent instantiation, enabling subsequent verification of agent
     authenticity and delegation chains across administrative
     boundaries. The attestation mechanisms MUST support multiple trust
     models ranging from self-signed certificates for development
     environments to hardware-backed attestation for high-assurance
     deployments, ensuring consistent identity semantics while
     accommodating diverse operational requirements.

     Agent identity establishment begins with the creation of a
     cryptographic identity pair and corresponding Provenance Anchor
     that binds the agent's initial state, capabilities, and authorized
     delegation scope. The Provenance Anchor MUST include the agent's
     public key, creation timestamp, initial Capability Manifest hash,
     and attestation metadata as defined in [RFC5280] certificate
     extensions. For agents created through delegation, the Provenance
     Anchor MUST reference the parent agent's identity and include a
     delegation certificate chain as specified in Section 4.3 of
     [draft-aap-oauth-profile]. The attestation layer SHOULD integrate
     with existing Public Key Infrastructure (PKI) systems and identity
     providers to leverage established trust relationships while
     maintaining agent-specific provenance requirements.

     Identity preservation across transactions requires embedding agent
     identity information within protocol tokens to maintain provenance
     visibility throughout distributed agent interactions. Following
     the patterns established in [draft-liu-oauth-a2a-profile], the
     attestation layer MUST encode agent identity claims within JWT
     tokens [RFC7519] using structured claims that capture agent
     context, delegation chains, and operational constraints. The "sub"
     claim MUST identify the agent using a stable identifier, while
     custom claims including "agentid", "delegationchain", and
     "capability_hash" provide machine-readable context for
     authorization decisions. Token validation procedures MUST verify
     both the cryptographic integrity of the token and the validity of
     embedded delegation chains against current agent registry state.

     Delegation chain validation ensures that agents acting on behalf
     of humans or other agents maintain verifiable authorization
     inheritance throughout multi-hop delegation scenarios. The
     attestation layer MUST implement validation rules that verify each
     delegation link's cryptographic signature, temporal validity, and
     scope constraints as defined in Section 5.2 of [draft-aap-oauth-
     profile]. Validation procedures MUST check that each delegation in
     the chain is properly authorized by its parent and that the
     cumulative delegation scope does not exceed the original
     authorization grant. For cross-domain delegations, the layer MUST
     support federation mechanisms that enable delegation chain
     verification across different administrative domains while
     preserving local policy enforcement.

     Integration with existing identity management systems requires
     protocol adapters that bridge APAE agent identity concepts with
     established authentication and authorization frameworks including
     OAuth 2.0 [RFC6749], SAML, and enterprise identity providers. The
     attestation layer MUST provide standardized interfaces for
     identity provider integration that preserve agent provenance
     information while leveraging existing user directories and policy
     engines. For environments requiring hardware-backed attestation,
     the layer SHOULD integrate with Trusted Platform Module (TPM)
     attestation mechanisms and secure enclave technologies to provide
     cryptographic proof of agent execution environment integrity as
     specified in [draft-jiang-seat-dynamic-attestation].

     The attestation layer maintains agent identity lifecycle
     management including identity renewal, revocation, and migration
     scenarios that preserve provenance chain integrity across
     operational changes. Identity renewal procedures MUST generate new
     cryptographic material while maintaining linkage to previous
     identity states through signed transition records. Revocation
     mechanisms MUST support both immediate revocation for security
     incidents and scheduled revocation for planned agent retirement,
     with appropriate notification to dependent systems. For agent
     migration scenarios, the layer MUST provide protocols for securely
     transferring agent identity and associated provenance data between
     execution environments while maintaining cryptographic integrity
     and audit trail continuity.

