1. Why Most AI QA Tools Fail in ProductionThe pattern is now familiar: a team integrates an LLM into their QA workflow, the demo impresses stakeholders, and three months later the tool is quietly deprecated. Tests it generated needed manual cleanup. Root cause analyses were generic enough to apply to any failure. Data provisioning left environments in inconsistent states. The on-call engineer stops trusting it and goes back to doing things by hand.The problem is rarely the model. It is the engineering around the model. Production-grade AI systems require the same rigor as any other software: quality gates, bounded failure modes, auditable outputs, and clear contracts about what the system will and will not do autonomously. Most AI QA integrations skip all of this, ship a thin wrapper around a prompt, and wonder why adoption stalls.