AI engineering teams are moving agents from single-user demos to multi-user enterprise deployments, and authentication breaks first.

A prototype can run on environment variables or a shared service account. A production agent that acts across tenants, users, and enterprise systems needs delegated authorization, credential isolation, policy enforcement, and audit trails. Without that layer, teams inherit credential drift, rate-limit collisions, broad API keys, inconsistent policy decisions, and confused deputy risk from indirect prompt injection attacks.

The right platform depends on what the agent needs to do: execute governed actions for users, connect quickly to many tools, sync product data, extend an identity layer, or secure infrastructure around the agent.

This article compares the available platforms across authorization enforcement, credential management, deployment model, tool execution, consent and approvals, and auditability.

TL;DR

Production AI agents fail when they rely on shared service accounts, static API keys, or DIY OAuth. You end up with confused deputy risk, token drift, and weak auditability. The durable pattern is two identities plus delegated context: the agent, the user, and the task-specific authorization context evaluated at runtime.

  • Best overall for 2026 production agent auth: Arcade.dev (action runtime + delegated context + per-action permission intersection + token vault + hosted tool execution + audit logs).
  • Choose Auth0 or WorkOS if you’re extending an existing CIAM/IdP and will build/own the execution runtime.
  • Choose Composio for individual use cases and rapid prototyping across many apps.
  • Choose AWS AgentCore if your team is standardized on AWS services.
  • Choose Nango or Merge when integration infrastructure is the primary requirement, and you will handle agent authorization separately.
  • Non-negotiables: two-identity modeling, delegated context, per-user token vault + auto-refresh, just-in-time consent, runtime policy hooks (HITL), OTel audit trails, SOC 2 Type II + KMS/HSM encryption.

Quick comparison of AI agent authentication platforms (2026)

The key question is where authorization is enforced. Gateways and wrappers can connect agents to tools. A runtime is the control point where credentials are resolved, permissions are checked, policies are applied, and the tool call executes for a specific user, agent, tenant, resource, and task.

AI agent auth platform comparison matrix

PlatformDeployment modelCredential supportAuthorization enforcement pointMCP/tool executionBest for
ArcadeManaged cloud, hybrid/private MCP servers, VPC, air-gapped, and enterprise self-host on KubernetesPer-user OAuth token vault, auto-refresh, and secrets for API-key-based custom toolsRuntime-enforced user + agent + delegated context intersection before tool executionHosted execution, agent-optimized tools, and governed MCP gatewaysProduction multi-user autonomous agents
ComposioManaged cloud with SDKs, CLI, MCP clients, and remote/local sandbox optionsPer-user connected accounts with managed authSession and tool-level controls for fast agent integrationsMCP gateway, SDKs, and tool catalogIndividual use cases and rapid prototyping
AWS AgentCoreFully managed AWS-native servicesIAM, OAuth, OBO flows, secure credential exchange, and AWS credential servicesAWS-native identity, gateway security, and policy controls across AgentCore servicesManaged gateway that turns APIs, Lambda, and services into MCP-compatible toolsAWS-native agent infrastructure
NangoCloud or open-source self-hosted deploymentPer-connection OAuth, API-key credentials, and managed refreshIntegration-level credential management. Teams own the agent/user permission intersectionSyncs, webhooks, action functions, and MCPCode-owned integration infrastructure
MergeManaged SaaS APIs and Agent HandlerLinked accounts, plus per-user or group auth in Agent HandlerScoped access over Merge connectors and Agent HandlerUnified APIs and MCP-ready connectorsEmbedded integrations and early governed agent tooling
Auth0Managed identity tenantOIDC/OAuth, agent identity, and Token VaultIdentity and policy layer. Teams provide the tool execution runtimeNo native MCP runtimeExtending Okta/Auth0 identity programs
WorkOSManaged identity and authorization APIsSSO, Directory Sync, and relationship-based FGAPolicy decision layer. Teams provide token vaulting and tool executionNo native MCP runtimeFine-grained policy for teams building their own runtime

How we evaluated AI agent authentication platforms

We reviewed current product pages and official documentation, then compared each platform against the production requirements for multi-user agents that take actions across enterprise systems. Raw connector count was secondary. The priority was whether the platform could safely authorize, execute, and audit real actions for real users.

