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. +------------------------------------------------+ | Provenance Query Layer | | (cross-domain search, history retrieval) | +------------------------------------------------+ | Cross-Domain Tracking Layer | | (gateway coordination, chain linking) | +------------------------------------------------+ | Operational Audit Layer | | (action logging, decision trails) | +------------------------------------------------+ | Capability Verification Layer | | (skill attestation, scope validation) | +------------------------------------------------+ | Identity Attestation Layer | | (agent ID, key management, TEE binding) | +------------------------------------------------+ | Trust Infrastructure Layer | | (PKI, trust anchors, revocation) | +------------------------------------------------+ Figure 1: APAE Layered Architecture 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. Origin Org Gateway Target Org +----------+ +---------+ +----------+ | Agent | |Provnce | | Verifier | | Registry |--->| Bridge |--->| Registry | +----+-----+ +----+----+ +----+-----+ | | | +----+-----+ +----+----+ +----+-----+ |Provenance| | Chain | |Provenance| | Record |--->| Linker |--->| Record | +----------+ +---------+ +----------+ Figure 2: Cross-Domain Provenance Tracking 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