  6.  Capability and Manifest Verification

     The APAE framework establishes comprehensive mechanisms for
     verifying agent capabilities, loaded models, active policies, and
     operational constraints through structured capability manifests
     and cryptographic validation procedures. Agents MUST maintain up-
     to-date capability manifests that accurately reflect their current
     operational state, including all loaded models, available tools,
     active security policies, and operational constraints. These
     manifests serve as authoritative declarations of agent
     capabilities that can be independently verified by other agents,
     infrastructure components, and human operators. The framework
     supports both static capability declarations established at agent
     creation time and dynamic capability updates that reflect runtime
     changes to agent configuration.

     Capability manifests MUST be structured as JSON documents
     conforming to a standardized schema that includes sections for
     model identification, tool descriptions, policy specifications,
     and operational metadata. Each manifest MUST include cryptographic
     signatures from the agent's identity key as defined in Section 5,
     enabling verification of manifest authenticity and integrity. The
     manifest format incorporates semantic capability descriptions that
     support capability-based discovery mechanisms, allowing agents to
     locate and interact with other agents based on functional
     requirements rather than network topology. Manifests MAY include
     capability hierarchies and dependency relationships that describe
     how complex capabilities are composed from simpler primitives,
     enabling fine-grained capability matching and verification.

     The framework defines validation procedures that enable both local
     and remote verification of agent capabilities through multiple
     assurance mechanisms. Agents MUST implement self-attestation
     procedures that generate cryptographic proofs of their current
     capability state, including verification that loaded models match
     declared specifications and that active policies are properly
     enforced. The framework supports oracle-free evaluation mechanisms
     that allow agents to demonstrate their capabilities through
     standardized test interactions without requiring external ground
     truth verification systems. For higher assurance levels, the
     framework MAY utilize remote attestation procedures where trusted
     third parties verify agent capabilities through controlled testing
     environments or hardware-based attestation mechanisms.

     Capability verification integrates with the APAE audit framework
     to maintain comprehensive records of capability changes and
     verification events throughout an agent's operational lifecycle.
     All capability updates MUST be logged to the provenance audit
     trail with cryptographic timestamps and verification evidence. The
     framework supports incremental capability verification where only
     modified capabilities require re-validation, reducing
     computational overhead while maintaining security properties.
     Verification procedures MUST account for capability delegation
     scenarios where agents operate with restricted or derived
     capabilities from their base configuration, ensuring that
     delegation constraints are properly enforced and documented.

     The framework establishes integration patterns with existing
     authorization and policy systems through standardized capability
     assertion formats compatible with OAuth 2.0 scopes [RFC6749] and
     JWT claims [RFC7519]. Capability manifests MAY be embedded in
     access tokens or presented as separate attestation documents
     depending on the integration requirements of the target system.
     The framework supports prioritization mechanisms that ensure
     critical capability verification traffic receives appropriate
     network and processing priority, particularly in high-throughput
     agent communication scenarios. Capability verification procedures
     MUST preserve privacy by supporting selective disclosure of
     capability information while maintaining the ability to verify
     relevant capabilities for specific interactions.

  7.  Provenance Audit and Logging Framework

     The provenance audit and logging framework establishes
     comprehensive mechanisms for recording, storing, and verifying the
     operational history of AI agents throughout their lifecycle. This
     framework builds upon the identity attestation and capability
     verification layers to create tamper-evident audit trails that
     preserve agent provenance across administrative domains and
     protocol boundaries. The audit framework integrates with existing
     logging infrastructure while providing specialized capabilities
     for agent-specific events including delegation chains, capability
     modifications, and cross-domain interactions.

     Agent operations MUST generate structured audit records using a
     standardized format that includes cryptographic integrity
     protection and temporal ordering guarantees. Each audit record
     contains the agent identity from the attestation layer, operation
     type, relevant capability manifest references, timestamp with
     cryptographic time-binding, and operation-specific metadata. The
     audit record format builds upon JSON Web Signature [RFC7515]
     structures to ensure cryptographic integrity while maintaining
     human readability for operational troubleshooting. Records MUST
     include sufficient context to reconstruct the agent's state and
     decision-making process at the time of the logged operation,
     enabling comprehensive provenance verification.

