This article proposes a model of software development as nested optimization where AI agents refine code against failures and tests, while developers refine the agent's understanding and intent. It formalizes the relationship between test suites and production failures using concepts of soundness and completeness, showing how repeated refinement drives the gap between tested and actual errors toward zero.