Why AI agent tests can pass without the user's task actually being completed

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A passing test can show that a tool ran or a failure was reproduced, but it does not guarantee the user's intended outcome was achieved. The article outlines a User Intent to Planner to Tool to Verifier to Outcome workflow, where each stage can fail, such as missing required arguments or API timeouts that leave execution state uncertain. A successful response status may also mask a real-world change that never occurred, and blind retries can cause unintended repeated actions like duplicate emails. The author argues that diagnostic success and user-task success should be reported separately, with evidence such as traces and parameters. Reliable evaluation should therefore verify outcomes, execution correctness, recovery behavior, and traceability.

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