Introduction
A customer asks an AI agent to refund an order. The customer is already authenticated and has permission to view orders, update account information, manage payment methods, cancel purchases, and request refunds.
Should the AI agent simply inherit that customer’s identity and all of those permissions?
Definitely not. The agent was asked to perform one specific task. Yet if it operates using the customer’s full security context, downstream systems may see little distinction between an action performed directly by the customer and one selected and executed autonomously by the agent. The agent may also receive far more authority than the task actually requires.
This is the difference between delegation and impersonation in AI agent authorization.
With delegation, the relationship between the principal and the agent remains explicit. The user authorizes an identified agent to exercise defined authority for a particular purpose. The agent acts on behalf of the user without simply becoming the user.
User/Principal → Delegated Authority → AI Agent → Requested Action → Resource/API
Impersonation takes a different approach. The agent operates using a security context that represents the user as the subject. That can be appropriate in some architectures, but it becomes risky when autonomous agents receive broad user permissions or when downstream systems lose visibility into which actor actually performed an action.
Authenticating the user proves who initiated the relationship. It does not automatically prove that every action an AI agent later chooses to perform reflects that user’s intent—or that the agent should receive everything the user is allowed to access.
For autonomous agents, the authorization model needs to answer more than “What can this user do?” It also needs to answer “Which agent is acting, what authority was delegated to it, which resource is it accessing, and is this particular action within the approved boundary?”
That distinction becomes increasingly important as agents move beyond answering questions and begin calling APIs, invoking tools, changing account data, initiating transactions, or delegating work to other agents.
The safer architectural goal is not to make an AI agent indistinguishable from the user it represents. It is to preserve both identities while giving the agent only the authority required to complete the approved task.
What Is the Difference Between Delegation and Impersonation for AI Agents?
Delegation and impersonation can both allow an AI agent to perform an action connected to a human user, but they represent that relationship differently.
With delegation, the user remains the principal behind the request while the AI agent remains identifiable as the actor exercising delegated authority. The agent does not need to receive everything the user can access. Its permissions can be narrowed according to the task, resource, action, scope, audience, time window, and applicable policy.
With impersonation, the agent operates using a security context that represents the user as the subject. Depending on how the system is designed, downstream services may primarily see the user identity rather than a distinct agent acting on the user's behalf. That can make the model simpler for certain existing applications, but it can also weaken actor-level attribution and make it easier to grant the agent more user authority than necessary.
The distinction becomes easier to see when the two models are placed side by side.
| Question | Delegation | Impersonation |
|---|---|---|
| Who owns the original authority? | The user or other principal | The user or other principal |
| Who performs the action? | A separately identifiable AI agent | Agent acts using a context representing the user |
| Is the agent distinguishable downstream? | Can be, when actor context is preserved | May not remain distinguishable in the resulting security context |
| How is authority assigned? | Can be narrowed to the delegated task | May reflect broader subject authority depending on implementation |
| What can the audit trail show? | Principal → Agent → Action | May primarily attribute the action to the subject |
| Can agent access be controlled separately? | Yes, when delegation and agent credentials are independently managed | Depends on how impersonation is implemented |
| Fit for autonomous agent workflows | Useful when agent identity, bounded authority, and attribution need to remain explicit | Requires care when autonomous actions must remain distinguishable from direct user actions |
Delegation: The Agent Acts on Behalf of the User
Suppose a customer asks a shopping agent: “Cancel Order #4821 and request a refund.”
The customer may have permission to view every order, update their profile, manage payment methods, change addresses, and make new purchases. None of those additional permissions are necessary for this task.
A delegated model can preserve the relationship while narrowing the agent's authority: Customer → Shopping Agent → Order #4821 → Cancel + Request Refund
The customer remains the principal whose authority makes the action possible. The shopping agent remains the actor carrying it out. Most importantly, the authorization boundary can be smaller than the customer's full set of permissions.
This gives downstream systems more useful context. Instead of asking only whether the customer is permitted to request refunds, the resource can evaluate whether this agent, acting for this customer, has been delegated authority to perform this refund-related action against this order.
Delegation therefore does not mean copying the user's permissions and handing them to an agent. It means granting an agent a defined portion of authority for an approved purpose.
Impersonation: The Agent Acts as the User
Impersonation changes what the downstream security context represents.
Rather than preserving the agent as a separate actor exercising delegated authority, the resulting context may represent the user as the subject of the request. To the downstream application, the action can therefore look much closer to one performed directly by the user.
Consider the same refund request: Customer → Agent Uses Customer Security Context → Order API
The Order API may be able to establish that the customer is authorized to request a refund. What becomes harder, depending on the implementation, is determining whether the customer personally initiated that API action or whether an autonomous agent selected and executed it on the customer's behalf. That distinction matters once agents can make consequential decisions.