We evaluated each platform across seven criteria:

  • Authorization enforcement: Whether the platform enforces permissions at execution time using the user, agent, tenant, resource, scope, task, and delegated context.
  • Credential management: Whether the platform provides OAuth token vaulting, automatic refresh, secrets/API-key support, and isolation from the LLM context window.
  • Consent and approvals: Whether the platform supports just-in-time consent, verified first-time authorization, and step-up approvals for commit actions.
  • Tool execution model: Whether the platform executes tool calls in a governed runtime or only provides identity, policy, SDKs, gateways, or integration functions.
  • Deployment model: Whether the platform supports managed cloud, hybrid, private MCP servers, VPC, air-gapped, or self-hosted deployment models.
  • Auditability and compliance: Whether logs are detailed enough for SIEM, incident response, SOC 2 review, and per-action chain of custody.
  • Best-fit architecture: Whether the platform is built for production agent actions, rapid prototypes, product integrations, identity/policy layers, or cloud-native infrastructure.

We weighted runtime authorization highest because the core threat model for multi-user agents is a classic version of the confused deputy problem, adapted to agents. When malicious content enters an agent’s context window, the LLM can autonomously call an API using the application’s underlying credentials. If the platform relies on broad service credentials, the agent blindly executes undesired actions.

Category 1: Agent runtimes, gateways, and AWS-native infrastructure

Agent-native authorization runtimes are complete infrastructure platforms built specifically to execute, secure, and manage the lifecycle of AI tool calling. They sit directly between your LLM orchestration layer and the destination MCP servers and tools, managing identity policy and executing the actual network request.

Arcade (agent-native action runtime)

Best for

Engineering teams scaling multi-user, multi-tool agents that require strict, per-action authorization, secure token vaulting, and reliable MCP tools without rebuilding the infrastructure themselves.

Overview

Arcade is a purpose-built, vendor-neutral action runtime for building and deploying AI agents that take actions across enterprise systems. It is the execution layer where credentials are resolved, permissions are checked, policies are applied, and the tool call runs. It unifies agent authorization, an extensive library of intent-optimized tools, and tool- and agent-level governance into a single infrastructure layer.

Arcade enforces a strict permission intersection model at execution time. This means agents only act within the intersection of their own scoped permissions and the delegated user’s permissions. External credentials stay completely isolated from the LLM context window.

That runtime placement matters. A stateless gateway can route requests, but it cannot reliably evaluate where a request sits inside a multi-step agent workflow. Arcade evaluates the specific user, agent, tenant, resource, scope, and task context at the point of execution.

Key features

  • Multi-user, post-prompt authorization: Evaluates access rights per action at the exact moment of execution. This prevents privilege escalation and neutralizes indirect prompt injection attacks before they reach the API.
  • Two-identity delegated context: Carries the agent identity, delegated user identity, tenant, scope, audience, resource, task ID, and expiry through the tool call.
  • Automated token vault: An encrypted, per-user, per-provider vault that handles the full OAuth token lifecycle automatically. Async token refresh, rotation, and scope mismatch resolution happen without developer intervention.
  • Secrets for API-key tools: Supports managed secrets for custom tools, including API-key-based integrations when OAuth delegation is not available.
  • Just-in-time consent: Requests new provider access or scopes only when a task needs them, then resumes execution without exposing credentials to the LLM.
  • Verified first-time authorization: Binds first-time OAuth authorization to the authenticated app user so the wrong user cannot complete an intercepted consent flow.
  • Agent-optimized MCP tools: A large catalog of tools optimized for LLM intent rather than raw API wrappers. This semantic alignment reduces parameter hallucination and schema mismatches compared to the alternative.
  • Contextual Access capability: Pre- and post-tool-call policy hooks for injecting custom governance logic, including required out-of-band approvals for human-in-the-loop workflows on irreversible commit actions.
  • OTel-compatible audit logging: Generates standardized logs for SIEMs to support enterprise SOC 2 and compliance audits, tracking the user, agent, policy decision, and arguments for tool actions. Because Arcade doesn’t touch or store the underlying data flowing through tool calls, it simplifies compliance and integrates with existing DLP and AI security posture tools for PII scanning, never acting as a new policy silo.

