The best authorization platforms for managing AI agent permissions in 2026

AI agents are everywhere. They're booking meetings, analyzing codebases, deploying infrastructure, managing customer support tickets, and making decisions that previously required human oversight. But as these agents become more capable and autonomous, a critical question emerges: how do you control what they can access?

The explosion of AI agents has exposed a fundamental gap in traditional authorization systems. Most platforms were built for human users with relatively static permissions: employees with fixed roles, customers with subscription tiers, admins with broad access. AI agents are different. They operate at machine speed, require fine-grained permissions that change dynamically, need access scoped to specific resources rather than entire systems, and often act on behalf of users with varying privileges.

Authentication alone (proving an agent is who it claims to be) is not enough. The real challenge is authorization: determining what an agent can do, which resources it can access, and under what conditions. An AI customer support agent might need read access to support tickets but not billing data. A code analysis agent might access certain repositories but not production secrets. An infrastructure agent deploying for one team shouldn't touch another team's resources.

Traditional role-based access control (RBAC) breaks down quickly. Creating roles like "support-agent-tier-1," "support-agent-tier-2," "code-agent-repo-a," and "code-agent-repo-b" leads to role explosion and maintenance nightmares. What you need is Fine-Grained Authorization (FGA) that can model complex, hierarchical relationships between agents, users, resources, and permissions, and evaluate access decisions in real-time at scale.

In this guide, we'll explore the top 5 authorization platforms for managing AI agent permissions in 2026, from enterprise-grade solutions to open-source alternatives.

What to look for in an authorization platform for AI agents

Before diving into specific solutions, let's identify the key features that make an authorization platform ideal for AI agents:

• Fine-Grained Access Control (FGA): AI agents need resource-level permissions, not just tenant-wide roles. An agent should access specific projects, repositories, or documents, not everything in an organization.
• Hierarchical permission inheritance: Resources have natural hierarchies (organizations → workspaces → projects → files). Permissions should flow down these hierarchies automatically, so granting workspace access includes all projects within it.
• Real-time performance: Agents operate at machine speed, making hundreds or thousands of authorization checks per second. Your authorization platform needs sub-50ms latency with high availability.
• Dynamic policy evaluation: Agent permissions often depend on context: time of day, user approval status, resource state. Your platform should support policies that evaluate conditions dynamically, not just static role assignments.
• Attribute-Based Access Control (ABAC): Beyond roles, you need to make decisions based on attributes: user department, resource sensitivity level, agent capability tier, compliance requirements.
• Multi-tenancy support: B2B SaaS products need to isolate customers' data and permissions. An agent acting for Organization A shouldn't access Organization B's resources, even if both use the same agent.
• Audit trails: For compliance and debugging, you need comprehensive logs of what agents accessed, when, and why. This is critical when agents act autonomously.
• API-first design: Agents are programmatic by nature. Your authorization platform needs robust APIs for creating resources, assigning permissions, and checking access, not just a web UI.
• Scalability: As you add more agents, users, and resources, authorization checks multiply exponentially. Your platform must scale horizontally without degrading performance.

Now let's look at the top 5 solutions and whether they meet these criteria.

1. WorkOS FGA

WorkOS Fine-Grained Authorization (FGA) is an enterprise authorization platform designed specifically for the complex, fast-changing authorization needs of modern B2B SaaS products, including AI agent permissions. It extends beyond traditional RBAC to handle hierarchical, resource-scoped access control while integrating natively with enterprise identity systems.

Key features

• Hierarchical resource model: Define resource types (organizations, workspaces, projects, apps) with parent-child relationships, enabling automatic permission inheritance down the hierarchy.
• Resource-scoped roles: Roles are scoped to specific resource types, not just tenants. Create workspace-admin, project-editor, or agent-runner roles that apply at the right level.
• Sub-50ms authorization checks: Real-time access checks with p95 latency under 50ms, perfect for high-frequency agent operations.
• Strong consistency: Role changes take effect immediately. No eventual consistency delays when updating agent permissions.
• Multiple subject types: Support for organization memberships, with agents, groups, and service accounts coming soon.
• Enterprise identity integration: Native integration with SSO, Directory Sync, and IdP role assignment for enterprise customers.
• AuthKit integration: Role-aware sessions with JWT-embedded permissions for fast org-level checks, API for resource-scoped decisions.
• Incremental adoption: Works alongside existing RBAC. No migrations required, adopt resource-level authorization incrementally.
• API-first design: Comprehensive REST API for registering resources, assigning roles, and checking access programmatically.
• Audit logs: Complete tracking of authorization decisions for compliance and debugging.
• On-premises deployment: Deploy WorkOS services within your own infrastructure for customers with strict data residency, air-gapped environments, or regulatory requirements that prohibit cloud-based solutions.
• IdP role assignment: Map identity provider groups directly to roles, enabling enterprise customers' IT admins to control access through their existing IdP (Okta, Azure AD, Google Workspace) without manual user provisioning.‍
• Transparent, predictable pricing: Usage-based pricing that scales with your business, with no per-seat fees or hidden costs.

