Open Source or Custom in Enterprise AI Systems?

This article argues that as AI makes code generation cheaper, engineering judgment becomes more valuable, and that velocity metrics like PR volume or lines changed are weak signals of progress. It explains where quality breaks in AI-assisted systems—at integration boundaries, retries, observability, and governance—and recommends building on proven open-source components, applying Twelve-Factor and annotation discipline, then adding AI-assisted implementation with strict review, testing, and monitoring. It cites an advisory project for a U.S. industrial manufacturer where an internally built agentic system was rebuilt on the OpenAI Agents SDK for greater robustness and maintainability.