Oso for AI Agent Security: Features, Pricing, and Alternatives

Authorization presents a significant challenge for engineering teams, particularly as applications integrate AI agents capable of autonomous actions, necessitating precise control over who can access what under specific conditions. As simple role checks prove inadequate for scaling, companies like Oso offer solutions with centralized policy engines, allowing teams to define access rules using Polar, a declarative language, to ensure actions remain scoped, auditable, and predictable. While WorkOS focuses on identity infrastructure, managing authentication and user data, Oso operates at the policy-expression layer, offering a model that supports RBAC, ReBAC, and ABAC approaches. This separation allows organizations like Intercom and Wayfair to externalize authorization logic, maintaining visibility and consistency across complex permission structures, especially when dealing with AI agents or multi-tenant environments. Oso’s approach involves inline checks during request flows, using its Rust-implemented engine for low-latency decision-making, and provides centralized audit logs for compliance and debugging. However, the effectiveness of such authorization systems hinges on the foundational identity data provided by solutions like WorkOS, emphasizing that authorization and identity infrastructure occupy distinct layers within application architecture.