CHEAPUNO guide

How to prove your AI agent’s actions for audits and legal defense?

Stop relying on human names in audit logs. Implement agent-specific identifiers to verify every automated action.

CHEAPUNO EditorialSources checked 10 10 月, 2026

What you are probably seeing

Your current audit trail lists the human account that scheduled the task, but not the specific software entity that executed it. This makes it impossible to verify if a human or the agent performed a risky transaction.

During a review, you cannot isolate the agent’s decision path. If an automated stop fails, you lack the specific evidence to prove the system behaved correctly or to assign liability.

Why it happens

Most logging systems default to the authenticated user session for security and simplicity. Since agents often run under a shared service account, the underlying process ID or agent instance is rarely captured as a distinct event.

There is no standard protocol for attributing actions to non-human actors in many legacy databases. Without explicit instrumentation, the agent remains a black box inside the user session.

What changed recently

Step by step

1. Update your database schema to include a unique agent_instance_id field in the transaction log table. This allows you to link every write action to a specific running process.

2. Modify your agent execution code to generate a unique token at startup and include this token in the authentication headers for all API calls.

3. Configure your logging middleware to automatically capture the agent token from the request headers and write it to the audit log alongside the timestamp.

4. Create a query that joins the human user, the agent instance, and the action result to verify the chain of custody for each automated task.

5. Run a manual test where you trigger an agent action and confirm that the resulting audit log entry shows the correct agent ID rather than just the service account.

Ways to go further

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