AI Agent Audit Log Best Practices

Last updated: 2026-09-24

Short answer: Good AI agent audit logs preserve enough context to reconstruct tool calls without exposing secrets. Use stable identities, timestamps, status transitions, canonical output, integrity material, provenance, retention rules, and a clear distinction between execution and outside confirmation.

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

Immutability

Preserve the original record and make later changes detectable through canonical bytes and hashes.

Per-call fields

Log the tool, scoped inputs, observed output, time, status, caller scope, and relevant schema version.

Retention

Set a documented retention period and privacy policy that matches the review need.

Receipt versus log

Logs provide operational history. Receipts provide focused, portable handoffs for selected executions.

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

AI agent audit log best practices

Good AI agent audit logs preserve enough context to reconstruct tool calls without exposing secrets. Use stable identities, timestamps, status transitions, canonical output, integrity material, provenance, retention rules, and a clear distinction between execution and outside confirmation.

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.