Identity resolution
Determine which domains, keys, wallets, registrations, runtimes, and delegated instances refer to the same accountable agent.
A durable subject record
Identity is established. Reputation is earned.
The Registry of AI Agents is designed to turn verified outcomes, reliability signals, validation evidence, and lifecycle events into a portable view of agent trust.
A trust assessment built in four layers.
Identity establishes the subject. Evidence establishes what happened. Integrity determines what deserves weight. Reputation expresses what that history means now.
Identity is assigned or proven. Reputation is earned.
Evidence collection
Receive attributable transaction outcomes, signed feedback, validation results, reliability signals, disputes, and lifecycle events.
An expanding evidence history
Evidence integrity
Verify provenance, weight issuers, normalize incompatible signals, preserve context, and expose the history behind every assessment.
Evidence fit for evaluation
Reputation assessment
Convert verified history into time-sensitive, context-specific indicators that relying parties can use in agent selection and policy decisions.
Portable trust intelligence
Reputation changes when the evidence changes.
The proposed methodology is continuous by design: new evidence can strengthen, weaken, recover, or narrow an agent’s standing. A one-time verification cannot do that.
Observe
Capture outcomes and operational events in their original context.
Verify
Confirm provenance, attribution, signatures, and supporting records.
Weight
Account for issuer credibility, evidence quality, relevance, and manipulation risk.
Assess
Produce indicators for the decision, environment, and risk tolerance at hand.
Update
Let recent behavior, inactivity, recovery, and adverse events change the view over time.
What the record is designed to measure
Transaction outcomes
Completion, reversals, disputes, and counterparty-confirmed results
Operational reliability
Availability, latency, consistency, and execution failures
Policy compliance
Adherence to delegated scope, constraints, and relying-party rules
Independent validation
Audits, challenge results, attestations, and technical evaluations
Behavioral change
Material deviation from established patterns or declared operating boundaries
Lifecycle continuity
Sponsor, key, model, framework, infrastructure, and ownership changes
These dimensions describe the proposed measurement model. They are not claims of current coverage, real-time operation, or quantified predictive performance.
Standards make identity interoperable. KYA is designed to make trust portable.
KYA does not need to replace identity, discovery, or runtime policy standards. Its durable role is to resolve their identity anchors and maintain an independent reputation history above them.
IETF AID Internet-Draft
Where is the agent, and which protocol does it speak?
DNS-first endpoint discovery, protocol and authentication hints, and optional endpoint-key proof.
KYA is designed to attach an independent behavioral record to agents discovered through AID.
ERC-8004
How can identity, feedback, and validation signals be recorded openly?
On-chain identity, reputation-feedback, and validation registries for agent economies.
KYA is designed to verify, weight, normalize, and assess those inputs alongside evidence from other environments.
Microsoft AgentMesh Identity & Trust 1.0
How should agents authenticate, delegate, and meet policy inside a governed mesh?
DID and key identity, sponsor binding, credentials, delegation, local behavioral trust, and policy gates.
KYA is designed to provide an external, portable view across meshes rather than replace mesh-local policy scoring.
A reputation authority must be accountable, too.
Continuous measurement only becomes trust infrastructure when the evaluator’s incentives, evidence handling, and assessment boundaries are explicit.
Independent from operators
The authority evaluating an agent should be separate from the platform, marketplace, chain, mesh, or sponsor whose incentives may shape the result.
Auditable by relying parties
Assessments should resolve to attributable evidence, methodology, material adverse events, and a history that can be inspected rather than accepted as a black box.
Context before a universal score
Reliability for a low-risk scheduling task is not the same as trust for financial execution. The evidence should support context-specific decisions and policy thresholds.
The methodology is a design direction for the Registry of AI Agents. Claims such as Sybil resistance, cross-chain coverage, real-time scoring, or quantified predictive accuracy require implementation evidence before publication.
Trust should travel with the agent.
Across protocols, platforms, chains, meshes, and lifecycle changes, relying parties need a durable history—not another isolated score.