If an audit record simply says “Customer 123 requested refund”, it tells a different story from “Shopping Agent 47 requested refund on behalf of Customer 123 under Delegation 892.”
The second representation preserves both the authority behind the action and the actor that exercised it.
Impersonation is not automatically insecure, nor does every impersonation architecture erase all actor information. Systems can maintain additional logs and controls outside the resulting security context. But relying on impersonation by default becomes harder to justify when an autonomous agent needs narrowly bounded authority, independent lifecycle controls, and clear attribution.
For agent-driven applications, that leads to a useful architectural distinction: Impersonation asks: “Can the agent act as this user?”
Delegation asks: “What has this user authorized this specific agent to do?”
As AI agents gain more autonomy, the second question usually provides the more precise authorization boundary.
How Delegated Authorization Works When an AI Agent Acts on Behalf of a User
Delegated authorization works by keeping three things separate: the principal who owns the authority, the AI agent exercising part of that authority, and the specific permissions granted for the task.
Consider a customer who tells a financial agent: “Pay my electricity bill from Account A.”
The customer may already be authenticated and permitted to make payments. But the agent still needs its own identity and a defined authorization boundary before it can act.
A simplified model looks like this: Authenticate Principal → Identify Agent → Define Delegation → Issue Scoped Access → Request Action → Evaluate Authorization → Execute or Deny → Audit/Revoke
The important part is what survives through that chain. A downstream service should have enough trusted context to determine whose authority is being used, which agent is using it, and whether the requested action falls within the authority that was actually delegated.
1. Authenticate the Principal Behind the Agent
The process starts with the principal whose authority makes the action possible. Depending on the workflow, that might be a customer, employee, application, or another authorized entity.
In our example, the banking application first establishes the customer's identity using its normal authentication controls. Stronger verification may also be required depending on the sensitivity of the action.
But successful authentication answers only: Who is the customer?
It does not answer: What may this AI agent do for that customer?
Those are separate decisions.
2. Identify the AI Agent as a Distinct Actor
The agent should also be identifiable rather than disappearing behind the customer's identity.
Suppose the customer has authorized both a budgeting agent and a bill-payment agent. Even though both operate for the same person, they may have completely different purposes and permissions.
The relationship might look like: Customer 123 → Bill-Payment Agent 17
rather than simply: Customer 123
Keeping the agent distinct gives the authorization system something concrete to govern. Permissions can be assigned, activity attributed, credentials managed, and access revoked for that agent without treating every AI service connected to the customer as the same actor.
3. Define Exactly What Authority Is Being Delegated
This is where the customer's broad authority becomes a narrower agent authorization.
The customer asked the agent to pay an electricity bill from Account A. That does not necessarily authorize the agent to read every account, create new beneficiaries, change security settings, or make unrelated transfers.
The delegation might instead describe:
Principal: Customer 123
Agent: Bill-Payment Agent 17
Resource: Account A
Action: Pay Electricity Bill
Amount: Within Approved Limit
Audience: Payment API
Duration: Current Task
The exact representation depends on the authorization architecture. The principle does not.
Delegation should describe the authority required for the task rather than reproduce the principal's complete permissions.
This preserves the boundary established earlier: Agent Authority ≤ Delegated Authority ≤ Principal Authority
4. Issue Access Scoped to the Agent and Task
Once the delegation is approved, the agent needs a way to present that authority to the appropriate service.
That may involve an access token or another verifiable credential containing or referencing the authorization context needed by downstream systems. Where supported, access can be constrained by scope, resource, audience, lifetime, and other policy conditions.
For example, an agent authorized to use the Payment API should not automatically receive a credential accepted by the Profile API, Administration API, or every other service available to the customer. Credential lifetime matters too.
If the authority exists to complete one payment, a credential that remains broadly usable for weeks creates a much larger security boundary than the original task requires. Short-lived access helps bring credential lifetime closer to delegated-task lifetime.
5. Authorize the Action at the Resource
Possessing a valid token should not be the end of the authorization decision. When the bill-payment agent calls the Payment API, the resource still needs to determine whether the requested operation is permitted under the presented authority.
That decision can consider context such as:Principal + Agent + Delegated Scope + Resource + Action + Audience + Current Policy
Suppose the agent is authorized to pay the electricity provider from Account A, but it attempts to transfer money to a newly created beneficiary instead.
The customer may personally have permission to make that transfer. The agent does not necessarily have delegated authority to do so. The request should therefore be evaluated against the agent's authorization boundary rather than the customer's maximum capability.
