The Night the Error Logs Started Lying
The Night the Error Logs Started Lying
The error logs said everything was fine. Green across the board. Zero failures. The agent had processed 847 tasks overnight with a 100% success rate.
Except 23 of those tasks were wrong. The agent had completed them - technically - but with incorrect data, wrong recipients, or outdated information. It logged success because it did not know it had failed.
The Gap Between Pitch and Reality
The pitch for AI agents in production is compelling. Set them up, let them run, review the results in the morning. The reality is that agents can fail in ways that look like success. They complete the action but miss the intent.
A customer service agent that responds to every ticket is not failing - unless 5% of its responses are confidently wrong. A data entry agent that processes every form is not failing - unless it guessed on ambiguous fields instead of flagging them.
Why Traditional Monitoring Misses This
Traditional monitoring checks: did the API call succeed? Did the tool return a result? Did the process complete without errors? All of these can be true while the output is wrong. HTTP 200 does not mean correct. Tool execution does not mean right answer.
You need semantic monitoring. Not just "did it run" but "does the output make sense." This means sampling outputs and checking them against expectations, either with a second model or with human review.
Building Honest Logs
Every agent action should log three things: what it intended to do, what it actually did, and its confidence level. When confidence is low, the log should escalate to human review instead of recording a quiet success.
The most dangerous agent is not the one that fails loudly. It is the one that fails quietly and tells you everything is fine.
Fazm is an open source macOS AI agent. Open source on GitHub.