AgentFacts Metadata Standard

• AgentFacts Metadata is a universal, verifiable standard for AI agent descriptions, ensuring trust through independent multi-authority attestations.
• It employs dynamic permission management with time-limited annotations to maintain up-to-date, compliant agent capabilities and operational states.
• The extensible metadata schema supports seamless integration and governance across diverse systems, facilitating risk assessment and interoperability.

AgentFacts Metadata refers to a universal, cryptographically verifiable metadata standard for AI agents, designed to provide systematic, independently validated descriptions of agent capabilities, identities, operational parameters, and compliance characteristics across organizational boundaries. The AgentFacts framework addresses critical “Know Your Agent” (KYA) challenges in enterprise AI deployment by shifting from self-declared, unverifiable agent properties to multi-authority verified, dynamically updatable, and governance-ready metadata, thereby transforming agent procurement, management, and oversight at scale (Grogan, 11 Jun 2025).

1. Purpose and Scope of AgentFacts Metadata

AgentFacts was conceived to address the lack of universal, trusted standards for AI agent metadata in enterprise, governmental, and cross-domain contexts. Existing agent onboarding and evaluation rely on self-declared claims or bespoke integration processes, leading to trust gaps and coordination friction. The primary goals are:

• Consistent Disclosure: Enabling structured, machine-readable disclosure of agent core identity, skills, compliance attestations, operational constraints, and provenance.
• Independent Verification: Embedding cryptographically signed attestations from multiple authorities (e.g., security auditors, compliance verifiers, performance evaluators) into the metadata, supporting systematic trust and facilitating risk assessment.
• Dynamic Management: Providing mechanisms for dynamic, fine-grained permission and scope management, ensuring that authorization and confidence in agent states remain current.
• Interoperability: Supplying a schema extensible across organizations, domains, and evolving regulatory regimes, supporting integration with existing metadata standards and translation frameworks (e.g., JSON-LD).

AgentFacts functions analogously to “Know Your Customer” (KYC) in financial compliance, but for AI agents, systematizing evaluation, discovery, and governance.

2. Cryptographic Verification and Multi-Authority Attestations

A central innovation in AgentFacts is the use of cryptographic signatures at the granularity of individual metadata claims or fact bundles:

• Signed Capability Declarations: Each claim—such as API support, protocol compliance, or audited performance metrics—is digitally signed using industry standards (e.g., ECDSA, RSA, and emerging post-quantum schemes). Key signature fields encode the signing authority’s ID, timestamp, verification scope (the facts covered), and a confidence measure.
• Multi-Authority Validation: Rather than consolidating trust in a single party, different authorities independently sign and attest to different sections of the metadata package. Security controls may be attested by a cybersecurity auditor, while legal compliance is validated by a regulatory body.
• Reducing Single Points of Failure: No single authority can compromise the agent’s trust profile. Each validation layer is independently auditable and accountable, enabling nuanced risk management policies and graduated confidence depending on the validator.
• Immutable Record Chain: Signature chains link consecutive updates. Each update references and cryptographically verifies its predecessor, supporting tamper-evident, versioned metadata histories:

$$\text{Signature\_chain: } S_1 \rightarrow S_2 \rightarrow \dots \rightarrow S_n$$

This multi-signee architecture eliminates reliance on self-certification and enables external consumers and governance systems to verify agent properties programmatically.

3. Dynamic Permission Management and Temporal Validity

AgentFacts treats agent metadata as inherently dynamic, reflecting the changing states and permissions of deployed AI agents:

• Dynamic Permissions: The metadata integrally encodes time-limited (TTL-based) permissions and operational scopes, such that temporary authorizations or state transitions are recorded cryptographically.
• Time-to-Live (TTL) Enforcement: Each fact or permission is annotated with a TTL value. Upon expiry, the permission or assertion is no longer considered valid unless explicitly refreshed and re-signed. For a field updated at time $t_0$ with a validity window $\Delta t$:

$TTL_\text{value} = t_0 + \Delta t$

• Automated Revocation: If an agent’s permission is suspended or an assertion becomes obsolete, revocation is enacted by omitting or replacing the corresponding signature in the next metadata update.
• Auditability: All changes (grants, revokes, updates) are anchored in the signature chain, facilitating robust regulatory compliance and post hoc investigation.

