How to Audit Your AI Agent After the Fact

Last updated: 2026-09-24

Short answer: To audit an AI agent after the fact, collect the task context, order the execution receipts, inspect each observed result, run the integrity checks, and compare claimed outside effects with upstream evidence. Preserve failures and missing evidence as part of the audit.

Last reviewed: 2026-09-23. This page describes a reviewable evidence pattern and does not claim an outside result without upstream confirmation.

Collect

Gather receipt URLs, session identifiers, timestamps, tool names, and the original task boundary.

Order

Build a timeline that distinguishes proposed, executed, failed, blocked, pending, and confirmed states.

Verify

Call each verifier and record the integrity result without changing the original record.

Conclude

Write separate findings for execution, recorded output, integrity, and external state.

Live verification example

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

how to audit AI agent after the fact

To audit an AI agent after the fact, collect the task context, order the execution receipts, inspect each observed result, run the integrity checks, and compare claimed outside effects with upstream evidence. Preserve failures and missing evidence as part of the audit.

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.