Authorization for AI Agents: Permit.io, Descope & more

I have been exploring agent identity and the authentication/authorization platforms that could support it, while also examining how standards like OAuth 2.0 and frameworks such as Keycloak might apply. 

Below, I listed the best AI agent–specific platforms and features, categorized by their primary focus. Use the links below to jump to detailed review sections:

*Policy mechanism categories:

• Policy engines (Rego, Cedar, Keto): Use formal policy languages to define fine-grained authorization rules.
  • Best for: enterprises or regulated industries (finance, healthcare, gov) that need fine-grained / auditable policies for application or AI agents.
• Scope & role-based access: Rely on OAuth, roles.
  • Best for: teams that just need basic, service-level authorization tied to OAuth roles/scopes.
• Relationship / delegation models: Focus on agent identity, delegation chains, and continuous consent.
  • Best for: AI agent–heavy environments where identity, delegation, and continuous consent matter most (e.g., agents are delegated with user tasks).
• MCP / tool-level access control: Centered on securing Model Context Protocol (MCP) servers and tools. Define which APIs and tools an agent can access, with audit and rate limits.
  • Best for: agent frameworks that depend on MCP to connect agents with APIs/tools. 

Authentication & authorization features

Platforms with:

• RBAC (Role-Based Access Control): Define roles (e.g., admin, analyst, viewer) and assign them to agents for quick, structured permission management.
• ReBAC (Relationship-Based Access Control): Grant agents access based on user or resource relationships.
• ABAC (Attribute-Based Access Control): Use dynamic attributes (e.g., department, time) to control what agents can do in real time.
• HITL (Human-in-the-Loop): Require human approval before agents perform sensitive or high-risk actions.
• MCP-based (Model Context Protocol): Support the MCP standard so agents can request permissions or context securely across systems.
• Continuous consent: Continuously re-check agent permissions during activity, adapting to context or revoking access if conditions change.

Approaches to agent authentication and authorization

There are three common approaches to handling authentication and authorization for agents:

1. Standards:

Protocols such as OAuth2 are the foundation of agent authorization, allowing applications to access resources on behalf of a user. Most APIs require them, and AI agents can use them through:

• Token-based access: Agents can obtain and use OAuth 2.0 tokens, such as those used by web or mobile apps.
  
• Delegated authority: OAuth2 lets one app act on behalf of a user, mapping to an agent acting for its user.
  

However, standards alone are not sufficient for agentic use cases:

• Non-determinism: Agents rely on LLM function-calling to generate requests, which is non-deterministic. Thus, LLMs may not follow strict OAuth2 flows.
  
• Context handoff: Agents interact with many third-party APIs, each with unique authentication requirements. Every API call requires extra context (function definitions, parameters, and tokens) to work reliably.
  
• Human-in-the-loop gaps: OAuth2 doesn’t support approval workflows that let humans review or confirm actions before they are executed. Open-source tools also don’t provide this capability natively.

2. Open source authentication tools:

Open source frameworks like Keycloak, Authelia, or Zitadel help developers implement such standards in practice. These provide identity management, token handling, and policy enforcement around OAuth2 and related protocols.

However, while they make standards usable at scale, they are not agent-specific:

• These tools are for human users and applications.
• Running them involves heavy integration work

3. Developer platforms: 

Developer platforms can take much of the complexity out of securing agents. Instead of building your own systems for access control or API authentication, these platforms:

• Incude ready-to-use authentication and authorization flows.
  
• Provide agent-specific features such as continuous consent, delegated approvals, and MCP integration.
  
• Enable gradual adoption with basic auth or role-based access control (RBAC), and expand later into other AI models without needing to rewrite or restructure your system.

Permit.io

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Permit.io provides application-level authorization as a managed service. Instead of building and maintaining your own authorization system, developers can use Permit.io to manage access control policies, user roles, and permissions.

Best for organizations that need to enforce fine-grained access models (RBAC, ABAC, ReBAC) and manage policies consistently across their application stack.

Pricing: Permit.io’s pricing is based on monthly active users.

How Permit.io works

Permit.io simplifies authorization by decoupling policy from application code. Instead of embedding access rules directly in your app, it uses open-source policy engines (OPA) as the foundation and adds a management layer on top.

Permit.io object model

 At the core of Permit.io is the workspace, which represents your organization. A workspace can contain:

• Projects: logical groupings of applications or services.
  
• Environments: each project can have multiple environments (e.g., development, staging, production).
  

Within each environment, you define and manage policies, roles, and permissions. 

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Built-in authorization features

Permit.io includes a range of built-in authorization features that go beyond policy enforcement. Examples include:

• User management: configure roles, groups, and permissions.
  
• Audit logs: track access decisions and policy changes.
  
• Approval flows: support human-in-the-loop reviews for sensitive actions.
  
• API key management: issue, rotate, and revoke keys securely.
  
• Emergency access: provide temporary elevated permissions when required.