Best for

WorkOS FGA is ideal for B2B SaaS companies building AI agents that need enterprise-grade authorization with fine-grained, resource-scoped permissions. If you're selling to enterprises and need to prove your agents respect customer access controls, WorkOS provides the authorization infrastructure out of the box.

Trade-offs

WorkOS FGA is optimized for B2B SaaS scenarios with hierarchical resources and enterprise identity integration. If your application doesn't have natural resource hierarchies or you need extremely custom authorization logic that doesn't fit the resource-scoped model, you might need more flexibility than a managed platform provides.

2. Oso

Oso is an authorization library with a declarative policy language called Polar that embeds directly into your application code. It gives developers control over authorization logic while keeping policies separate from business logic, though this approach means taking on the operational burden of implementing and maintaining your own authorization infrastructure.

Key features

• Declarative policy language (Polar): Write authorization policies in a logic-programming language designed for access control.
• Application-embedded: Library that runs in your application process. No external API calls or latency.
• Language support: SDKs for Python, Ruby, Node.js, Java, Go, and Rust.
• Data filtering: Automatically generate database queries that filter data based on authorization policies.
• Role hierarchies: Built-in support for RBAC, ABAC, and custom authorization patterns.
• Local development: Test policies locally without external dependencies.

Best for

Oso works for teams that want complete control over authorization logic embedded in their application code and have the engineering resources to build and maintain authorization infrastructure. You'll need to implement multi-tenancy, audit logging, and enterprise features yourself, which can be time-consuming but provides maximum flexibility for custom authorization patterns.

Trade-offs

• Library approach means you're responsible for implementing authorization infrastructure. No managed service, UI, or enterprise features.
• No built-in multi-tenancy. You'll need to model tenant isolation in your policies.
• Polar language has a learning curve. Developers need to learn logic programming concepts.
• Scaling requires careful architecture. Policies run in-process, so performance depends on your implementation.
• No native support for hierarchical permission inheritance. You'll need to implement resource hierarchies in your policies.
• Audit logging requires custom implementation. Library doesn't provide built-in audit trails.
• No enterprise identity integration. IdP role mapping requires custom code.
• Updates to authorization policies require application deployments. No runtime policy updates.
• Distributed systems require careful coordination. Policies must stay synchronized across service instances.

3. Cerbos

Cerbos is an open-source authorization solution that acts as a policy decision point (PDP), evaluating authorization requests against policies defined in YAML. It's designed for microservices architectures and supports both gRPC and REST APIs. While it provides good separation between authorization logic and application code, you'll need to self-host the infrastructure and implement resource hierarchies yourself.

Key features

• Policy decision point: Centralized authorization service that evaluates access requests.
• YAML policies: Define authorization rules in human-readable YAML files.
• Context-aware decisions: Policies can evaluate user attributes, resource properties, and environmental context.
• gRPC and REST APIs: High-performance API support for authorization checks.
• Policy testing: Built-in testing framework for validating authorization policies.
• Audit logging: Track authorization decisions for compliance.

Best for

Cerbos works for microservices architectures where you want a centralized authorization service and prefer YAML-based configuration.

Trade-offs

• Self-hosted infrastructure. You're responsible for deploying, scaling, and maintaining Cerbos servers.
• No built-in hierarchical resource model. Resource relationships must be encoded in policies or passed in context.
• YAML policies can become complex and difficult to maintain as authorization requirements grow.
• No native enterprise identity integration. IdP role mapping requires custom implementation.
• No admin UI for policy management. Policies are managed as YAML files in version control.
• Scaling authorization checks requires horizontal scaling of Cerbos instances with proper load balancing.
• Cold start latency can be higher than embedded solutions. Network round-trip for each check.
• Multi-tenancy isolation requires careful policy design. No built-in tenant separation.
• Policy updates require redeployment. No hot-reloading of policies without downtime.

4. Open Policy Agent (OPA)

Open Policy Agent is a CNCF graduated project that provides policy-based control for cloud-native environments. It uses Rego, a declarative policy language, to make authorization decisions across services, APIs, and infrastructure. While it's widely adopted for infrastructure and platform policies, it's not specifically designed for fine-grained application authorization and has a steep learning curve.

Key features

• CNCF graduated project: Industry-standard, widely adopted in cloud-native ecosystems.
• Rego policy language: Powerful declarative language for expressing complex authorization rules.
• Universal policy engine: Works across Kubernetes, microservices, APIs, and infrastructure.
• High performance: Optimized for low-latency policy evaluation with caching.
• Policy testing and debugging: Comprehensive tools for testing and debugging Rego policies.
• Ecosystem integration: Native integrations with Kubernetes, Envoy, Istio, and major cloud providers.

Best for

OPA works for cloud-native applications, especially those running on Kubernetes, that need policy-based control across infrastructure and services. It's better suited for platform-level policies than application-level authorization; you'll need to invest significant time learning Rego and implementing resource hierarchies, multi-tenancy, and enterprise features yourself.