     The framework defines multiple audit trail storage models to
     accommodate different deployment profiles and trust requirements.
     Local audit storage provides high-performance logging for single-
     domain deployments, while distributed audit architectures enable
     cross-domain provenance tracking through secure federation
     protocols. Audit trails MAY be stored in blockchain-based systems
     for maximum tamper resistance, traditional database systems with
     cryptographic integrity checking, or hybrid approaches that
     balance performance and assurance requirements. The storage layer
     MUST implement retention policies that comply with regulatory
     requirements while supporting efficient querying and verification
     operations across potentially large audit datasets.

     Cross-domain audit trail federation enables provenance tracking as
     agents operate across multiple systems and administrative
     boundaries. The federation protocol builds upon the Agent
     Communication Gateway architecture [draft-agent-gw] to establish
     secure channels for audit trail sharing while preserving privacy
     and confidentiality constraints. Federation participants MUST
     implement access control policies that govern which audit
     information can be shared across domain boundaries, ensuring that
     sensitive operational details remain protected while enabling
     necessary provenance verification. The federation protocol
     supports both real-time audit streaming for active monitoring and
     batch synchronization for historical audit trail reconstruction.

     Audit trail verification mechanisms provide cryptographic
     assurance of audit record integrity and completeness across
     distributed storage systems. Verification protocols MUST detect
     tampering, insertion attacks, and selective deletion of audit
     records while providing efficient proof generation for compliance
     reporting. The framework integrates with QUIC-based
     publish/subscribe systems [draft-a2a-moqt-transport] to enable
     real-time audit verification and alerting when provenance
     violations are detected. Verification results are themselves
     logged to create a complete audit chain that can demonstrate the
     integrity of the provenance tracking system to external auditors
     and regulatory authorities.

     The audit framework defines standard retention policies and
     archival procedures that balance operational requirements with
     storage efficiency and regulatory compliance. Short-term audit
     data SHOULD be stored with high availability and low latency
     access to support real-time monitoring and incident response,
     while long-term archival systems focus on integrity preservation
     and cost-effective storage. Automated policy enforcement ensures
     that audit data transitions through appropriate lifecycle stages
     while maintaining cryptographic integrity and chain-of-custody
     documentation required for legal and regulatory purposes.

  8.  Cross-Ecosystem Integration Patterns

     The APAE framework is designed to integrate seamlessly with
     existing identity, authorization, and communication ecosystems
     through well-defined integration patterns. These patterns enable
     organizations to retrofit provenance assurance capabilities into
     deployed agent systems without requiring wholesale replacement of
     existing infrastructure. The integration approach leverages
     established protocols and standards while extending them with
     agent-specific provenance metadata and verification mechanisms.

     Integration with OAuth 2.0 and OpenID Connect ecosystems follows
     the Agent Authorization Profile (AAP) [draft-aap-oauth-profile]
     extension pattern. Existing OAuth 2.0 authorization servers can be
     extended to issue agent-specific access tokens that include
     provenance claims alongside traditional authorization claims. The
     APAE framework defines standardized JWT [RFC7519] claim extensions
     that carry provenance chain identifiers, attestation levels, and
     capability manifest references within OAuth access tokens.
     Authorization servers MUST validate provenance claims during token
     issuance and MAY enforce provenance-based authorization policies.
     Resource servers can extract provenance information from access
     tokens to make fine-grained authorization decisions based on agent
     identity, delegation chains, and operational constraints without
     requiring separate provenance lookups.

     Identity management systems integration leverages the SCIM Agent
     Extension [draft-abbey-scim-agent-extension] to represent agents
     as first-class identity resources. Existing SCIM [RFC7644]
     deployments can be extended with agent resource types and user
     schema extensions for agent relationships, enabling centralized
     management of agent identities alongside human users. The APAE
     framework specifies how provenance anchors and capability
     manifests are stored as extended attributes within SCIM agent
     resources. Identity providers MUST maintain provenance metadata
     consistency when provisioning or updating agent identities across
     connected systems. User schema extensions enable tracking of
     delegation relationships between human users and their associated
     agents, supporting organizational governance requirements for
     agent oversight.