Pros

  • Eliminates the massive engineering burden of building per-user OAuth flows, handling token drift, and synchronizing token expirations across different providers.
  • Provides a strong security posture against prompt injection because credentials never touch the LLM or the client application.
  • Fully agnostic to models, frameworks, and clients. Avoids cloud vendor lock-in while offering flexible deployment models, including managed cloud, hybrid/private MCP servers, VPC, air-gapped, and enterprise self-hosted deployments.

Cons

  • The cloud-hosted version uses specific callback URL patterns that highly customized legacy identity providers require manual adaptation to support.

Pricing

  • Free tier available for development, testing, and rapid prototyping.
  • Usage-based pricing based on tool calls and auth events, alongside a platform fee.
  • Enterprise tier provides VPC, air-gapped, custom SLA requirements, and dedicated support.

Composio (MCP Gateway and Integration Wrapper)

Best for

Developers and individual users who need to prototype AI agents quickly across many apps.

Overview

Composio provides managed authentication, per-user sessions, MCP access, SDKs, and a large catalog of pre-built tools. It is best suited for individual workflows and prototype agent builds where speed of setup matters more than centralized enterprise governance.

Key features

  • Extensive connector catalog: Covers many apps and tool actions out of the box.
  • MCP access through sessions: Connects agents to tools quickly through session-based MCP usage.
  • Managed auth: Handles standard OAuth flows and per-user connected accounts.
  • Intent-based tool search: Helps agents select actions from the catalog.
  • Framework agnostic: Provides SDKs for Python, JavaScript/TypeScript, and native framework integrations like LangChain and LlamaIndex.

Pros

  • Fast setup for prototypes, hackathons, and early-stage agent builds.
  • Broad connector catalog for common SaaS tools.
  • Drop-in integrations with popular open-source AI frameworks.

Cons

  • Better fit for individual use cases and prototypes than centralized, enterprise-wide agent governance.
  • MCP-based usage does not provide the same runtime-enforced agent and user permission intersection as a full action runtime.
  • Restricts SOC 2 Type II compliance to its highest Enterprise tier, complicating security reviews for startups.
  • Observability does not publish the same OTel-first audit model expected in SIEM-heavy enterprise environments.
  • The tool catalog is only extensible using the vendor SDK, promoting vendor lock-in. Connecting external MCP servers into the gateway is not supported.

Pricing

  • Per-tool-call tiered pricing model.
  • Free tier available for individual developers and testing.
  • Pro and Enterprise plans required for higher rate limits, compliance standards, and priority support.

AWS AgentCore (AWS-native agent identity and runtime)

Best for

Teams standardized on AWS that want a managed, native agent stack and accept the resulting service coupling.

Overview

AWS AgentCore provides a suite of managed cloud services for building, deploying, routing, observing, and securing agents inside AWS. It leans on AWS identity, networking, policy, and observability primitives rather than providing a vendor-neutral action runtime.

Combine AgentCore Runtime, Gateway, Identity, Policy, and Observability, and AWS gives you a broad native environment for agent deployment. The tradeoff is tighter coupling to AWS services and operating models, and the need to manually manage how the services work with each other.

Key features

  • AWS IAM and OAuth integration: Integrates AWS IAM with OAuth and on-behalf-of identity flows for agent access.
  • AgentCore Runtime and Gateway: Provides managed runtime infrastructure and gateway components for tool and MCP access.
  • AWS-native policy and observability: Uses AWS services for policy enforcement, logs, traces, metrics, and operational controls.

Pros

  • Aligns with existing architectures and native integrations for organizations standardized on AWS.
  • Uses AWS IAM, networking, and security operations patterns that many enterprises already run.
  • Provides high scalability and availability backed by mature AWS infrastructure.

Cons

  • Creates significant AWS ecosystem lock-in. Moving to GCP, Azure, or hybrid on-premises environments requires re-architecting the identity, runtime, and observability layers.
  • Requires AWS platform expertise across identity, networking, runtime, gateway, observability, and cost controls.

Pricing

  • Pay-as-you-go model across AgentCore services and underlying AWS services.
  • Total cost of ownership spans runtime, gateway, identity, observability, model usage, storage, and networking.

Category 2: Unified APIs and integration runtimes

Unified-API and integration platforms were originally built to simplify traditional B2B SaaS integrations. They’re now pivoting to support AI agent use cases.

