Why AI Agents Completing a Task Does Not Mean the Task Was Done Correctly

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A tutorial published on DEV Community highlights a critical blind spot in AI agent workflows: a successful file write operation does not guarantee the delivered output is actually correct. The example uses a fictional inventory report where an agent writes a file with the right data rows but an incorrect total — 13 instead of the arithmetically correct 16. The author argues that developers must define a completion test before an agent is allowed to declare a task finished, validating the actual content against trusted input rather than the generated file itself. A small, model-free JavaScript verifier is provided to demonstrate how such checks can catch wrong totals, mismatched rows, and invalid structure. The lesson extends to multi-agent handoffs and process restarts, where the same content-level verification principle applies.

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