# generated_drafts

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"]}


