ietf-draft-analyzer/data/reports/generated-drafts/draft-ai-agent-provenance-assurance-ecosystem-00.txt

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