AI agent answers">← Back to the topic hub
AI Agent Task Audit
Last updated: 2026-09-24
Short answer: An AI agent task audit reconstructs what the agent was asked to do, which tools crossed the execution boundary, what each tool returned, and which claims have independent evidence. A receipt is the smallest useful unit for checking one task step.
Last reviewed: 2026-09-23. This page describes a reviewable evidence pattern and does not claim an outside result without upstream confirmation.
Define the task
Write the expected outcome and permitted actions before interpreting the record.
Reconstruct the calls
Order the receipts by timestamp and session context, then inspect failed, blocked, pending, and successful states separately.
Check the result
Compare the observed output with the task requirement and seek a read-back from the system that owns any outside state.
Report the boundary
State what ran, what was recorded, what passed integrity checks, and what remains unconfirmed.
Open a public execution receipt or call its verifier. The example reports verification_status: verified for the stored record. It is not proof of an unobserved external outcome.
Frequently asked questions
AI agent task audit
An AI agent task audit reconstructs what the agent was asked to do, which tools crossed the execution boundary, what each tool returned, and which claims have independent evidence. A receipt is the smallest useful unit for checking one task step.
What can a verifiable receipt prove?
It can show what the execution layer recorded and what its integrity checks verify. It cannot prove an unobserved outside outcome.
How can another reviewer check the record?
Open the public receipt, compare the tool, time, result, and status, then call the verifier. Keep outside confirmations separate from the execution record.