The production failures now being documented by engineering teams reveal a specific structural error: AI tools were integrated into shipping workflows before teams developed the practices to validate what those tools produced. The teams that moved fastest — deploying tools broadly, encouraging experimentation, measuring success in merged pull requests — absorbed the consequences in their incident logs. What teams that tripled velocity discovered is that AI-generated code requires an architect, not just a reviewer. That distinction — between catching bugs and ensuring a component fits a coherent system — is…