Enterprise Agentic AI: Missing Policy Layer

Enterprise AI systems are missing a crucial layer for policy and compliance. Current agent frameworks are not designed to evaluate every agent action against necessary rules. Developers have many tools for building AI agents, like OpenAI's frameworks and LangChain, which help coordinate tasks and connect agents. This has sped up sophisticated workflow creation significantly. However, the tools for using new AI capabilities are here before the infrastructure to govern them. This gap becomes clear in production environments. Agent frameworks are good at deciding what a system should do, but not where or under what conditions tasks can run. For example, summarizing customer support transcripts in an enterprise setting involves data residency, model approval for regulated data, and audit requirements. These are execution governance problems. Gartner predicts over 40% of agentic AI projects will fail by late 2027 due to inadequate risk controls. A separate orchestration layer is needed between agent logic and execution. This layer would evaluate every agent action against policies for data location, model usage, authorization, and organizational context. This separation allows both the agent framework and the orchestration layer to evolve independently, ensuring AI development also prioritizes critical governance and compliance.