     Communication protocol integration patterns address both
     synchronous and asynchronous agent interactions across different
     transport mechanisms. For HTTP-based communications, the framework
     defines standardized headers that carry provenance attestations
     and audit trail references alongside existing authentication
     headers. Message-based systems can embed provenance metadata
     within message headers or signed payloads using COSE [RFC9052]
     structures for cryptographic integrity. The framework specifies
     how existing API gateways and service meshes can be configured to
     extract, validate, and propagate provenance information across
     service boundaries. Protocol adapters SHOULD preserve provenance
     chains when translating between different communication protocols
     or message formats.

     Retrofitting guidance for deployed systems emphasizes incremental
     adoption patterns that minimize operational disruption.
     Organizations can begin by deploying APAE registry services
     alongside existing infrastructure to collect provenance data
     without enforcing verification policies. Existing agents can be
     gradually onboarded by adding provenance metadata to their
     identity records and configuring audit logging to feed into APAE
     audit trail storage. The framework supports hybrid deployments
     where some agents participate in full provenance tracking while
     others operate with reduced assurance levels during transition
     periods. Migration tooling can extract existing audit logs and
     identity information to bootstrap provenance chains for previously
     deployed agents.

     Legacy system integration addresses scenarios where existing agent
     systems cannot be directly modified to support APAE protocols.
     Proxy components can be deployed to intercept agent communications
     and inject provenance metadata based on static configuration or
     external attestation sources. Gateway services can perform
     provenance validation on behalf of legacy systems while presenting
     traditional authentication and authorization interfaces to
     unchanged applications. The framework defines compatibility modes
     that allow APAE-enabled systems to interact safely with non-APAE
     systems while maintaining provenance tracking for supported
     interactions. Organizations SHOULD document provenance gaps when
     integrating with legacy systems and implement compensating
     controls where full provenance assurance cannot be achieved.

  9.  Deployment Profiles and Trust Models

     The APAE framework defines three primary deployment profiles to
     accommodate varying operational requirements, risk tolerances, and
     regulatory constraints across different environments. Each profile
     represents a distinct configuration of trust anchors, verification
     requirements, and assurance mechanisms while maintaining
     architectural compatibility to enable agent mobility between
     environments.

     The Development Profile provides minimal provenance overhead
     suitable for development, testing, and low-risk operational
     environments. In this configuration, agent identity MAY be
     established through self-signed certificates or local trust
     anchors, capability manifests SHOULD be cryptographically signed
     but MAY use development keys, and audit trails MAY be stored
     locally with basic integrity protection. The Development Profile
     enables rapid iteration and testing while introducing core
     provenance concepts without the infrastructure overhead of higher
     assurance levels. Cross-domain provenance tracking in Development
     Profile deployments SHOULD preserve chain integrity but MAY accept
     lower verification standards for external attestation anchors.

     The Enterprise Profile establishes intermediate assurance levels
     appropriate for business-critical applications, regulated
     industries with moderate risk profiles, and multi-organizational
     collaborations. This profile MUST implement cryptographically
     verifiable provenance anchors rooted in organizational PKI or
     approved certificate authorities, capability manifests MUST be
     signed by authorized capability authorities, and audit trails MUST
     be protected against tampering through cryptographic mechanisms
     such as hash chains or digital signatures per [RFC5280]. The
     Enterprise Profile MUST support human oversight requirements as
     defined in [draft-aap-oauth-profile], enabling enforcement of
     human-in-the-loop controls based on agent capabilities,
     operational context, or regulatory requirements. Cross-domain
     provenance federation in Enterprise deployments MUST implement
     bilateral or multilateral trust agreements with explicit policy
     mappings for attestation level recognition.

