Why can't I just use my existing ML governance framework for agents?

Answer

Traditional ML governance was designed for batch models that produce predictions — you validate the model, deploy it, monitor drift, and retrain. Agents break this model because they take actions in real time, chain tools together unpredictably, and operate autonomously. You need runtime interception (not just monitoring), action-level policy enforcement, multi-agent coordination controls, and the ability to halt an agent mid-execution. Your existing framework covers the model inside the agent, but not the agent itself.