The Tool Desk
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How do OAuth and workload identity differ?
OAuth is not itself an identity type. It is a framework that lets a client obtain access tokens under an authorization server’s rules. In a delegated flow, a user authorizes the client to access specified resources or perform specified actions. The resulting authorization reflects the user’s consent and granted scope.
Workload identity identifies software running as a non-human principal: for example, an agent deployed on a cloud runtime or a service operating in another environment. Its credentials can come from the runtime, a cloud identity system, or an external identity provider. The target service still needs a way to authorize that principal; an identity alone does not automatically grant access.
| Decision axis | Delegated OAuth | Workload or agent identity |
|---|---|---|
| Whose authority is used? | A named user’s consent and authorized scope. | The running workload or agent’s own principal. |
| Typical task | Reading or changing resources for a specific user. | Autonomous service-to-service work. |
| Identity or credential source | An authorization server and user consent. | A runtime, cloud platform, or external identity-provider assertion. |
| Credential handling | Protect and appropriately scope access and refresh tokens. | Prefer platform-managed or federated short-lived credentials over static keys where supported. |
| Attribution | Actions can be associated with the delegated user and client context. | Actions can be associated with the distinct workload or agent principal. |
| What must be supported? | The target API must support a suitable OAuth flow and scopes. | The runtime, federation trust, target IAM configuration, and API support must align. |
These are not mutually exclusive categories. Google’s Agent Identity overview lists three-legged OAuth, two-legged OAuth, cloud identity, and OIDC federation for different authorities and targets.
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When should an AI agent use delegated OAuth?
Use a user-delegated flow when the agent is meant to work with a particular person’s data or permissions, such as accessing that user’s calendar or modifying a document they are authorized to edit. The user’s grant should be limited to the scopes the task needs. If the platform provides an on-behalf-of flow, use the platform’s documented pattern rather than treating the agent as an unrestricted copy of the user.
Google documents three-legged OAuth for external tools used with user authority. Microsoft documents an agent on-behalf-of pattern in its Entra Agent ID authentication guidance. Their examples are platform-specific: verify which flows and scopes the target service actually accepts.
Should an autonomous AI agent use a service account or workload identity?
For an autonomous production agent, give it a distinct principal and grant only the permissions needed for its job. Depending on the platform, that principal may be an attached service account, a managed identity, or an agent-specific identity. Google describes these workload identity options in its identities for workloads documentation, and its Agent Identity documentation describes identities tied to an agent’s lifecycle.
A service account can be one way to represent a workload, but avoid assuming that every service account is automatically short-lived, managed, or least-privilege. Configure the identity and its permissions deliberately. Google warns that service-account keys can pose a security risk if not managed correctly and advises choosing a more secure alternative when possible.
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How should an agent authenticate to an external service?
Use the machine-to-machine method the external service supports when the agent acts under its own authority. Google recommends two-legged OAuth for external services that support OAuth, and lists OIDC federation as another option for external backends. The available option depends on the target’s identity and authorization capabilities, not just the agent framework.
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If the agent must act for a user at that service, use the service’s supported delegated flow instead. An agent’s own identity and a user’s delegated authority answer different questions: “which workload is calling?” and “whose access is being exercised?”
How can an AI agent access tools on behalf of a user?
- Confirm the authority. Decide whether each tool call should use a specific user’s authority or the agent’s own principal. Do not infer user delegation merely because a person launched the agent.
- Check the target’s supported flow. Confirm the API’s OAuth grant or on-behalf-of support, required scopes, and token handling requirements. For workload access, confirm the supported identity type, trust configuration, and IAM permissions.
- Grant only what the task needs. Keep delegated scopes or workload permissions narrow, and separate production agent access from a developer’s own broad identity.
- Protect and manage credentials. Use the platform’s supported token or federation mechanism, and establish how credentials are refreshed, revoked, and rotated. Prefer short-lived federated or platform-managed workload credentials over static keys where supported.
- Check attribution and logs. Verify which principal appears in audit records and whether that gives operators the accountability they need. Google notes that MCP actions made using a user identity are attributed to that user and have the same permissions.
What changes when the agent uses MCP?
MCP authentication is not uniform across clients and servers. Google says available methods vary by application; its remote servers do not support Dynamic Client Registration or OAuth Client ID Metadata Documents. Check the credentials the specific MCP client supports and the server’s documented authentication options before choosing an architecture.
For production MCP workloads, Google recommends a separate agent or workload identity rather than using a developer’s identity, so permissions can be limited and actions logged under the distinct principal. Using a person’s identity can make agent actions inherit that person’s permissions, which may be broader than the agent needs.
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What OAuth security controls should an agent implement?
RFC 9700, the IETF’s Best Current Practice for OAuth 2.0 Security published in January 2025, sets out current OAuth security guidance. It says public clients must use PKCE; it recommends PKCE for confidential clients as well. The RFC also recommends asymmetric client authentication, such as mutual TLS or signed JWTs, where feasible, rather than storing sensitive symmetric keys at the authorization server.
To reduce misuse of stolen access tokens, RFC 9700 says authorization and resource servers should use sender-constraining mechanisms such as mutual TLS or DPoP. These controls complement, rather than replace, least-privilege scopes, careful token storage, and a suitable identity choice.
Microsoft’s guidance for its described agent integration identifies managed identities as the preferred credential type and warns against client secrets in production agent identity blueprints, pointing instead to federated identity credentials or client certificates. That is Microsoft-specific implementation guidance, not a universal OAuth protocol requirement.
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Read RFC 9700 for the protocol security recommendations. NIST SP 800-63C-4, published August 1, 2025, provides general guidance on federation and assertions; it is not an AI-agent-specific comparison of OAuth and workload identity.
How to choose
- The agent operates on a user’s behalf: choose delegated OAuth or the platform’s on-behalf-of flow, with only the required scopes.
- The agent runs autonomously in production: assign a distinct managed, attached, or agent-specific identity and apply least-privilege permissions.
- An external workload needs Google Cloud access: use Workload Identity Federation where the workload and target configuration support it.
- The agent calls an external service as itself: use the service’s supported machine-to-machine authorization method.
- The agent connects through MCP: choose from the authentication methods that the actual client and server support.
No identity pattern is universally safer or universally compatible. The right design depends on whose authority the task requires, where the agent runs, and what the target accepts. Before deployment, confirm token lifetimes, scopes, audit attribution, identity lifecycle, and revocation behavior for the specific platform and API.
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