Depending on the action, the result may also be more nuanced than a simple allow or deny:
Allow → Restrict → Require Additional Authorization → Require Human Approval → Deny
This is useful when an agent moves from routine actions into something more consequential.
6. Preserve Attribution and End Authority When the Task Ends
Delegation should remain traceable after authorization succeeds. For consequential actions, the system should be able to reconstruct a relationship such as: Customer 123 → Bill-Payment Agent 17 → Delegated Payment Authority → Payment API → Electricity Bill Paid
That is substantially more informative than an audit event that records only: Customer 123 → Payment API
The distinction becomes valuable when investigating unexpected transactions, reviewing permissions, responding to customer disputes, or determining which agent used a particular authorization.
Delegated authority also needs an endpoint. If the payment is completed, the customer revokes consent, the agent is disconnected, its purpose changes, or the agent becomes compromised, its access should be removable without disabling the underlying customer identity.
So delegated authorization is not simply a mechanism for passing user access to an AI agent. It creates a controlled relationship between principal, agent, authority, resource, and action.
The next technical question is how that relationship can be represented as authorization moves between systems. That is where OAuth concepts such as subject, actor, and token exchange become particularly relevant.
OAuth Delegation vs. Impersonation: How Subject, Actor, and Token Exchange Fit AI Agents
The difference between delegation and impersonation becomes more concrete once authorization has to move across APIs and services.
An AI agent may start with authority connected to an authenticated user, call one API, invoke another tool, and eventually request access to a downstream resource. Simply forwarding the user's token through that entire chain is tempting. It is also where identity context can become muddy.
OAuth provides mechanisms that can support a cleaner model. In particular, OAuth 2.0 Token Exchange (RFC 8693) distinguishes between the subject represented by a security token and an actor that may be acting on that subject's behalf.
That distinction maps naturally to AI agent workflows—but there are some details worth getting right.
Subject and Actor Represent Different Parts of the Authorization Relationship
In OAuth token exchange terminology, the subject represents the entity about which the token makes claims. An actor can represent another party acting on behalf of that subject in a delegation scenario.
For an AI agent, the conceptual relationship could be: Subject: Customer 123
Actor: Shopping Agent 47
The customer remains the principal behind the delegated authority, while the agent remains visible as the actor exercising it. That is different from reducing the entire interaction to: Subject: Customer 123
The difference may look small in a token. Operationally, it is significant. Suppose Shopping Agent 47 requests a refund. A downstream Order API may need to know that Customer 123 has authority over the order. But it may also need to know that the request came through Shopping Agent 47 rather than directly from the customer.
Preserving both pieces of context makes policies more expressive: Is Customer 123 allowed to request this refund?
and: Is Shopping Agent 47 allowed to exercise that authority for this order and action?
Those are related authorization questions, but they are not interchangeable.
OAuth Token Exchange Can Carry Authority Into a New Security Context
An agent often should not pass the original user token unchanged to every downstream service.
OAuth 2.0 Token Exchange provides a standardized mechanism for exchanging one security token for another. In an agent workflow, that can support moving from one authorization context to a downstream token intended for a more specific service or interaction.
A conceptual flow could look like: User Authorization → Agent Context → Token Exchange → Scoped Downstream Token → Resource API
For example, a customer may authorize a travel agent to manage one reservation. The agent then needs to call a hotel API.
Instead of sending a broadly usable customer token directly to that API, the authorization infrastructure could issue another token intended for the hotel service and appropriate to the delegated operation. That downstream authority might be constrained around:
Audience: Hotel API
Resource: Reservation #4821
Scope: Reservation Cancellation
Actor: Travel Agent 17
Subject: Customer 123
Lifetime: Limited Execution Window
The exact claims and enforcement model depend on the implementation. The architectural objective is more important: the downstream token should carry only the authority appropriate to the downstream interaction while preserving the identity context needed to evaluate it.
Token Exchange Does Not Automatically Mean Least Privilege
Here’s where implementations can go wrong. Exchanging one token for another does not automatically make the resulting authorization safer or narrower. RFC 8693 provides a token exchange framework; it does not guarantee that every exchanged token will contain less authority than the token presented to obtain it.
That responsibility remains with the authorization system and its policies.
If a broadly privileged user token is exchanged for an equally broad agent token accepted across multiple APIs, little has been gained.
A useful exchange should consider boundaries such as: Scope → Audience → Resource → Actor → Requested Action → Lifetime → Policy
The authorization server needs to decide what authority can legitimately move into the new context. The resource server still needs to enforce the permissions represented by that context.
For AI agents, this distinction matters because delegation should normally preserve or narrow authority, not silently expand it.