Below is an example of use management configuration:

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Real world Permit.io examples:

• Google drive permissions (ReBAC): Tutorial showing how to model and enforce relationship-based access control similar to Google Drive’s sharing model.²
  
• Fintech demo: Demonstrates fine-grained authorization flows for a self-service banking system, useful for financial use cases.³
  
• Healthcare demo: Sample healthcare app illustrating multiple models in practice.⁴

Strengths:

• Mature authorization platform: support for multiple access control models (e.g., RBAC, ABAC, ReBAC)
  
• Provides policy-as-code with OPA or Cedar.
  
• Has Access Request MCP and capability to enforce human-in-the-loop (HITL) approvals via MCP workflows.
  
• Full integration with Git for version control of policies.
  
• Unlike other vendors, includes enterprise authorization features like audit logs, approval flows, API key management, and embeddable UIs.
  
• Hybrid deployment options (control plane/data plane) to keep sensitive data local.

Limitations:

• Doesn’t natively provide authentication (AuthN),  you need an external identity provider.
  
• MCP integration still requires setup.

Ory (Keto)

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Primarily, Ory is an open-source identity and access management (IAM) platform. It also provides tools for authentication (AuthN), authorization (AuthZ), and identity management. It is code / configuration driven, there is no UI.

It has different modulessuch as:

• Hydra for OAuth2/OpenID connection
• Kratos for identity management
• Keto/Permissions for fine-grained authorization

Ory Keto Permissions is the particular product for agent auth. It integrates fine-grained, relationship-based access control into their apps or AI systems. 

Best for teams that want configuration control.

Strengths:

• Supports OAuth2 and OIDC standards, ensuring interoperability with APIs, SaaS tools, and enterprise systems.
  
• Offers fine-grained, least-privilege policies through Ory Keto
  
• Available as self-hosted, SaaS (Ory Network), or enterprise-licensed.

Limitations:

• Not AI-native: Designed for general IAM, with extensions for agents, but lacks agent-specific features like persistent agent identity or continuous consent (offered by agent.security).
  
• Provides authorization policies, but not as out-of-the-box or no-code as platforms like Permit.io.
• Requires more engineering effort and IAM expertise compared to plug-and-play platforms like Permit.io or Descope.
  

Descope (Agentic Identity Hub)

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Descope is an identity orchestration and governance platform for AI agents and the MCP ecosystem. It’s an external no-/low-code IAM (Identity and Access Management) platform, more likely a UI, which translates complex auth flows into visual components.

It provides both connectivity (to apps and SaaS tools) and identity governance at an enterprise scale. 

Its agentic identity control plane adds enterprise-grade governance and oversight. 

It’s built on top of standards like OAuth2 and MCP. 

How descope works:

• Inbound apps: Enable any application to act as its own identity provider using OAuth. This allows AI agents to authenticate, request scoped permissions, and operate with explicit user consent.
  
• Outbound apps: Provide AI agents with secure, prebuilt integrations to more than 50 SaaS applications (e.g., Slack, HubSpot, Gmail, Notion). Token handling, scope management, and refresh logic are abstracted away from developers.
  
• MCP Auth SDKs & APIs: Add standardized OAuth-based authorization to remote MCP servers. These SDKs enforce authorization policies at the server boundary.

Agentic identity dontrol plane builds on this foundation by adding enterprise grade features such as:

• Policy-based governance through a scope-aware policy engine that enforces role-based and hierarchy-driven restrictions.
  
• Monitoring and auditing to give security teams visibility into agent identities, access scopes, and potential misconfigurations or rogue agents.
  
• Identity lifecycle management for AI agents, covering registration, consent flows, token storage, and linking agent identities to human delegators.

Strengths:

• Combines authentication (OAuth, SSO, inbound app support) with authorization (scopes, policies, consent flows).
  
• Provides Outbound Apps with 50+ prebuilt SaaS connectors (Slack, Gmail, HubSpot, Notion, etc.).
• Delivers MCP Auth SDKs & APIs to secure remote MCP servers with OAuth-based authorization.
  
• Limitations:
• Documentation lacks complex implementation examples.
• Certain integrations (e.g., LinkedIn SSO,) require extra setup effort.
• Inconsistent terminology and UI abstractions compared to other IAM platforms

agent.security

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agent.security is an AI identity and authorization platform purpose-built for agents.  It works through a gateway layer that adds agent-specific controls.

Unlike Permit.io, which focuses on managing complex authorization policies across applications, agent.security addressES challenges unique to AI agents:

• Persistent identity: Instead of operating with anonymous tokens borrowed from human users, it provisions each agent with a persistent identity that remains valid across model updates and infrastructure changes.
  
• Continuous consent: It implements continuous consent so that permissions are not granted once and left unchecked but are verified and updated in real time as conditions evolve.
  
• Delegation chains: It also enforces clear delegation chains, ensuring that every action (e.g. accessing a database, sending an email) can be reliably traced back to the human who authorized it.

