Your AI agent just took an action. Do you know what it did?

A few months ago, a fintech company's accounts payable agent approved and triggered a $47,000 payment to a vendor that had been flagged for fraud two weeks earlier. The flag was in the system. The agent never saw it. By the time anyone noticed, the money was gone. The company had logs. Technically. They had server logs, database logs, error logs. What they didn't have was a clear record of what the agent saw, what it decided, and why it sent that payment. When their auditors asked, the engineering team spent three days piecing together a timeline from scattered log files that were never designed to answer that question. This is not an edge case. This is what happens when you put AI agents into production without thinking about accountability. Agents are different from software Traditional software is deterministic. If a bug causes a wrong transaction, you look at the code, find the bug, fix it. The behavior is reproducible and the cause is traceable. AI agents don't work like that. The