Delegation and Impersonation Have Different Token Semantics
RFC 8693 makes an important distinction between the two models. With impersonation, the requesting party is authorized to act as the subject. The resulting token represents the subject without necessarily expressing a separate delegation relationship in the same way.
With delegation, the actor performs an action on behalf of the subject. Actor information can therefore form part of the resulting authorization context.
For agent architectures, that can translate conceptually into: Impersonation
AI Agent → Token Representing User → API
versus: Delegation
User → AI Agent → Token Preserving Subject + Actor Context → API
This does not mean every AI architecture must encode delegation using one particular claim structure. Nor does it mean impersonation is prohibited. What matters is whether the security model preserves enough information for downstream systems to make the authorization decisions the application actually requires.
If the API needs to distinguish a customer's direct action from an autonomous agent action, collapsing both into the same user-only security context can remove useful information.
On-Behalf-of Authorization Is More Than Token Issuance
The phrase “on behalf of” can sound as though the problem is solved once an agent receives a token connected to the user. It isn't.
The authorization system still needs to establish the relationship between the principal and agent, determine what was delegated, constrain the resulting access, and give the resource enough context to enforce that boundary.
A stronger model looks more like: Authenticated Principal → Identified Agent → Delegated Authority → Scoped Token → Resource Authorization → Action
Notice where the final decision occurs. A valid token establishes trusted authorization context, but the resource still needs to determine whether the requested action is allowed.
This becomes especially important when an agent moves between operations. Reading an account balance, changing a shipping address, issuing a refund, and transferring money may all happen within the same broader workflow, but they should not automatically inherit the same authorization decision.
OAuth can provide important building blocks for these relationships. It does not remove the need for agent-aware authorization policy.
Delegation is also only one part of securing agent access. AI agents still need to authenticate as identifiable actors before systems can determine what they are authorized to do. For the broader architecture covering agent authentication, authorization, credentials, tokens, and access to APIs and tools, see our guide on Auth for AI Agents.
And once one agent begins handing work to another, the problem gets harder again. The system now has to preserve authority across multiple actors without allowing each delegation hop to acquire more permission than the one before it.
How Agent-to-Agent Delegation Prevents Authority From Expanding Across AI Workflows
Delegation becomes harder when the AI agent receiving authority is not the agent that ultimately completes the task.
A customer might authorize one travel agent to plan a trip. That agent could then call a flight agent, a hotel agent, and eventually a payment agent. Each agent performs only part of the workflow, but all of them are connected to authority that originally came from the customer.
The chain may look like: Customer → Travel Agent → Flight Agent → Payment Agent → Payment API
The dangerous shortcut is to let each downstream agent inherit whatever authority the previous actor possesses. Permissions can gradually become detached from the task that originally justified them.
Agent-to-agent delegation needs a tighter rule: Delegated Authority ≤ Delegating Authority
An agent should never be able to delegate authority it does not have. And simply possessing a permission does not mean the agent should pass that entire permission set downstream.
Each Agent Should Receive Only the Authority Its Subtask Requires
Suppose a customer tells a travel agent: “Book the cheapest direct flight to Singapore under ₹40,000.”
The travel agent may need to search flights and coordinate the booking. It asks a specialized flight agent to find eligible options and later invokes a payment agent once the booking has been approved. Those agents do not need identical permissions.
The authorization chain might instead become: Customer → Travel Agent: Plan + Book Approved Trip
Travel Agent → Flight Agent: Search Eligible Flights
Travel Agent → Payment Agent: Pay Approved Merchant Up to ₹40,000
The flight agent does not need payment authority. The payment agent does not need access to the customer's complete travel history. Neither agent should automatically inherit every capability available to the coordinating travel agent.
Here’s where teams can get into trouble: delegation is not permission forwarding.
Each handoff should create an authorization boundary around the downstream task.
Authority Should Narrow, Not Grow, Across Delegation Hops
Consider the full relationship: Principal Authority → Agent A Authority → Agent B Authority → Tool/API Authority
The permissions available at the end of that chain should not become broader simply because another actor was introduced.
If Agent A can only read a customer's travel preferences, Agent A should not be able to authorize Agent B to modify the customer's payment method.
Likewise, if Agent A has both search and booking permissions but delegates only flight search to Agent B, the fact that Agent A possesses booking authority does not mean Agent B should receive it. The constraint can be expressed as: Agent B Authority ≤ Authority Delegated by Agent A ≤ Agent A Authority ≤ Principal Authority
That matters in dynamic agent systems because the delegating agent may decide at runtime which specialized agent or tool to invoke. The authorization system cannot assume that every downstream actor deserves the complete security context available upstream.
Instead, it needs to evaluate what authority is allowed to move across each boundary.
