Treating AI Code Hallucinations as an Architecture Problem

AI-generated code can introduce risks that better prompting alone may not resolve, particularly when models lack persistent access to system rules, dependencies, and constraints. The article proposes a specification-driven architecture where machine-readable requirements become the source of truth, generated outputs are validated against typed contracts and tests, and human approval remains part of the release path. It also emphasizes traceability from requirement through generation and sign-off. Technology leaders should govern AI coding through architectural controls that prevent unsupported output from reaching production.