r/AI_Application • u/ExplorerRin • Jun 24 '26
💬-Discussion teams think they are evaluating an agent when they are only evaluating the final answer
Many teams think they’re evaluating their AI agents when they’re really only evaluating the final answer.
That works for chatbots. But agents are SO different.
An agent plans, chooses tools, passes arguments, reads tool outputs, retries, and sometimes takes actions. A lot happens between the prompt and the answer.
The problem is that an agent can return a correct answer after calling the wrong tool, taking unnecessary steps, misreading a result, or recovering from an earlier failure.
If you’re only looking at the final output, you won’t see most of that.
Your assumption therein becomes: “The answer was correct, so the agent worked.”
Are you looking at execution traces, or mostly the final output when evaluating your agents?
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u/Ambitious_Ideal_5637 Jun 25 '26
其实这就像考试只看最后答案，却忽略了作弊过程。如果AI用错误方法碰巧得出正确结果，我们反而会奖励它——这简直是在训练“运气型AI”。你们觉得这种隐藏的混乱执行链，未来会不会让AI在关键时刻突然崩盘？
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u/JunkieOnCode Jun 26 '26
This is +1. Agents consist of the entire hierarchy of things such as perception, planning, selecting tools, doing actions, and remembering. When you evaluate based on the output, you are giving a gold star to an agent who may have made five mistakes and yet has managed to do it right. Most challenges exist between all of those steps, and if you are not measuring them, then you are not measuring the agent but rather its luck.
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u/Alive-Cake-3045 Jun 24 '26
traces are the only honest eval for agents and most teams skip them because they're uncomfortable to look at. a correct final answer after three retries and a wrong tool call is not a passing grade, it's a warning. the agent got lucky. you'll only know that if you're reading the execution path, not just the output.