Delegated authority for AI agents is the authority a human or system grants an agent to act on its behalf, scoped to permitted activities and traceable back to the party that granted it. For a delegated task, authorization should evaluate both the agent performing the action and the limits of the grant under which it acts.
An agent may also have permissions assigned directly to its own identity. Those permissions should not silently expand what it can do on behalf of a requester. Reading a customer record, sending an email, or approving a transaction must remain subject to the applicable resource, action, and task policies.
Why delegation becomes harder across agent workflows
Delegation is not new. OAuth supports delegated access, and traditional applications can call downstream services on a user's behalf. AI agents add workflows that can branch and change during execution, making it especially important to preserve authority across each handoff.
A user asks an agent to complete a task. That agent delegates part of the work to a second agent, which invokes a tool that calls an API. By the time the API accesses a database, several components separate the operation from the original request.
Each boundary is a place where delegated authority can be lost, expanded, or applied to the wrong task. Two failure patterns deserve particular attention.
Authority gets dropped at the boundary
A tool that receives only an agent identity may not know which requester authorized the work or what the task permits. If it falls back to a powerful service credential, it can perform an operation that exceeds the original grant.
The receiving system needs trustworthy delegation context, or a way to resolve it, before deciding whether the request is authorized. When required context is missing or invalid, the workflow should reject the operation or obtain a valid grant through the appropriate process.
Authority gets confused between principals
The confused deputy problem occurs when a component with legitimate privileges is induced to use them for a requester who lacks the corresponding authority. In an agent workflow, this can happen when a tool uses its own broad access without checking the user's delegated limits.
Mixing context between users or tasks creates a related risk. An agent may carry a valid grant but apply it to a different request, resource, or objective. Binding the grant to the intended task and validating that binding helps prevent this misuse.
How to preserve delegated authority for AI agents
Delegation context must accompany the request through a trustworthy mechanism. That can include validated tokens and references to server-side task or grant records. An unverified user identifier or a natural-language claim of permission is insufficient.
Identify the grantor, agent, and permitted task
The grant should establish who authorized the work, which agent can act, which resources and operations are permitted, and the conditions under which access ends. Include the intended recipient or service where relevant, along with any limits on further delegation.
The purpose must be grounded in an approved task and policy. An agent should not be able to enlarge its authority simply by describing a broader objective.
Validate authority at every handoff
When agent A delegates to agent B, the receiving service should evaluate B's identity and eligibility to act, the applicable delegated grant, and the resource policy together. B's own privileges are not a substitute for the requester's authority.
Depending on the mechanism, validation includes checking the issuer, integrity, intended audience, expiry, task status, and whether onward delegation is allowed. Passing context along without checking it does not preserve a reliable security boundary.
Keep downstream access within the original grant
A subtask should receive only the scope it needs, and it must not exceed the authority delegated for the parent task. Scope can remain equal where necessary; it should narrow when the subtask requires less.
If the original grant permits summarizing a document, a downstream agent cannot use that grant to justify editing it. A broader operation requires a separately authorized change in scope. This connects delegation directly to least privilege for AI agents.
Preserve a record of the chain
Link the action to its task, parent delegation, originating requester, policy decision, and execution outcome. Preserve the scope and conditions relevant at the time of the operation.
Those records allow investigators to follow each handoff rather than stop at the identity that made the final API call. See what an AI agent audit trail should record.
What OAuth 2.0 Token Exchange provides
OAuth 2.0 Token Exchange, RFC 8693, defines a protocol for requesting security tokens from an authorization server. It supports delegation and impersonation, with mechanisms for representing the subject and the actor acting on that subject's behalf.
Delegation preserves the distinction between the acting party and the party represented. The specification includes an actor claim that can represent a delegation history. Implementations can use token exchange to obtain credentials appropriate to a downstream service.
The protocol does not automatically enforce an agent's task purpose, narrow every grant, or establish the deployment's trust model. Issuance policy, receiving-service validation, and authorization enforcement remain necessary. Token exchange also does not, by itself, guarantee that revoking an upstream token invalidates every token obtained from it; lifecycle behavior must be designed explicitly.
Example: delegate a summary without delegating editing
Suppose a user authorizes agent A to summarize a document. Agent A delegates retrieval to agent B, which calls a document tool.
- The task identifies the user, document, read operation, purpose, and expiry.
- Agent A delegates only the retrieval work that agent B needs.
- The document tool validates B's authority for the task and the user's access to the document.
- A request to edit the document is denied because it exceeds the grant.
- The records connect the retrieval and any denied requests to the parent task and delegation.
This is an illustrative design. Teams should also test a different document identifier, an expired task, a changed audience, and an attempted handoff to an unauthorized agent. A successful summary demonstrates functionality; these negative cases test whether the delegation boundary holds.
How IndyKite supports delegated agent workflows
IndyKite's connected identity and context model provides a basis for representing relationships among humans, agents, resources, and systems. With delegation context modeled and supplied, authorization can consider the authority under which an agent is acting alongside the requested operation.
AgentControl applies contextual control to agent workflows and supports traceability to data provenance and policy decisions. Its architecture materials describe delegation chain tracking and runtime auditability across agents, tools, and resources.
Intent-based access control adds task purpose and execution context to the evaluation. This helps address the case where a grant is valid but the requested action falls outside the approved objective. The relevant task constraints must still be defined and enforced at the points where operations occur.
Explore fine-grained authorization for AI agents to see how delegation fits into resource-level decisions, or read the AgentControl Architecture Guide.
