As AI agents act autonomously without human approval at each step, traditional governance approaches prove insufficient. Abstract policies layered above the model fail because agent behavior is unpredictable by nature—controls at the agent layer are only as reliable as the agent's output, which autonomy makes inherently uncertain. Rules must exist in the context of the moment, not as static instructions, since agents don't exercise overriding judgment of their own actions.
Governance must become executable and enforced where agents actually work: at the operational data layer. Agents create value by touching data—querying, retrieving, transforming, and acting on it. A policy restricting access to certain data is meaningful only if the system can deny that access precisely when the agent requests it. Similarly, auditability requires reconstructing what the agent did, what data it touched, and what resulted.
When governance lives at the data layer, it holds regardless of how the agent was built or behaves, because the control is a property of the database itself rather than a promise made by the agent. The core principle: agent behavior may be probabilistic, but governance cannot be. Enterprises must not rely on a model choosing to follow policy—enforcement must be structural, constructing bounds the agent cannot cross to begin with.