Strengths:

• Best for agent governance: persistent agent identity, continuous permission controls, and behavior oversight.
  
• A good fit for agents whose identity and permissions must evolve.

Limitations:

• Interfacing with MCP/agent frameworks might require integration.

 Composio AgentAuth

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AgentAuth is a developer platform purpose-built for AI agents that handles authentication to third-party services

Its for LLM function-calling, ensuring non-deterministic agents can still complete API calls reliably.

So, it differs from Permit.io and agent.security, which are primarily authorization-first platforms, and from Descope, which spans both AuthN and AuthZ with a broader identity orchestration layer.

Best for teams building AI agents that interact with many SaaS APIs. For example:

• Customer support bots connecting to HubSpot, Gmail, and Zendesk.
  
• Scheduling agents working with Google Calendar and Slack.
  
• Developer workflow agents automating GitHub, Jira, and CI/CD notifications.

Strengths:

• Built for agent authentication (AuthN) and API access, handling multi-method auth and token management.
  
• Developer-centric, offers integration with 15+ agent frameworks.
  
• Provides prebuilt integrations with 250+ apps (Gmail, Slack, HubSpot, Jira, Salesforce, etc.) and integrates with agentic frameworks (LangChain, CrewAI, LlamaIndex, and others).

Limitations:

• Authorization capabilities are likely minimal or limited to service-level or resource-level checks. You’ll need a dedicated authorization system if you require fine-grained policies, delegation, or HITL flows.

AgentPass

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Provides a managed MCP server platform. It aims to help organizations securely expose their APIs, tools, and systems to AI agents.

While it includes authentication and basic authorization (RBAC/ABAC at the enterprise tier), its primary goal is to abstract away the complexity of building and running secure MCP servers.

Strengths:

• Built specifically for MCP / agent integration: provides MCP server capability.

Limitations:

• Narrower in scope (mainly focusing on tool-level agent permission management).

How to implement AI agent authorization?

Clearly define your existing permission model

Authorization is about defining what an agent is allowed to do, and the foundation of that is your permission model. Roles are the most common way of structuring this.

Think of roles as bundles of permissions:

• A Reader role might only allow viewing records.
  
• An Editor role might allow creating or modifying records.
  
• An Admin role might inherit Reader and Editor rights, plus add the ability to manage users.
  
• An Auditor role might provide read-only access across all systems for compliance checks.

These roles should reflect real responsibilities and follow a consistent hierarchy. If Admins always inherit Reader rights, that inheritance must be consistent everywhere. Without this consistency, giving agents roles will just replicate human confusion.

Define fine-grained permissions

Broad permissions (like a single global API key) make safe delegation impossible. An agent holding such a key could read sensitive data, delete records, or perform actions far beyond its intended scope.

Instead, permissions should be fine-grained and modeled explicitly:

• In RBAC (Role-Based Access Control), this means separating actions into clear roles (e.g., Reader vs. Editor).
  
• In ABAC (Attribute-Based Access Control), permissions can be scoped by attributes such as time, location, or data sensitivity (e.g., read-customer-data during business hours).
  
• In ReBAC (Relationship-Based Access Control), actions can be defined by relationships (e.g., only managers can update records of their direct reports).

For example:

• “read-customer-data” is distinct from “delete-customer-data.”
  
• An agent generating a report only needs the first, not the second.

Align permissions with roles

When using OAuth or token-based systems, permissions should map directly to your roles.

Think of permissions as the “keys” that unlock the actions defined by roles:

• A reader permission corresponds to the Reader role.
  
• An editor permission corresponds to the Editor role.

Avoid broad, catch-all permissions like “full access.” They recreate the same risks as global API keys, giving agents far more authority than necessary. Permissions should reinforce your existing role model, not create a new parallel system.

Add governance and controls

Controlling how AI agents are granted and enforced is critical. Admins should have levels to restrict which agents or apps can request certain scopes and to enforce organization-wide policies. For example:

• Only admins can approve agents that request editor or admin scopes.
  
• Sensitive actions, such as spending money or deleting data, might require a human-in-the-loop approval before the agent proceeds.

This governance layer ensures authorization decisions aren’t just embedded in code but are actively managed.

Ensure full auditability

Every action taken by an agent must leave a clear trail that connects back to who authorized it and under what scope.

For example:

• If a support agent updates a customer ticket in Zendesk, the audit log should show it acted under the support-editor scope.
  
• If a reporting agent modifies entries in a financial database, the log should tie back to the finance-analyst role that delegated the permission.

Reference Links

1.

https://docs.permit.io/overview/how-does-it-work/

2.

https://docs.permit.io/modeling/google-drive

3.

https://docs.permit.io/modeling/mesa-verde

4.

https://docs.permit.io/modeling/rebac-GHC

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Cem Dilmegani

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Mert Palazoğlu

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Mert Palazoglu is an industry analyst at AIMultiple focused on customer service and network security with a few years of experience. He holds a bachelor's degree in management.

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