     The High-Assurance Profile provides maximum security and
     verification suitable for national security applications, critical
     infrastructure, and highly regulated environments with stringent
     compliance requirements. This profile MUST implement hardware-
     backed provenance anchors using mechanisms consistent with [draft-
     jiang-seat-dynamic-attestation], all capability manifests MUST
     undergo formal verification processes before deployment, and audit
     trails MUST be stored in tamper-evident systems with distributed
     verification. High-Assurance deployments MUST implement real-time
     capability verification, continuous operational monitoring, and
     mandatory human oversight for all agent actions exceeding
     predefined risk thresholds. The High-Assurance Profile MUST
     support air-gapped operation modes where cross-domain provenance
     verification occurs through offline cryptographic proof exchange
     rather than real-time federation protocols.

     Trust models within APAE deployments establish the foundation for
     provenance verification and cross-domain interoperability while
     accommodating different organizational risk appetites and
     regulatory frameworks. The framework defines three trust model
     categories: Direct Trust, where all agents operate within a single
     administrative domain with unified trust anchors; Federated Trust,
     where multiple domains establish bilateral or multilateral
     agreements for provenance recognition with explicit policy
     mappings; and Zero Trust, where each provenance claim undergoes
     independent verification regardless of source domain. Zero Trust
     configurations MUST implement continuous verification of agent
     identity, capabilities, and operational constraints, treating all
     provenance claims as potentially compromised until
     cryptographically verified against current trust anchors.
     Federated Trust models SHOULD implement policy translation
     mechanisms to map attestation levels and capability
     classifications between domains with different trust frameworks.

     Configuration guidelines for each deployment profile specify
     minimum cryptographic requirements, key management practices, and
     operational procedures necessary to achieve the target assurance
     level. Development Profile deployments MAY use simplified key
     management with local certificate authorities and reduced audit
     granularity, while Enterprise Profile deployments MUST implement
     organizational key management policies consistent with business
     risk assessments and applicable regulatory requirements. High-
     Assurance Profile configurations MUST implement defense-in-depth
     approaches including hardware security modules for key protection,
     formal verification of configuration integrity, and continuous
     monitoring for provenance chain anomalies or attacks. All
     deployment profiles MUST support migration paths to higher
     assurance levels, enabling the same agent infrastructure to
     operate across varying trust requirements while maintaining
     verifiable provenance chains through configuration upgrades rather
     than architectural changes.

  10.  Security Considerations

     The APAE framework introduces novel security challenges beyond
     traditional identity and authentication systems due to the
     dynamic, autonomous nature of AI agents and the distributed
     character of their operational environments. The framework MUST
     address threats across multiple attack vectors including identity
     spoofing, provenance chain manipulation, capability
     misrepresentation, and audit trail tampering. Unlike static
     systems, AI agents can modify their behavior, delegate authority,
     and evolve capabilities during runtime, creating attack surfaces
     that extend beyond initial deployment security assumptions. The
     security model MUST account for the fact that agents operate
     across trust boundaries, potentially in adversarial environments,
     while maintaining verifiable provenance chains that can be
     validated by third parties without exposing sensitive operational
     details.

     Privacy preservation represents a critical security requirement as
     provenance chains inherently contain sensitive information about
     agent capabilities, operational patterns, and delegation
     relationships. The framework MUST implement selective disclosure
     mechanisms that allow verification of specific provenance claims
     without revealing the complete operational history, as specified
     in the Capability Manifest attestation procedures [draft-jiang-
     seat-dynamic-attestation]. Privacy-preserving techniques such as
     zero-knowledge proofs, selective redaction, and differential
     privacy SHOULD be employed to protect sensitive provenance data
     while maintaining verification capabilities. Cross-domain
     provenance sharing MUST implement privacy controls that respect
     jurisdictional boundaries and organizational policies, ensuring
     that provenance verification can occur without inappropriate
     information disclosure between administrative domains.

