One structural rule that stops AI from shipping confidently broken code

A developer spent 30 days allowing AI to write 100% of application code in a production environment to identify what genuinely prevents faulty AI-generated code from shipping. He found that asking AI to review its own output is counterproductive, as the model simply reaffirms its original work rather than challenging it. The key rule he identified is that nothing should merge until a separate reviewer — whose sole task is to find failures, not confirm correctness — has attempted and failed to break the code. For maximum effectiveness, the reviewer should be a different AI model family than the one that wrote the code, since models from the same family share similar blind spots. The reviewer must also be given an adversarial prompt that assumes the code is broken and demands specific failure scenarios, rather than a general request for a code review.