The Delegation Chain Should Preserve Who Authorized Whom
Least privilege solves only part of the problem. Multi-agent systems also need attribution.
Suppose a payment API receives a request from Payment Agent C. Knowing that Agent C has a valid credential may not be enough to explain why it is authorized to spend a customer's money.
The relevant context may be: Customer 123 → Authorized Travel Agent A → Delegated Payment Task to Agent C → Payment API → ₹32,000 Airline Payment
That chain helps answer several different questions:
Who originally authorized the workflow?
Which agent received that authority?
Which agent delegated the downstream task?
What authority was passed?
Which agent ultimately performed the action?
Without that context, an audit trail can show the final API caller while losing the authorization relationship that allowed the caller to act.
This does not require recording an agent's private reasoning or every internal planning step. The important record is the identity and authorization chain behind consequential actions.
Downstream Agents Should Not Reuse Upstream Credentials by Default
Passing the same credential from agent to agent can undermine these boundaries.
Imagine Agent A receives a token accepted by several APIs and simply forwards it to Agent B. Agent B may now be able to use every permission and resource available through that token—even though it was delegated only one narrow subtask.
It can also become harder for the receiving API to distinguish:
Agent A using its authority
from:
Agent B using Agent A's credential
A stronger design gives the downstream agent authorization appropriate to its own identity and task. Conceptually: Agent A Authority → Delegation Evaluation → Agent B Scoped Authority → Target Resource
OAuth token exchange can support this kind of downstream token issuance in architectures that use it, but the security property comes from the authorization policy around the exchange. The resulting token still needs appropriate scope, audience, resource, lifetime, and actor context.
A token being “exchanged” does not automatically mean authority was safely reduced.
Agent-to-Tool Calls Need the Same Delegation Discipline
The downstream actor does not always have to be another AI agent.
Agent A may invoke an MCP server, payment service, database interface, search tool, or business API. From an authorization perspective, the same question remains: What authority should cross this boundary?
If a customer authorizes an agent to retrieve one order, connecting that agent to a tool capable of reading the entire customer database should not silently turn narrow delegation into broad data access.
The tool or resource should receive authorization appropriate to the operation being requested: Customer → Agent → Delegated Order Access → Tool/API → Order #4821
rather than: Customer → Agent → Broad Customer Credential → Every Connected Tool
The more tools and agents involved, the more valuable these boundaries become. Multi-agent systems therefore need more than authenticated agents. They need constrained delegation between authenticated actors.
Every handoff should preserve enough context to establish where the authority came from, narrow that authority to the downstream task, and keep the resulting action attributable.
Otherwise, a workflow that begins with one tightly defined user instruction can end several hops later with an agent holding permissions the user never intended to delegate.
Security Risks When AI Agents Impersonate Users or Receive Too Much Delegated Authority
The security problem with impersonation is not simply that an AI agent can act as a user. In some architectures, that behavior may be intentional. The bigger concern is what happens when the agent receives broad user authority, downstream systems cannot distinguish the actor, or delegated access remains usable beyond its original purpose.
Autonomy makes those weaknesses more consequential. A human usually performs sensitive actions individually. An agent can call several tools, move between resources, and trigger downstream operations while pursuing one instruction.
Four risks deserve particular attention.
Excessive Privilege Can Turn a Narrow Task Into Broad Account Access
The most immediate risk is over-authorizing the agent. Suppose a customer asks an AI support agent: “Check whether Order #4821 has shipped.”
The agent needs read access to one relevant order. If it instead operates using the customer's broader account permissions, it might technically be able to view other orders, change delivery information, cancel purchases, modify profile data, or initiate refunds.
None of those permissions came from the customer's actual request. The problem becomes: Narrow User Intent → Broad User Authority → Agent Receives Broad Authority
Delegation gives the application an opportunity to break that chain: Narrow User Intent → Scoped Delegation → Agent Receives Task-Relevant Authority
This is why least privilege for AI agents should not stop at asking whether the underlying user has permission. The authorization system also needs to determine how much of that permission the agent should be allowed to exercise. The constraint remains: Agent Authority ≤ Delegated Authority ≤ Principal Authority
An authenticated user may be capable of doing many things. An agent completing one task usually should not be.
Lost Actor Attribution Makes Human and Agent Actions Harder to Distinguish
Impersonation can also create ambiguity in audit records when downstream systems primarily see the user identity.
Consider two events: Customer 123 → Changed Shipping Address
and: Customer 123 → Shopping Agent 47 → Changed Shipping Address
Both may ultimately depend on Customer 123's authority. But they describe different events. In the second case, an autonomous actor selected and executed the action.