     Tamper resistance mechanisms are essential for maintaining the
     integrity of provenance chains throughout the agent lifecycle. The
     framework MUST employ cryptographic techniques including digital
     signatures, hash chains, and merkle trees to ensure that
     provenance records cannot be modified or deleted without
     detection. Agent Identity Preservation patterns MUST be
     implemented to maintain cryptographic binding between agent
     identity and provenance records across delegation chains and
     cross-domain operations [draft-liu-oauth-a2a-profile]. The
     framework SHOULD implement distributed ledger or consensus
     mechanisms for high-assurance deployments where centralized tamper
     resistance is insufficient. All provenance anchors MUST be
     cryptographically bound to hardware security modules or trusted
     execution environments in high-assurance configurations to prevent
     root-level compromise of the provenance chain.

     The threat model encompasses both external adversaries attempting
     to compromise agent provenance and insider threats from
     compromised agents or malicious operators within trusted domains.
     External threats include provenance spoofing attacks where
     adversaries attempt to forge agent identities or capability
     claims, replay attacks using captured provenance tokens, and man-
     in-the-middle attacks during cross-domain provenance verification.
     The framework MUST implement robust authentication and
     authorization controls as defined in the Traceable Identity and
     Provenance requirements [draft-chen-ai-agent-auth-new-
     requirements], including time-bound attestations, nonce-based
     replay protection, and mutual authentication during provenance
     exchanges. Insider threat mitigation MUST include separation of
     duties for provenance administration, cryptographic audit logs
     that cannot be modified by system administrators, and anomaly
     detection for unusual provenance patterns that may indicate
     compromise.

     Attack mitigation strategies MUST address the unique
     characteristics of AI agent systems including their ability to
     rapidly scale, adapt, and potentially collaborate in unexpected
     ways. The framework SHOULD implement rate limiting and reputation
     mechanisms to prevent provenance flooding attacks where
     adversaries attempt to overwhelm verification systems with invalid
     provenance claims. Behavioral analysis MUST be employed to detect
     agents operating outside their declared capabilities or exhibiting
     patterns inconsistent with their provenance claims. The audit
     trail federation mechanisms MUST include Byzantine fault tolerance
     to ensure that provenance verification remains reliable even when
     some participating systems are compromised or behaving
     maliciously.

     Security controls for provenance data MUST include encryption at
     rest and in transit, access controls based on least privilege
     principles, and secure key management for all cryptographic
     operations. The framework MUST integrate with existing PKI
     infrastructure [RFC5280] and modern cryptographic standards
     [RFC9052] while supporting post-quantum cryptographic algorithms
     for long-term provenance chain protection. High-assurance
     deployments MUST implement hardware security modules for key
     storage and cryptographic operations, while lightweight
     deployments MAY use software-based cryptographic implementations
     with appropriate security controls. All security controls MUST be
     configurable based on deployment profiles to ensure that the same
     framework can support varying security requirements from
     development environments to regulated production systems.

  11.  IANA Considerations

     This document requires several IANA registrations to establish the
     necessary protocol elements for the Agent Provenance Assurance
     Ecosystem (APAE) Framework. The registrations encompass URI
     schemes for agent identification and provenance tracking, MIME
     types for provenance data formats, and registry namespaces for
     standardized provenance attributes and attestation levels.

     The framework introduces new URI schemes that MUST be registered
     with IANA to enable consistent agent identification and provenance
     chain references across implementations. The "agent-id" URI scheme
     provides a standardized format for globally unique agent
     identifiers that preserve identity across delegation chains and
     administrative domains. The "provenance-chain" URI scheme enables
     direct references to specific provenance records and audit trail
     segments, supporting cross-domain provenance verification as
     specified in Section 8. These URI schemes follow the registration
     procedures defined in [RFC7595] and include comprehensive security
     considerations for preventing identifier collision and spoofing
     attacks.

     New MIME types are required for the structured data formats
     defined by the APAE framework, particularly for capability
     manifests and provenance audit records. The "application/agent-
     manifest+json" MIME type identifies capability manifest documents
     as specified in Section 6, enabling proper content negotiation and
     validation of agent capability attestations. The
     "application/provenance-audit+cbor" MIME type identifies binary-
     encoded audit trail records that provide tamper-evident
     operational history logging. These MIME types support both human-
     readable JSON formats for development environments and compact
     CBOR encodings for high-throughput production deployments,
     aligning with the deployment profiles described in Section 9.