These tools excel at standardizing disparate API schemas, managing product integrations, and keeping background data pipelines fresh. They can support AI agents through MCP or action functions, but their core fit is integration infrastructure rather than turnkey agent governance.

Nango (code-first integration runtime for syncs, actions, and MCP)

Best for

Engineering teams that need code-owned integration infrastructure for data syncs, webhooks, and selected agent tools.

Overview

Nango is a code-first integration platform for managing OAuth, API credentials, syncs, webhooks, proxy requests, and integration functions.

Nango supports MCP and tool calling through action functions. Its core fit is code-owned integration infrastructure for external-account auth, syncs, webhooks, and selected agent tools rather than turnkey agent governance.

Key features

  • Continuous data syncs: Keeps third-party data fresh for product workflows and agent context.
  • Webhooks and triggers: Supports reactive automation alongside polling and proxy requests.
  • Integrations as code: Lets teams manage integration logic through a structured, code-owned workflow.
  • White-labeled auth flows: Provides end-user authentication and authorization for product integrations.
  • Action functions through MCP: Exposes selected functions as tools for agent workflows.
  • Logs and OTel export: Provides integration observability for debugging and operations.

Pros

  • Effective at keeping third-party data fresh for product workflows and agent context.
  • Code-first model gives engineering teams control over integration behavior.
  • Handles API polling, proxy requests, and webhooks in one integration layer.

Cons

  • Agent tools are built from custom action functions, so teams still own tool design and safety tuning.
  • Higher operational overhead required to maintain custom integration code compared to turnkey managed agent runtimes.
  • No native runtime-enforced agent and user permission intersection for delegated agent actions.

Pricing

  • Pricing scales across active connections, proxy requests, function runs, compute, logs, sync storage, and webhooks.
  • Free tier provided for testing and low-volume usage.

Merge (normalized unified API with early Agent Handler support)

Best for

Teams that need standardized embedded integrations across specific SaaS categories and want to evaluate early agent-tooling features separately.

Overview

Merge offers a Unified API that normalizes data within fixed software categories, including HRIS, ATS, CRM, Ticketing, Accounting, and File Storage. By making CRMs or ATS platforms look similar to the developer, Merge reduces integration debt.

Merge Agent Handler adds MCP-ready connectors, tool packs, authentication options, DLP, audit trails, and SIEM streaming on top of Merge’s integration infrastructure. It is a newer layer relative to Merge’s mature Unified API. Evaluate it separately for production agent action use cases.

Key features

  • Category unified APIs: Provides stable normalized schemas across HRIS, ATS, CRM, Ticketing, Accounting, and File Storage categories.
  • Embedded auth link: Provides a drop-in UI component for end-user authentication and authorization.
  • Normalized webhooks: Standardizes event listening across fundamentally different third-party platforms into a single event stream.
  • Agent Handler: Exposes selected tools to agents through MCP-ready connectors with scoped permissions and audit controls.

Pros

  • Reduces engineering maintenance when integrating with multiple tools in the same software category.
  • Normalized schemas reduce API complexity and boilerplate code for developers.
  • Mature core Unified API infrastructure for embedded integrations.

Cons

  • Unification uses a lowest-common-denominator schema, so agents lose access to niche, app-specific actions that don’t fit the common model.
  • Agent Handler is newer, so tool coverage, policy model, and deployment fit still need validation before production use.
  • Built primarily for B2B embedded data syncs, so agent tool-calling is still adjacent to a data-sync-first architecture.

Pricing

  • Unified API pricing is contract-oriented and commonly based on linked accounts and product usage.
  • Agent Handler pricing uses usage credits and separate plan tiers.
  • Free sandbox environment available for initial testing.

Category 3: Identity providers (CIAM/IdP) for agent identities

Traditional customer identity and access management (CIAM) and workforce identity platforms are now releasing features specifically targeting machine and agent identities. These platforms are strong at directory management and complex authorization modeling, but they explicitly leave the tool execution and MCP gateway layers to you.

Auth0 (Okta) for AI agent identities

Best for

Enterprises already using Auth0 or Okta that want to extend existing identity architecture to include agents as first-class principals while owning the execution runtime.

Overview

Auth0 is extending its identity platform for AI agent use cases with OAuth, OIDC, agent identity, cross-app access, and token management capabilities. Its strength is identity architecture: defining the human, the agent, and the access grants that connect them.

