As AI agents gain more autonomy, securing them against unauthorized actions becomes critical for enterprises. Responsibility for agent actions rests with the enterprise, requiring proactive, not reactive, governance. Intelligent agents demand intelligent rules, which must adapt to real-time context. Traditional guardrails layered above models are insufficient because agent output becomes unpredictable with autonomy. Effective governance must be executable and enforced at the operational data layer, where agents interact with data. This means denying access to sensitive data at the moment of the agent's request and enabling reconstruction of agent actions for auditability. The data layer acts as the enforcement point, leveraging existing controls like role-based access and encryption. Agent identity must be recognized as a first-class principal with a declared purpose bound to its session. This approach allows enterprises to adopt AI agents faster by building trust through robust, source-level enforcement.