A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents

The article presents a new reference architecture for the runtime governance of production AI agents, addressing the limitations of existing policy engines in managing risks associated with agent-driven workflows. It introduces a five-plane decomposition consisting of a reasoning plane for intent adjudication and four enforcement planes (network, identity, endpoint, data) to implement decisions, along with mechanisms for stateful evaluation of composite principals. This framework is crucial for practitioners as it provides a structured approach to managing delegated actions in AI systems, ensuring compliance and security in complex workflows, with performance metrics indicating microsecond adjudication times and robust audit capabilities.