19 / Technical insight
AI agent verification: evidence, receipts and audit trails
AI agent verification asks a narrower and more useful question than whether an agent sounds confident: what action or service outcome is being claimed, which evidence supports it, which rule evaluated it and can another system reproduce the decision?
01 / Direct answer
What is AI agent verification?
AI agent verification is the process of testing a concrete claim about an agent's action or service outcome against explicit evidence and a known evaluation policy. A useful verification record identifies the claim, the evidence supplied, the rule version, the result, the time of evaluation and an integrity commitment.
This differs from accepting a chat response, screenshot or mutable application log as proof. Those artefacts may help operators investigate an event, but they do not by themselves make the decision portable or independently re-checkable.
02 / Verification chain
Five layers turn a claim into reviewable evidence.
A defensible workflow starts with a bounded claim, such as an API responding within a stated threshold or an output matching a committed digest. It then records the acceptance terms before evaluation, preserves the observed measurements, applies deterministic checks and emits a receipt that can be verified again.
Each layer should remain visible. Hiding policy logic behind a confidence score makes it difficult to distinguish a failed measurement, an unevaluated rule and a model judgement.
- Claim: the exact action or service outcome being asserted.
- Terms: measurable thresholds and the versioned policy in force.
- Evidence: observations, timestamps and optional content commitments.
- Decision: explicit pass, fail or unevaluated results for every rule.
- Receipt: a portable record with an integrity digest another system can recompute.
03 / Logs versus receipts
Observability explains a system; verification challenges a claim.
Logs are essential for debugging, monitoring and incident response. Their weakness is not that they are useless, but that a verifier may need to trust the same system that created and stores them. A receipt narrows the evidence into a stable, canonical record and exposes the evaluation contract.
Strong implementations bind receipts to external measurements, independently controlled signing keys or third-party attestations. First-party receipts remain useful engineering evidence, but should not be described as independent verification without that separation.
04 / Common failure modes
Verification fails when evidence boundaries are vague.
A cryptographic digest can detect modification of a receipt, but it cannot make a false input true. Likewise, a deterministic rule can reproduce a decision while still applying the wrong policy. Verification systems need explicit provenance, clock assumptions, missing-evidence handling and versioned canonicalisation.
- Self-attestation presented as independent evidence.
- A pass/fail label without the evaluated inputs and rule version.
- Mutable logs without a stable commitment or replay path.
- Missing, stale or unauthorised evidence silently treated as a pass.
- Cryptographic integrity described as proof of real-world truth.
05 / Working example
Test a deterministic evidence receipt in the browser.
PactVerity's public Proof Engine accepts explicit latency, uptime, freshness and optional output-integrity criteria, evaluates supplied observations and exports a portable receipt. The same receipt can be imported and checked again against the documented engine behaviour.
The current MVP does not custody funds or settle payments. Its purpose is to make the evidence and decision boundary inspectable before broader protocol functions are claimed.
Continue researching