False Completion Is the Real Failure Mode of Coding Agents

A deliberately strict process for preventing false completion in autonomous software development In an earlier article, I argued that the model should have to earn every agent in an agentic system. One agent is a valid answer. Every additional boundary has to justify its cost. This article starts one level lower. Assume we have selected the topology. Who gets to decide that the resulting product is actually the product we asked for? After experiencing dozens of AI-development efforts and reviewing the results of many more, I no longer think the main danger is that the development procedure will manifestly fail. A crashed build, a red test or an explicit error report is relatively easy to deal with. The system has admitted that it has a problem. The far more serious failure is that of FALSE COMPLETION: the AI system believes it has accomplished the development goal, reports success with complete confidence, and delivers a product that has little to do with what the system design