From AI Hype to Controlled Enterprise AI-Assisted Development

Most AI coding demos answer the easiest question: can a model generate something that runs? This experiment asks a harder one: can a multi-agent workflow produce code that stays aligned with requirements, respects project constraints, and remains reviewable enough for a long-lived enterprise codebase ? This article covers the first iteration of that experiment: a four-role orchestration setup, the artifacts around it, and the lessons that came from requirement drift, weak review gates, and over-optimistic test confidence. The AI wave of the last few years did not pass my company by. Presentations, workshops, ambitious plans to improve productivity by double-digit percentages before the end of the year through AI adoption — all of that quickly became part of everyday engineering life. We recently got access to GitHub Copilot, and for me that was the final signal: there is no going back. These tools are here, and I need to adapt to this new reality seriously. I work in a large financial