That distinction matters when a customer disputes an operation, an agent behaves unexpectedly, a security team investigates an incident, or administrators need to determine which agent should lose access.
The problem gets worse when several agents act for the same principal. If all of them appear downstream as the same customer, service account, or shared application identity, technically valid requests can become difficult to attribute to the actor that generated them.
For consequential actions, audit context should preserve enough of the chain to answer: Who was the principal? → Which agent acted? → What authority was delegated? → Which resource was accessed? → What action occurred?
A valid user identity is not a substitute for actor attribution.
Confused Deputy and Token Misuse Can Break the Intended Authorization Boundary
An agent or intermediary can have legitimate access and still use that access in a way the original principal never intended.
This is particularly relevant when agents invoke tools, APIs, MCP servers, or other agents. A downstream component may possess privileges of its own, accept credentials intended for another resource, or receive more authorization context than the task requires.
Suppose an agent has a token intended for Service A and passes it to an intermediary that subsequently presents the same token to Service B. If audience and resource restrictions are weak—or the receiving service does not enforce them correctly—the credential may travel farther than its original authorization boundary.
The same problem can occur when one broadly scoped user credential is passed repeatedly through an agent workflow.
A safer pattern constrains access around the intended destination and action: Agent → Resource-Specific Authority → Intended API
rather than: Agent → Reusable User Authority → Multiple Tools and APIs
Audience validation, resource-specific authorization, scoped access, appropriate token lifetimes, and explicit policy checks all help maintain that separation.
The important question is not merely: “Is this token valid?”
It is: “Is this actor authorized to use this authority for this resource and this action?”
Weak Revocation Boundaries Can Leave Agent Authority Active Too Long
Agent access also needs to end cleanly. Imagine a customer connects a personal finance agent to analyze spending for the next 30 days. Later, the customer disconnects that agent.
If the agent operates through independent, delegated access, the relationship can be revoked: Customer → Finance Agent → Revoked
without necessarily affecting: Customer → Banking Application → Active
or: Customer → Another Approved Agent → Active
The situation becomes harder when the agent depends on reusable user credentials or an impersonation mechanism tightly coupled to the customer's broader access. Removing the agent may then require additional application-specific controls, invalidating credentials, or disrupting access that was never meant to be revoked.
Long-lived tokens make the problem worse. An agent may finish its assigned task while the credential it received remains technically usable.
Agent authorization should therefore have a lifecycle of its own. Access should be revocable when the task ends, consent changes, the agent is disconnected, its ownership changes, or the agent becomes compromised.
This is another reason delegation should represent a bounded authorization relationship, not merely a convenient way to pass user access to autonomous software.
None of these risks means impersonation must never be used. The more useful question is whether the architecture actually needs impersonation and whether it can preserve the authorization boundaries, attribution, and revocation controls required for autonomous actions.
There are cases where the answer may still be yes.
When Is Impersonation Appropriate for AI Agents—and When Should You Avoid It?
Delegation gives AI systems a cleaner way to preserve the relationship between the principal and the agent. That does not make impersonation inherently wrong.
There are architectures where a service legitimately needs to operate using a security context representing the user. Existing enterprise applications may already depend on that model, and some downstream systems may have no concept of a separate agent identity or delegated actor.
The problem starts when impersonation becomes the easiest way to give an autonomous agent access—and nobody asks what identity or authorization context gets lost along the way.
A useful decision point is simple: does the downstream system need to know that an AI agent, rather than the user directly, performed this action?
For many agent-driven workflows, the answer will matter.
Impersonation Can Make Sense in Tightly Controlled Environments
Some applications were designed around user-centric authorization. Their APIs expect a user identity, permissions are evaluated against that identity, and the surrounding infrastructure may not support a separate actor or delegation context.
An agent operating inside a tightly controlled application could therefore need to use an impersonated user context to interact with an existing service.
That can be reasonable when the trust boundary is narrow, the available actions are constrained, the agent is controlled by the same organization, and additional controls preserve enough information to identify the actual actor.
For example: Authenticated Employee → Approved Internal Agent → Legacy Application
If the legacy application understands only employee identities, introducing full delegation support may require architectural changes that cannot happen immediately.
Impersonation can serve as a compatibility mechanism in such cases. But compatibility should not be confused with least privilege.
The surrounding system still needs to control which agents may impersonate which users, what actions they can perform, how long that authority remains valid, and how agent activity is recorded.
Read-Only and Low-Consequence Workflows May Carry Less Risk
The consequences of impersonation also depend on what the agent can actually do.
An internal agent retrieving a user's calendar availability presents a different authorization problem from an agent transferring funds, changing account credentials, deleting data, or approving a purchase.
