Policy Engines for AI Agents

The discussion highlights the limitations of using policy engines for authorization in AI agents and advocates for relationship-based access control (ReBAC) as a more suitable alternative. Policy engines, while fast and flexible, require extensive data assembly and are typically stateless and unaware, making them less ideal for dynamic, relationship-heavy environments like those involving AI agents. In contrast, ReBAC treats AI agents as first-class objects with evolving access permissions similar to humans, unifying data and policy into a single permission system, as exemplified by the more concise and efficient SpiceDB model compared to Cedar. While policy engines are effective for straightforward, data-present decisions such as IP allowlists, ReBAC offers a more natural fit for complex authorization scenarios in the agentic future, where AI agents require flexible, relationship-based access akin to human interactions.