Auth0 is a strong fit when an organization wants agent identity to live inside the same trust and compliance program as its existing Auth0 or Okta deployment. Teams still need to build or buy the runtime that executes MCP/tool calls, handles retries, and applies per-action governance.

Key features

  • Agent-as-security-principal: Supports distinct, trackable agent identities inside the existing identity architecture.
  • OAuth and OIDC foundation: Uses standard identity protocols for token issuance, token exchange, and API access grants.
  • Token management: Provides Token Vault (GA) for OAuth token storage, refresh, and exchange; Cross-App Access (XAA) is upcoming (as of July 2026) for centralized consent across the enterprise.
  • Fine-grained authorization: Supports authorization checks for RAG, APIs, and application resources.

Pros

  • Uses existing enterprise identity trust and compliance documentation. Makes it a straightforward architectural sell to the CISO.
  • Strong standards-based implementation of modern OAuth and OIDC specifications.
  • Backed by Okta’s proven enterprise scalability and extensive developer documentation.

Cons

  • Identity and authorization layer, not a complete action runtime. Engineering teams must bring their own MCP server and agent execution runtimes.
  • Implementing fine-grained authorization for complex, dynamic agent intents requires significant custom data modeling upfront.
  • Pricing can escalate rapidly when you’re multiplying thousands of human users by numerous corresponding agent identities.

Pricing

  • Contract-oriented CIAM pricing based on plan, users, tenants, enterprise features, and agent-related usage.

WorkOS fine-grained authorization for agents

Best for

B2B SaaS teams that need relationship-based authorization checks and directory sync for agent-aware products they are building themselves.

Overview

WorkOS provides core enterprise identity infrastructure, including SSO, directory sync, and Fine-Grained Authorization (FGA). Its FGA product acts as a policy decision layer for applications that need relationship-based access checks.

Built on relationship graphs, WorkOS helps teams define who can access which resources before an agent or application takes action. It does not execute external API calls or store delegated SaaS tokens.

Key features

  • Hierarchical FGA: Enforces access policies based on nested resource hierarchies, like Organization → Team → Document.
  • Directory sync: Pulls user groups, roles, and states directly from enterprise IdPs like Microsoft Entra and Okta automatically.
  • High-speed check APIs: Provides sub-50ms p95 policy checks for runtime authorization decisions.

Pros

  • Relationship-based FGA is strong for limiting lateral movement and unintended privilege escalation.
  • Effective for multi-tenant SaaS environments that require deeply customized data sharing rules.
  • Top-tier developer experience, SDKs, and clean API design for policy management.

Cons

  • Requires careful relationship tuple and schema design before agent workflows can rely on the policy model.
  • Doesn’t provide an execution runtime, a pre-built agent tool catalog, or an MCP gateway. WorkOS acts as the policy decision point.
  • No native external OAuth token vaulting. WorkOS determines if an action is allowed, but you have to build the vault to hold the credentials to actually execute the action.

Pricing

  • Usage-based pricing on FGA relationship checks.
  • Flat predictable rates for SSO and Directory Sync infrastructure features.

Adjacent tools: policy engines and workload identity

Tools like Cerbos and Oso provide reliable policy-as-code capabilities. They act as Policy Decision Points (PDPs), evaluating YAML-defined or DSL-defined rules at runtime, but they don’t inherently store user tokens or execute network calls. You have to pair these tools with an execution runtime or integration layer to function within an agent stack.

Confusing workload identity with delegated identity creates the wrong accountability model for user-delegated agents.

Workload identity platforms like Aembit are designed for service-to-service communication on the compute plane. They issue bounded authority to non-human entities where no human is in the delegation chain.

AI agents operating on behalf of a user require delegated identity. Treating workload identity tools as solutions for delegated on-behalf-of agent actions fails enterprise accountability requirements entirely.

Logging an agent’s multi-tenant actions under a generalized service account destroys the cryptographic audit trail linking the action back to the specific human who authorized it.

These adjacent tools are effective for securing the infrastructure that the agent runs on, but they don’t solve the fundamental “delegated user OAuth plus LLM execution” problem. Pair them alongside an agent runtime.

