Agents can fail anytime, anywhere.
Track these metrics:
- Task completion rate (did it finish the job?)
- Tool accuracy (did it use the right tools?)
- Response quality (was the output good?)
- Latency (how long did it take?).
But the hard part is emergent errors. The agent might hallucinate data. Use the wrong tool. Make a decision that violates policy. These aren’t bugs in the traditional sense.
Best practices: Log every agent action. Store the full reasoning trace. Have human review for critical tasks. Set up alerts for unusual patterns.
I’ve learned this the hard way: You can’t just deploy an agent and assume it works. Monitor actively, especially in the beginning.
The good news is that agents can improve with feedback. When something goes wrong, you can refine prompts, adjust tool definitions, or add guardrails.