If the available resource is narrowly constrained and the operation is read-only or otherwise low consequence, the additional complexity of a full delegation architecture may not always be justified.
But the label “low risk” should come from the permitted operation and resource—not simply from the fact that an AI assistant is involved.
A read-only token accepted across an entire customer database, for example, can still expose far more information than one agent task requires.
Impersonation Becomes Harder to Justify as Agent Autonomy Increases
Here’s where the architectural trade-off changes. An agent that retrieves one predefined resource is relatively predictable. An agent that can choose tools, sequence actions, modify data, initiate transactions, or delegate tasks downstream has considerably more room to exercise authority.
As that autonomy increases, preserving the distinction between principal and actor becomes more valuable.
Consider: User → Agent → Read Account Balance
versus: User → Agent → Select Tool → Create Beneficiary → Transfer Funds → Notify User
The second workflow contains several independently consequential actions. Representing all of them simply as actions by the user can make authorization and attribution unnecessarily coarse.
This is where delegation provides a stronger model: Principal → Identified Agent → Delegated Authority → Specific Action
The system can constrain what the agent is permitted to do without denying that the underlying authority originated with the user.
Avoid Impersonation When It Gives the Agent the User’s Full Authority
One warning sign is an architecture where the implementation logic effectively becomes: User Can Do X → Therefore Agent Can Do X
That skips the delegation decision entirely. A user may be authorized to manage every part of an account because it is their account. An AI agent has no equivalent reason to inherit all of those permissions merely because the user asked it to complete one task.
For consequential agent workflows, there should usually be another question: User Can Do X → Did the User Authorize This Agent to Do X?
And sometimes another: Is This Agent Allowed to Exercise X Against This Resource Under Current Policy?
Those additional boundaries are what prevent user authorization from becoming unrestricted agent authority.
Avoid Impersonation When Actor-Level Auditability Matters
Impersonation also becomes a poor fit when the organization needs to distinguish direct human activity from autonomous execution.
Financial transactions, customer account changes, administrative actions, production deployments, healthcare workflows, and other sensitive operations may require stronger attribution. An audit trail that records: User 123 performed Action X
may be incomplete if the actual event was: Agent 47 performed Action X using authority granted by User 123
Even if the application must use impersonation for compatibility, the surrounding architecture should preserve the agent identity and authorization relationship through additional trusted audit context.
Otherwise, operational teams may know whose authority was exercised without knowing which autonomous actor exercised it.
Treat Impersonation as a Deliberate Trust Decision, Not a Shortcut
The choice between delegation and impersonation does not need to be ideological.
Impersonation may be practical for a controlled legacy integration or narrowly constrained workflow. Delegation becomes increasingly useful when agents require independent identities, narrower permissions, downstream authorization, agent-to-agent interactions, separate revocation, or clear actor attribution.
A practical decision framework is:
| Requirement | Delegation Is Usually Better Suited | Impersonation May Be Practical |
|---|---|---|
| Distinguish user from agent | Yes | Requires separate supporting context |
| Give agent less authority than user | Strong fit | Requires additional enforcement |
| Agent performs consequential actions | Strong fit | Requires careful controls |
| Agent delegates to other agents/tools | Strong fit | Becomes harder to preserve authority boundaries |
| Independent agent revocation | Strong fit | Depends on implementation |
| Legacy system accepts only user context | May require integration changes | Can provide compatibility |
| Narrow, controlled operation | Still applicable | May be sufficient |
| Detailed principal-to-actor attribution | Naturally supports the model | Requires additional audit mechanisms |
The deciding question should not be “Can we make the agent look like the user?”
It should be: “What identity and authority does the downstream resource actually need to make the correct authorization decision?”
If the resource needs to know both whose authority is behind the request and which autonomous actor is exercising it, preserving delegation context is usually the cleaner architecture.
If impersonation is necessary, keep the boundary narrow, make the decision explicit, and preserve the agent's identity somewhere trustworthy. Convenience alone is not a sufficient authorization model.
Best Practices for Secure AI Agent Delegation and Least-Privilege Access
Secure delegation depends on maintaining a boundary between what the principal can do and what an AI agent has actually been authorized to do.
That sounds straightforward. It gets harder once agents call multiple APIs, invoke tools, retain tokens, or delegate parts of a task to other agents. A permission that starts narrowly can become surprisingly broad after several handoffs if each system simply trusts the credential it receives.
The practical goal is to preserve identity, authority, and attribution from the original principal through the final action.