Reference architecture for delegated AI agent authentication (seven-step flow)

A safe execution pattern ensures credentials are never exposed to the context window and actions remain auditable:

  1. Human authentication: The end-user authenticates into the application through OIDC or an equivalent app-layer identity system.
  2. User-bound prompt: The app sends the user’s prompt to the agent orchestration layer and passes the authenticated user ID into every runtime call.
  3. Delegated context creation: The runtime binds the user, agent, tenant, scope, audience, resource, task ID, and expiry into a delegated execution context.
  4. Just-in-time authorization: If the task needs a new provider or scope, the runtime pauses execution, verifies the current app user, collects granular consent, and resumes the task.
  5. Intersectional policy check: The runtime cross-references the user’s identity and the agent’s baseline access, then calculates the strict intersection of allowed permissions for the specific tool action.
  6. Vaulted token retrieval and execution: Upon authorization, the runtime retrieves the specific per-user access token from an encrypted vault and executes the action against the target MCP server or API.
  7. Audit generation: The runtime generates an OpenTelemetry-compatible audit log with the human delegator, agent, tenant, task, resource, policy decision, approval state, and external action taken.

Worked examples: AI agent authentication in practice

Give an agent built-in Gmail access without maintaining your own OAuth

Pattern: A support or productivity agent needs to read and send Gmail for many users. Shared service accounts over-scope access and fail security review, and building per-user OAuth, storage, and refresh yourself is weeks of undifferentiated work.

Arcade advantage: Arcade provides built-in, per-user Gmail authorization with a managed token vault, so the agent gets scoped access without you maintaining your own OAuth. Composio and Nango can also broker Gmail OAuth; the difference is whether token vaulting, refresh, and per-action authorization come built in or require extra wiring.

Delegate a user’s Google Meet access for a Claude agent

Pattern: A Claude agent schedules and joins Google Meet calls for a user. The agent needs delegated Google access tied to that user, not a static, over-broad key.

Arcade advantage: Arcade runs the delegated OAuth handshake once, vaults the token, and enforces that the agent acts only within that user’s Meet permissions. If the agent is later tricked by an injected instruction, it still cannot exceed what the delegated user is authorized to do.

Automatically refresh OAuth tokens for a Notion integration

Pattern: Long-running agents routinely hit expired Notion tokens mid-task. A per-user, per-provider token vault needs to refresh and rotate tokens automatically so the agent keeps working without re-prompting the user.

Arcade advantage: Arcade provides a built-in automated token vault for agent workloads, keeping provider tokens isolated from the LLM context window while maintaining persistent access. Auth0 and Nango offer token vaulting too; for agent workloads, the deciding factors are automatic async refresh, execution context, and runtime authorization.

Enforce granular enterprise permissions for Xero and Outlook

Pattern: Finance and operations agents touching Xero or Outlook need per-user, per-action authorization plus an audit trail. Irreversible actions, such as posting an invoice in Xero or sending an external email from a shared Outlook mailbox, require human approval and a durable record.

Arcade advantage: Arcade’s Contextual Access policy hooks evaluate the agent-and-user permission intersection on every call and can require human approval before the action executes. Policy engines like Cerbos or Oso complement this pattern when a runtime enforces their decisions at execution time.

How to choose the right AI agent auth platform

Start with where authorization is enforced. Gateways and wrappers can connect agents to tools. A runtime is the control point where credentials are resolved, permissions are checked, policies are applied, and the tool call executes for a specific user, agent, tenant, resource, and task.

Account for the MCP authorization gap

The industry standard for connecting agents to external systems is the Model Context Protocol (MCP), developed by Anthropic, but MCP does not solve agent authorization by itself.

The current MCP specification defines authorization for HTTP-based transports, but authorization is not mandatory for every MCP implementation. MCP defines the handshake. The runtime still needs to handle token vaulting, just-in-time consent, verified user binding, policy enforcement, and audit logs. Connect an LLM directly to an MCP server using static tokens, and you’ve bypassed user-level security entirely.

Check permission intersection

Safe authorization needs the permission intersection model. Platforms must evaluate the strict intersection of what the agent can do and what the delegated user can do, per action. Effective permission is always this intersection and never the user’s full permission set.

That check needs three inputs on every tool call: the agent identity, the human user identity, and the delegated execution context. The context binds scope, audience, tenant, resource, task ID, and expiry to the request.