     The framework establishes a new IANA registry for standardized
     provenance attributes and attestation levels to ensure
     interoperability across different APAE implementations. The "Agent
     Provenance Attributes" registry defines canonical attribute names
     for agent identity properties, capability descriptors, and
     operational metadata that appear in provenance chains and audit
     records. The "Agent Attestation Levels" registry provides
     standardized identifiers for different levels of cryptographic and
     procedural assurance, ranging from self-signed development
     attestations to hardware-backed high-assurance certifications
     required in regulated environments.

     Registration of the "apae" namespace is requested for use in JSON
     Web Token (JWT) claims [RFC7519] and CBOR Web Token (CWT) claims
     [RFC9052] that carry agent provenance information within existing
     token-based authentication and authorization flows. This namespace
     ensures that APAE-specific claims do not conflict with existing
     JWT/CWT claim names while enabling seamless integration with OAuth
     2.0 [RFC6749] and related authorization frameworks. The namespace
     registration includes provisions for hierarchical claim
     organization to support future extensions of the framework without
     requiring additional IANA actions.

  12.  References

  12.1.  Normative References

     [RFC 2119]
           RFC 2119

     [RFC 8174]
           RFC 8174

     [RFC 6749]
           RFC 6749

     [RFC 7519]
           RFC 7519

     [RFC 5280]
           RFC 5280

     [RFC 9052]
           RFC 9052

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

     [draft-jiang-seat-dynamic-attestation]
           draft-jiang-seat-dynamic-attestation

     [draft-liu-oauth-a2a-profile]
           draft-liu-oauth-a2a-profile

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

  12.2.  Informative References

     [RFC 8446]
           RFC 8446

     [RFC 9110]
           RFC 9110

     [RFC 8126]
           RFC 8126

     [RFC 8259]
           RFC 8259

     [draft-narvaneni-agent-uri]
           draft-narvaneni-agent-uri

     [draft-wendt-stir-vesper]
           draft-wendt-stir-vesper

     [draft-a2a-moqt-transport]
           draft-a2a-moqt-transport

     [draft-abbey-scim-agent-extension]
           draft-abbey-scim-agent-extension

     [draft-agent-gw]
           draft-agent-gw

     [draft-ainp-protocol]
           draft-ainp-protocol

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

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

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


  Author's Address

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

rating_json:
  {"s": "Proposes APAE Framework for tracking AI agent identity, capabilities, and operational history across distributed systems through multi-layered architecture integrating with existing identity/auth systems. Addresses verification and audit trail requirements for agents operating in varying trust environments from development to regulated industries.", "n": 3, "nn": "Agent provenance tracking is relevant but incremental; applies established practices (identity attestation, audit logging, capability verification) to AI agents without fundamental innovations in verification mechanisms or cryptographic approaches.", "m": 2, "mn": "Early-stage framework (draft-00, March 2026 date appears fictional); lacks concrete protocol specifications, implementation guidance, and validation against real distributed agent deployments; abstract-level only.", "o": 4, "on": "Significant overlap with existing identity/authorization standards (OAuth, SAML, SPIFFE), supply chain provenance work (SLSA, SWID), and general audit logging practices; limited AI-agent-specific novelty.", "mo": 2, "mon": "Single early draft with no apparent community adoption signals; addresses real problem space but execution maturity and standardization pathway unclear; AI agent ecosystem still emerging.", "r": 3, "rn": "Moderately relevant for regulated deployments requiring agent auditability; less critical for research or development contexts; timing depends on AI agent adoption in high-assurance domains.", "c": ["Agent identity/auth", "Policy/governance", "Agent discovery/reg", "Data formats/interop", "AI safety/alignment"]}


