How to Evaluate an AI Agent Beyond Accuracy
A practical note on why production agents need evaluation beyond simple answer accuracy.
Most people evaluate an AI system by asking one question: is the answer correct?
For a production AI agent, this is not enough.
A customer-service agent can produce a fluent answer but still fail in several ways:
- It may misunderstand the user’s intent.
- It may call the wrong tool.
- It may retrieve irrelevant knowledge.
- It may answer confidently when the correct action is to ask a follow-up question.
- It may drift in a multi-turn conversation.
A Better Evaluation Frame
For agent systems, I prefer to split evaluation into several layers:
- Intent recognition: Did the agent understand what the user actually wanted?
- Knowledge grounding: Did it use the right knowledge source?
- Tool behavior: Did it call the correct tool with correct parameters?
- Conversation control: Did it know when to answer, ask, refuse, or escalate?
- User-facing quality: Was the final response clear, safe, and useful?
Why This Matters
Accuracy is only the final surface result. Agent reliability comes from the whole decision chain.
If the intent is wrong at the beginning, the final answer can look polished but still be useless.
My Takeaway
A good agent evaluation set should not only contain final answers. It should also record the expected intermediate behavior: intent, retrieved evidence, tool calls, and failure type.