IDKMesh

AI provenance, evidence, and reproducibility

AI provenance is the chain that connects a task, worker identity, exact artifacts, evaluation, and authority decision. Without that chain, an agent can produce a plausible result while later reviewers cannot establish what actually ran or which artifact was verified.

What a useful provenance record contains

For software-agent work, retain enough information to answer:

• What task and constraints were given?
• Which worker or model executed it?
• Which exact revision or artifacts did it produce?
• Which evaluator inspected those artifacts?
• Which plan and checks did the evaluator use?
• What evidence was observed?
• Who or what integrated the result?
• Can another reviewer reproduce the relevant checks?

IDKMesh separates these concerns across WorkUnit, ResultManifest, EvaluatorPlan, and VerificationResult contracts.

Evidence classes matter

The repository deliberately distinguishes:

1. implemented mechanism — the code or schema exists;
2. synthetic validation — fixtures or simulations exercise the mechanism;
3. observed evidence — controlled runs measured real behavior;
4. accepted conclusion — evidence is strong enough for the scoped decision.

A reproducible simulator can show that an algorithm behaves as implemented without proving that it improves real agent collaboration. Keeping those classes explicit makes AI evidence more useful to both humans and automated systems.

Provenance should bind to the exact candidate

A review result becomes ambiguous if the candidate changes after evaluation. Good provenance binds evidence to immutable hashes, revisions, or artifact digests so a later reader can tell exactly what was checked.

Common questions

What is AI provenance?

It is the traceable record of origin, transformations, identities, artifacts, and evidence behind an AI-produced output or decision.

Why is model output provenance important?

Because a text claim that “tests passed” is weaker than an inspectable record showing which tests ran on which exact artifact under which environment.

What makes an AI workflow reproducible?

Pinned inputs and artifacts, deterministic checks where possible, recorded versions and identities, stable schemas, and commands or procedures another reviewer can rerun.

Is an audit log enough?

Not if it only records events. Stronger evidence also binds those events to the exact data, candidate, and evaluation that support a conclusion.

Where are IDKMesh provenance contracts?

Start with the schema index, architecture, and specifications.

What metadata should an AI provenance record contain?

Useful fields include task identity, worker/model identity, timestamps, input references, artifact hashes or commit SHAs, tool/environment versions, evaluator identity, checks performed, outcomes, and the integration decision.

How can I prove which model generated an artifact?

Record model/provider identity together with observable runtime or artifact evidence when available, and bind that identity to the exact ResultManifest or output digest rather than relying only on a host-side label.

How do you bind an evaluation to an exact Git commit?

Store the immutable commit SHA or artifact digest in the evaluation record and require re-verification when the candidate revision changes.

What is the difference between logs and provenance?

Logs record events. Provenance connects identities, inputs, outputs, transformations, and evidence into a traceable relationship that can support later verification.

How do you preserve AI evidence without leaking secrets?

Store references, hashes, redacted metadata, and reproducible commands while excluding raw credentials and sensitive payloads. Secret values should remain in dedicated secret stores, not evidence documents.

Browse all AI-agent trust topics.

Last reviewed: 2026-09-22.

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