u/ExplorerRin

One thing I’ve noticed is that many teams think they’re evaluating an agent when they’re really evaluating the final answer.

That works for a chatbot. An agent does more than generate a response. It plans, chooses tools, passes arguments, reads outputs, retries, stops, and sometimes takes actions.

The problem is that an agent can still return the right answer after calling the wrong tool, taking extra steps, misreading a result, or ignoring a failed call.

From the outside, the answer looks fine.

But the question isn’t just whether the answer was right. It’s also whether the path to get there made sense.

The main trap: “The answer was correct, so the agent worked.”

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?

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 clearly 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 becomes: “The answer was correct, so the agent worked.”

But if an agent is going to run real workflows, the answer isn’t the only thing that matters. You also need to know whether the path it took was valid, efficient, grounded, and safe.

How are people here evaluating agents today? Are you looking at execution traces, or mostly the final output?

The biggest illusion is that teams think they are evaluating an agent when they are only evaluating the final answer.

That works well for a chatbot. But an agent is different.
It plans, chooses tools, passes arguments, reads observations, retries, stops, and sometimes takes action. A final-answer score hides most of the actual failure surface.

An agent can produce a good-looking answer after calling the wrong tool, wasting ten steps, misreading a tool result, or ignoring a failed call. From the outside, the answer may look acceptable. From a reliability perspective, the run is not acceptable.