We see how quickly AI coding tools and agent harnesses are improving. But I often wonder whether the surrounding system can keep that autonomy governable once an agent starts planning, executing tools, changing files, and consuming budget on a team’s behalf.

In this talk, I break down a technical case study based on a real AI coding control-plane architecture and show how serious systems structure autonomy through explicit boundaries: plan gates, permission controls, trust review, independent verification, and runtime observability. I will walk through the patterns that make these ideas concrete, explain why telemetry and quota tracing now belong inside the governance conversation, and show why integrity failures can still happen even with strong coding workflows.

This session gives engineering leaders and practitioners a framework for evaluating AI coding tools. The goal is to achieve agent governance that teams can trust, audit, and scale.