Two-identity modeling and permission intersection are related, but they are not the same thing. Two-identity modeling defines the actors: the agent and the user. Permission intersection defines the decision: the action is allowed only when both the agent and the user are allowed to perform it in that delegated context.

Check production readiness

Production readiness comes down to strict non-negotiables:

  • Two-identity delegated context: Every request must carry the agent identity, the human user identity, and the task-specific context. The runtime evaluates user, agent, tenant, resource, scope, task ID, and expiry together.
  • OIDC and OAuth separation: OIDC authenticates the human user. OAuth authorizes the agent’s tool access on that user’s behalf. Conflating them creates reusable, over-scoped tokens.
  • Short-lived, scoped, audience-bound tokens: Tokens need resource and action scopes, audience binding, and short lifetimes to limit replay and lateral movement.
  • Automated per-user token vaulting and per-tenant permissioning: Long-running async agents need credentials that automatically refresh across multiple providers without user intervention. Platforms must enforce permissions per user and per tenant, ensuring credentials stay completely isolated from the LLM context window.
  • Just-in-time consent: Agents request new scopes only when a task needs them. Blanket onboarding consent over-permissions users before the agent knows the action.
  • Read, draft, and commit approval levels: Reading and drafting stay low friction. External side effects like sending email, deleting records, committing code, or transferring funds require explicit step-up approval.
  • Verified first-time auth binding: First-time OAuth authorization must bind the consent flow to the authenticated app user, so an intercepted flow cannot be completed by the wrong person.
  • Context-aware policy hooks: Systems must support pausing execution for human-in-the-loop approvals on destructive actions and checking existing enterprise entitlement systems before every tool call.
  • OTel-compatible audit logs: Every tool call must generate a verifiable chain of custody with the user, agent, tenant, task, resource, policy decision, approval state, and outcome.
  • Enterprise compliance: The platform must pass SOC 2 Type II validation, support geographical data residency boundaries, and use KMS or HSM hardware for token encryption.

Legacy approaches like coarse OAuth scopes, flat role-based access control, shared service accounts, and unmanaged static API keys fail user-delegated agent auth. Managed secrets and API keys can still work for non-delegated tools or provider-limited integrations when they are vaulted, scoped, and kept out of the LLM context window.

To meet the audit and compliance requirements, a proper system log must capture both identities executing the action:

{
  "timestamp": "2026-06-14T08:23:45Z",
  "trace_id": "5b8a9d1e-4c2f-88a1",
  "event_type": "tool_execution",
  "identities": {
    "agent_id": "spiffe://internal/agent/financial-analyzer",
    "delegated_user_id": "usr_9a8b7c6d5e",
    "tenant_id": "tenant_acme"
  },
  "delegated_context": {
    "task_id": "task_123",
    "audience": "salesforce-api",
    "resource": "opportunity_456",
    "scope": "opportunity.update",
    "expires_at": "2026-06-14T08:38:45Z"
  },
  "tool": {
    "server": "mcp-salesforce-gateway",
    "action": "update_opportunity",
    "vaulted_token_reference": "kms-enc-771a"
  },
  "policy": {
    "decision": "allow",
    "policy_version": "2026-06-01",
    "intersection_policy_applied": "strict_obo"
  },
  "approval_status": "not_required",
  "prompt_hash": "sha256:8c42...",
  "status": "authorized"
}

Choose by architecture

Choose Arcade when your agent needs to execute real actions for many users, and you need delegated authorization, token vaulting, policy hooks, tool execution, deployment flexibility, and audit logs in one runtime.

Choose Composio for individual use cases, rapid prototypes, and fast access to many tools. Use Nango or Merge when integration infrastructure is the primary requirement and agent authorization is handled separately. Choose Auth0 or WorkOS when your priority is extending an existing identity or policy layer, and you will build or buy the execution runtime. Choose AWS AgentCore when your team is standardized on AWS-native agent infrastructure. Use policy engines and workload identity tools alongside the runtime to secure infrastructure and policy decisions.

Conclusion

Production agent auth is execution infrastructure, not just identity infrastructure. If your agents rely on shared credentials, custom OAuth glue, or broad API keys, you inherit confused deputy risk and weak auditability.

Arcade provides the action runtime for teams moving multi-user agents into production. It brings delegated authorization, permission intersection, token vaulting, policy hooks, hosted tool execution, flexible deployment, and audit logs into one layer.

