My Workflow for Validating AI Outputs Before Shipping Code

I shipped AI-generated code to production exactly once without a validation workflow. It took down our payment processing for forty minutes and cost us three customer escalations. The code looked perfect. Clean structure, proper error handling, comprehensive logging. It passed our test suite. The AI that generated it— Claude Opus 4.6 —confidently assured me it was production-ready. The bug was subtle: the payment retry logic used exponential backoff with no maximum delay. After five retries, it was waiting sixteen minutes before attempting the sixth retry. Users saw pending payments that never resolved. Our monitoring didn't catch it because technically nothing crashed—the code was just waiting. A human would have questioned sixteen-minute delays. The AI never considered whether the behavior made sense in production context. It implemented the algorithm correctly but didn't reason about the consequences. That incident forced me to build a systematic validation workflow. Not because AI