Agentic development starts as a productivity story, but at scale it quickly becomes a governance problem.
At AI Native DevCon London, we hosted a set of Chatham House roundtables with senior engineering leaders from a range of organizations. I won’t attribute comments to individuals or companies, but the patterns were strikingly consistent: agentic development is moving from an individual tooling conversation into an enterprise operating model question.
The first wave was familiar enough: devs tried GitHub Copilot, Cursor, Claude Code, Codex, Devin and similar tools, and many found obvious value. They wrote code faster, produced tests faster, explored ideas faster, and in some cases revived work that had been sitting in the backlog because it was too costly to attempt.
The interesting question is what happens once agents stop being a personal accelerator and start touching the way an engineering organization works. At that point, the problem shifts from “does the tool help?” to “can we make this safe, repeatable, measurable, and economically sane?”
That shift is why I think the most useful frame is AI agent governance. It means the systems that let teams move faster without losing control, including identity, permissions, context, evals, model routing, cost visibility, policy, ownership, and feedback loops.