Evaluate Arcade.dev to secure agent operations without rebuilding that infrastructure from scratch.

AI agent authentication and authorization FAQ

What is the difference between AI agent authentication and authorization?

Authentication verifies the identity of the user and the agent interacting with the system. Authorization determines what specific actions the agent and the user can take by calculating the strict intersection of their combined access rights.

What is the two-identity model for AI agent authorization?

Every tool call carries two identities: the agent application making the request and the human user on whose behalf the request is made. A production runtime evaluates both identities plus the delegated task context before executing the action.

Why should OIDC and OAuth stay separate for agents?

OIDC authenticates the human user into the application. OAuth authorizes the agent’s tool access on that user’s behalf. Keeping them separate prevents user login sessions from becoming broad, reusable tool credentials.

How do I prevent confused deputy attacks in AI agents?

Use a post-prompt, runtime authorization layer that scopes all tool executions strictly to the end-user’s permissions. Never allow an agent to execute API calls using a blanket, shared service account.

How should I handle OAuth token refresh for autonomous AI agents?

Use an automated, encrypted token vault that’s completely isolated from the LLM context window. This external vault must handle background rotation, provider-specific expiration limits, and scope mismatches during long-running async tasks.

What role does MCP play in AI agent security?

The Model Context Protocol standardizes how AI agents connect to external data sources and execution environments. Its HTTP authorization spec defines an authorization pattern, but MCP does not provide multi-tenant token vaulting, runtime policy enforcement, or audit logs. Securing the MCP gateway with a strict, user-delegated authentication and authorization runtime layer is the critical foundation for safe production deployments.

What is on-behalf-of (OBO) access for AI agents?

OBO access means an agent calls an API using a user-delegated token so every action is performed and audited as that specific user, not a shared service account.

Do I need an agent runtime if I already use Auth0/Okta/WorkOS?

For production delegated actions, yes. IdPs handle identity and policy, but you still need a runtime/gateway to vault tokens, execute tool calls safely, and produce per-action audit logs.

Can I use service accounts or API keys for AI agents in production?

Only for non-delegated, service-to-service tasks. For user-delegated actions, they break accountability and increase confused deputy risk because the agent can act with overly broad privileges.

What is the permission intersection model and why does it matter?

The permission intersection model is a security model where effective permission is the intersection of the agent’s allowed actions and the user’s allowed actions, evaluated per tool call to prevent privilege escalation.

What is just-in-time authorization for AI agents?

Just-in-time authorization means the agent requests provider access or a new scope only when a specific task requires it. The runtime pauses, collects granular consent, vaults the token, and resumes execution.

Why choose an agent runtime instead of an MCP gateway?

An MCP gateway connects agents to tools. An agent runtime enforces authorization at execution time, where it can resolve credentials, check the user-agent permission intersection, apply policy hooks, execute the tool call, and generate the audit record.

Does MCP include authentication and authorization by default?

Not by itself. MCP standardizes tool connectivity and defines optional HTTP authorization behavior, but token vaulting, audit trails, and runtime policy enforcement are out of scope. You need an additional auth/runtime layer for production.

When should I choose Arcade?

Choose Arcade when you need to deploy multi-user agents that take real actions across enterprise systems, and you need delegated authorization, reliable tools, and governance in a single runtime. Arcade is built for teams that want to skip months of building per-user OAuth flows, token vaults, just-in-time consent, policy hooks, and audit infrastructure. If you’re scaling beyond a single-user prototype, need to pass security reviews, or operate in a regulated industry, Arcade provides the complete action runtime to get to production without assembling separate services.

What should an audit log include for AI agent tool calls?

At minimum: timestamp, trace ID, agent ID, delegated user ID, tenant, task ID, resource, tool/action, policy decision, policy version, approval status, and outcome. Every action needs to be attributable and reviewable for compliance.

How do I support human-in-the-loop approvals for risky agent actions?

Use runtime policy hooks that can pause execution and require explicit approval before destructive actions (e.g., sending money, sending external emails, deleting records, changing permissions).

What’s the difference between workload identity and delegated identity for agents?

Workload identity secures service-to-service calls for compute. Delegated identity secures user-on-behalf-of actions and must preserve user attribution and consent.