4. Metadata Schema Structure and Extensibility

AgentFacts defines a canonical metadata schema comprising ten parent fields, explicitly designed for universality and extensibility:

Section	Description	Example Content
Core Identity	Persistent agent identifiers (DID, UUID), timestamps, cryptographic keys	agent_id, last_updated, public_key
Baseline Model	Underlying model information (model ID, release, training summary)	GPT-4-turbo, corpus stats
Classification	Task/vertical domains, capability tags, role labels	“Fact Extraction”, “Customer Support”
Capabilities	APIs, protocols, supported skills with cryptographic attestation	“/v1/generate”, verified web browsing enabled
Authentication & Permissions	Supported auth mechanisms, dynamic permission state, scoping	API keys, OAuth2, TTL: 1 hour, task scope
Compliance & Regulatory	Certification status, regulatory audits, conflict-of-interest attestations	“GDPR-compliant”, SEC-audited
Performance & Reputation	Benchmark metrics, uptime, independent reliability scores	98.3% availability, latency budget: 130ms
Supply Chain	Provenance, model fabrication traces, third-party dependencies	On-prem deploy, open weights hash: 0123…
Verification	Validators’ signature payloads, timestamps, certificates	{sig1: CyberFirm, sig2: Regulator}
Extensibility	Reserved for extension hooks (custom fields, translation, legacy support)	“custom_fact_foo” extension

The schema is technology-agnostic (JSON-LD-based), supporting backward-compatible evolution and translation layers for corporate or regulatory integration.

5. Transformative Impact on Enterprise AI Deployment

In traditional environments, agent onboarding is a fragmented process, requiring bespoke technical integration and manual legal verification. AgentFacts introduces several distinct benefits:

• Standardized Workforce Management: Agents can be indexed, reviewed, and permissioned via uniform processes analogous to employee management, with explicit roles, permissions, and audit trails.
• Governance and Compliance: Cryptographically verified metadata enable automated documentation for compliance audits, internal policy checks, and regulatory reporting.
• Transparency and Discoverability: All functional and non-functional properties (identities, benchmarks, certifications) are searching and filterable; enterprise users can define access policies or trust limits according to validator signatures or confidence levels.
• Efficient Coordination at Scale: Automated validation, revocation, and update protocols allow for rapid, scalable agent fleet management, minimizing latency from deployment to trusted operation.

AgentFacts supports not only technical interoperability but also legal, regulatory, and organizational accountability.

6. Limitations and Implementation Considerations

Although AgentFacts addresses many core challenges, several operational considerations are identified:

• Dependency on High-Quality Validators: The effectiveness and trustworthiness of AgentFacts rests on the integrity and expertise of third-party validators; insufficiently diverse or rigorous validation may degrade assurance levels.
• Dynamic Update Overhead: Systems for key rotation, TTL management, and audit log collation must be robust and performant to prevent governance lag or stale permissions.
• Schema Extension Management: While extensible, care must be taken as organizations add custom facts or integration hooks so as not to fragment the interoperability promise.

Optimal deployment requires integrating AgentFacts-compatible verifiers, scalable metadata stores, and automated update clients tied into enterprise governance platforms.

7. Comparative Perspective

When compared to prior methods—such as propagation in associative networks (0807.0023), template-based systems for spreadsheet metadata (O'Connor et al., 2023), or generic registry models (Singh et al., 5 Aug 2025)—AgentFacts is distinctive in providing:

• Multi-signer, cryptographically verified claim attestation
• Dynamic permissioning for fine-grained, time-scoped trust
• Extensible schema aligning with KYA-driven agent management

This highlights the AgentFacts framework’s central role in establishing a unified, robust, and scalable metadata foundation for the future of cross-domain, enterprise-scale AI agent ecosystems.

References

• AgentFacts: Universal KYA Standard for Verified AI Agent Metadata & Deployment (Grogan, 11 Jun 2025)
• Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts (Raskar et al., 18 Jul 2025)
• A Survey of AI Agent Registry Solutions (Singh et al., 5 Aug 2025)