For years, AI governance focused on outputs: was the answer biased, explainable, harmful, documented, and risk-checked? Agents break that frame. They call tools, read files, write code, mutate systems, move money, and run multi-step workflows. The consequence is that the blast radius shifts from a misleading answer to changed data, leaked information, shipped code, or a business process triggered before anyone notices. The governing question becomes what this system is allowed to do, under which conditions, with which controls, and who can stop or reverse it. Answering this means pushing Responsible AI into the platform execution layer as running machinery, not documents: verifiable agent identity and revocable delegation, policy-as-code that authorizes every tool call before it runs, machine-readable audit that reconstructs any decision after it, and regulatory obligations compiled into the platform itself. The paradox is that embedding these controls lets teams move and innovate faster because the system already carries responsibility.