An agent audit trail is a chronological record of an AI agent's identity, instructions, tool requests, approvals, outputs, and resulting changes. It lets operators reconstruct who authorized an action, what evidence the agent used, and whether the final state matches the recorded decision.
The trail links agent identity to each tool call, approval, and state change. An agent trajectory explains the reasoning path, while LLM observability exposes runtime signals. An audit trail records effects, not only model messages.
When does agent audit trail matter?
Keep records long enough for operational and legal needs, with access controls and data minimization. AI compliance may require evidence that policies were followed. A log that omits the external result cannot prove what the agent changed.
Frequently asked questions
What is agent audit trail used for?
Keep records long enough for operational and legal needs, with access controls and data minimization. AI compliance may require evidence that policies were followed.
Is an audit trail the same as an agent trace?
No. A trace helps debug execution; an audit trail also proves authorization, identity, and resulting changes.
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