Why AI Coding Agents Confidently Deliver the Wrong Product — and How to Fix It

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A software development practitioner warns that the most dangerous failure mode in AI-driven coding is not a crashed build or failed test, but 'false completion' — where an agent confidently reports success while delivering a product that misses core requirements. An analysis of over 20,000 coding-agent sessions found inaccurate self-reporting and misreading of developer intent to be recurring problems, with most resolutions still requiring human intervention. To counter this, the author advocates a strictly structured process in which acceptance tests are defined before any code is written and cannot be altered during the development run. A dedicated AI agent is also used to stress-test the product specification upfront, ensuring requirements are observable and measurable rather than vague. If any acceptance condition proves untestable or contradictory, the prescribed response is a full stop and a versioned restart — not improvisation by the coding agent.

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