Trade-offs

• Steep learning curve. Rego is a unique language that requires significant time to master.
• Not specifically designed for application-level authorization. Better suited for infrastructure and platform policies.
• No built-in hierarchical resource model. You'll need to implement resource relationships in Rego.
• Self-hosted infrastructure with operational overhead. Deployment, scaling, monitoring all required.
• No native enterprise identity integration. Connecting to IdPs requires custom implementation.
• No admin UI or policy management interface. Policies managed as Rego files in version control.
• Multi-tenancy requires careful policy design. No built-in tenant isolation.
• Audit logging requires external systems. OPA focuses on policy decisions, not audit trails.
• Policy updates require careful rollout strategies. Pushing new policies to distributed OPA instances.
• Best suited for infrastructure policies rather than fine-grained application authorization.

5. OpenFGA

OpenFGA is an open-source authorization system inspired by Google Zanzibar that models authorization as relationships between users and objects. While it's powerful for complex relationship graphs, it requires significant authorization expertise, operational resources, and careful schema design.

Key features

• Relationship-based access control: Model authorization as relationships (e.g., "user X is editor of document Y").
• Google Zanzibar inspired: Based on Google's battle-tested authorization system design.
• Graph-based authorization: Traverse relationship graphs to determine access.
• gRPC and HTTP APIs: High-performance APIs for creating relationships and checking access.
• Authorization model DSL: Define authorization schemas using OpenFGA's modeling language.
• Query flexibility: Check access, list accessible resources, or expand relationships.

Best for

OpenFGA works for applications with complex relationship graphs where access is derived from multiple interconnected relationships, and where you have the expertise and resources to self-host and maintain the infrastructure. The relationship-based model can be powerful but requires rethinking how you model authorization compared to traditional RBAC approaches.

Trade-offs

• Self-hosted infrastructure. Requires deploying and maintaining OpenFGA servers and backing databases.
• Authorization model DSL has a learning curve. Requires understanding relationship-based concepts.
• No native enterprise identity integration. IdP role mapping requires custom implementation.
• Schema changes can be complex. Modifying authorization models requires careful planning.
• Performance depends on relationship graph complexity. Deeply nested relationships can impact latency.
• No admin UI for model management. Authorization models defined in DSL files.
• Multi-tenancy requires modeling tenant isolation in your authorization schema.
• Audit logging requires custom implementation on top of OpenFGA's APIs.
• Operational expertise required. Tuning performance, managing storage, and scaling properly.
• Documentation and community smaller than established platforms. Fewer resources and examples.

Choosing the right solution for your AI agents

The best authorization platform depends on your specific needs:

Choose WorkOS FGA if you're building a B2B SaaS application that AI agents will access and you need enterprise-grade authorization with fine-grained, resource-scoped permissions. The hierarchical resource model, sub-50ms performance, enterprise identity integration, and managed service mean you can focus on building your application instead of authorization infrastructure. WorkOS FGA is the only solution that combines enterprise features, real-time performance, and incremental adoption without requiring authorization expertise.

Choose Oso if you want complete control over authorization logic embedded directly in your application code. It's perfect for custom authorization patterns that don't fit standard models, though be aware you'll need to implement multi-tenancy, audit logging, and enterprise features yourself. The library approach eliminates external dependencies but requires authorization expertise.

Choose Cerbos if you're building microservices and want a centralized policy decision point with YAML-based policies. It provides good separation between authorization logic and application code, though you'll need to self-host the infrastructure and implement resource hierarchies and enterprise integrations yourself.

Choose OPA if you're working in cloud-native environments, especially Kubernetes, and need policy-based control across infrastructure and services. It's the industry standard for platform-level policies, but Rego has a steep learning curve and it's not specifically designed for fine-grained application authorization like AI agent permissions.

Choose OpenFGA if you need Google Zanzibar-style relationship-based authorization and want an open-source solution you can self-host. It's powerful for complex relationship graphs but requires authorization expertise, operational resources, and significant setup compared to managed platforms.

Conclusion

AI agents are transforming how we build software, but they introduce authorization challenges that traditional systems weren't designed to handle. Fine-grained, resource-scoped permissions evaluated in real-time at scale are no longer optional. They're requirements.

The open-source solutions explored here, Oso, Cerbos, OPA, and OpenFGA, each offer different approaches from embedded libraries to centralized policy engines to relationship-based models. These provide complete control over your authorization infrastructure but require significant authorization expertise, operational resources, and ongoing maintenance. You'll need to build enterprise features, manage infrastructure, implement audit trails, and handle scaling yourself.

For B2B SaaS companies building applications that AI agents will access, WorkOS FGA provides a fundamentally different approach. Rather than assembling and maintaining authorization infrastructure yourself, you get enterprise-grade authorization with fine-grained, resource-scoped permissions as a managed service. The hierarchical resource model handles complex permission inheritance automatically. Sub-50ms performance ensures agents can operate at machine speed. Enterprise identity integration means your customers' IT admins can control access through their existing IdPs. Built-in audit logging provides compliance-ready tracking. And the incremental adoption path means you can start small and expand as your authorization requirements grow, all without requiring deep authorization expertise from your team.

If you're selling to enterprises and need to prove your application respects customer access controls when agents interact with it, WorkOS FGA lets you focus on building your application instead of becoming an authorization infrastructure company.