Agents

A practical audit trail for AI tools

Evidence beats memory when an agent changes a system.

Agent runs feel conversational, but the useful artifact is the trail of concrete work: files inspected, commands run, endpoints called, IDs returned, and validations performed after the change.

I started keeping the audit trail small. The question is simple: could I understand tomorrow why this change happened and how it was verified?

What belongs in the trail

• The exact target: repo, domain, project, or service.
• The operation type: read, write, deploy, purchase, delete.
• The resulting ID or URL from the system of record.
• The verification that proves the work actually landed.

If the tool cannot produce that evidence, I treat the operation as unfinished. The model's confidence is not a substitute for a real status check.