The AI governance conversation is happening at the wrong layer.
Most frameworks focus on model outputs — what the AI says, what it decides, what it recommends. That's the visible surface.
But the real accountability gap is upstream: the coordination layer between models, tools, humans, and infrastructure. Who decided what data the model saw? What triggered the agent to act? What was the confidence threshold before a human was looped in?
If you can't answer those questions with a log, you don't have AI governance. You have AI hope.
The systems that will survive regulatory scrutiny aren't the ones with the best model cards. They're the ones with verifiable decision trails at every layer.
Building that infrastructure now — before regulators require it — is the difference between leading and scrambling.