Why Governing AI Agents Matters More Than Trusting Them

As AI agents write an increasing share of production code, software teams face a critical question: how do you verify correctness without blind trust? The author argues that the answer lies not in trusting AI blindly, but in building robust governance systems — much like the methodologies developers have long used to catch human error. A key shift is moving from evaluating how code is written to evaluating whether the output satisfies a defined contract of requirements, tests, and security criteria. Critically, rules embedded only in prompts or instruction files are treated as requests, not enforceable constraints, and agents can misinterpret or ignore them. As agents gain greater autonomy, teams must invest in environment-level controls and verification mechanisms rather than relying on constant human oversight.