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Give every AI agent a distinct, verifiable identity
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Separate principal authority from agent authority
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Scope delegation to the task, resource, and action
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Prefer short-lived and audience-restricted access
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Preserve context (principal and actor) where authorization requires both
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Re-authorize when agent crosses a consequential boundary
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Constrain every agent-to-agent delegation
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Keep Delegation auditable and independently revocable
How LoginRadius Supports Delegated Authorization Without Making the AI Agent the User
An AI agent acting for a customer needs access to complete the approved task. It does not need to become indistinguishable from that customer.
LoginRadius approaches this by keeping the user identity, agent identity, and delegated authority connected but separate. AI agents can be managed as distinct identities, receive scoped access for authorized actions, and remain attributable when they interact with APIs, tools, MCP servers, or other protected resources.
The authorization relationship can therefore remain explicit: Verified User → Identified AI Agent → Delegated Authority → Scoped Access → Policy Decision → Resource/Action
That separation makes it possible to answer both sides of an agent-driven request: whose authority is behind the action, and which agent is actually exercising it?
Keep the User and AI Agent as Distinct Identities
LoginRadius supports unique, verifiable identities for AI agents independently of users and applications.
This avoids a model where the agent simply reuses the customer's identity as its own. The customer remains the principal behind the interaction, while the agent can be registered and governed as a separate actor with its own lifecycle.
That distinction also creates an independent control point. If an agent is compromised, retired, or no longer authorized, its access can be revoked or changed without necessarily removing access from the underlying user or other agents.
For delegation, this separation is fundamental: User Identity ≠ Agent Identity
Instead: User Identity → Delegated Authority → Agent Identity
Issue Scoped Access for Agents Acting on Behalf of Users
When an AI agent acts for a user, LoginRadius supports delegated OAuth-based access rather than requiring the agent to inherit or reuse the user's credentials.
The resulting access can represent the relevant user and agent context while being constrained by authorized scopes and expiration. LoginRadius also supports centralized token issuance, refresh, rotation, and revocation for agent access.
This changes the model from: User Has Permission → Agent Gets User Credentials
to: User Has Authority → Defined Authority Is Delegated → Agent Receives Scoped Access
Short-lived, scope-bound tokens also help prevent a temporary agent task from creating unnecessarily persistent access.
Constrain What Each Agent Can Actually Do
A distinct identity is useful only if authorization can treat that identity differently.
LoginRadius supports fine-grained authorization with per-agent and per-action permissions, allowing policies to constrain access according to the agent and the operation it is attempting.
Suppose a customer has broad account access but authorizes an agent only to retrieve an order.
The desired boundary is: Customer → Agent → Read Order #4821
not: Customer → Agent → Full Customer Account
The same principle can extend to MCP environments, where agents receive scoped access to specific tools and APIs rather than unrestricted access to every capability exposed by the server.
This preserves the central rule of delegated authorization: Agent Authority ≤ Delegated Authority ≤ Principal Authority
Preserve Delegation Context Across Downstream Services
Agent workflows rarely stop at one API.
An agent may need to call another service, invoke a tool, or pass part of a task downstream. LoginRadius supports OAuth 2.0 Token Exchange compatibility for these scenarios, allowing authorization context to move into downstream access without relying on the original credential being passed everywhere unchanged.
A conceptual relationship might look like: User → Agent A → Scoped Delegation → Downstream Token → API
The authorization boundary still matters. Token exchange should be paired with appropriate scope, audience, resource, and policy constraints rather than assuming an exchanged token is automatically least-privileged.
This gives organizations a standards-based way to preserve delegated access as agent workflows cross system boundaries.
Require Human Approval When Delegated Authority Is Not Enough
Delegation does not mean the agent must be allowed to complete every action autonomously.
A workflow may begin inside an approved boundary and later reach a more consequential action. LoginRadius supports human approval for sensitive agent operations, allowing execution to pause while a person reviews and approves or rejects the action.
For example:
Read Order Status → Allow
Request Standard Refund → Evaluate Policy
High-Value Refund → Require Human Approval
Once approved, the agent can continue with the authorization required for the permitted action.
This provides another boundary between what the user generally can do and what the agent may autonomously do without renewed human involvement.
Keep Agent Actions Traceable and Access Revocable
Delegation loses much of its value if the final audit trail collapses everything back into the user identity. LoginRadius provides visibility into agent actions, token usage, and delegation chains so organizations can preserve context around which agent acted and on whose behalf.
For an agent-driven operation, the useful audit relationship becomes: User → Agent → Delegated Authority → Action → Outcome
rather than simply: User → Action
That distinction helps when investigating unexpected behavior, reviewing agent permissions, or determining which authorization relationship needs to be revoked.
And revocation can remain targeted. If Agent A should no longer operate for the customer, its credentials or delegated access can be revoked without treating the customer's identity itself as compromised.
The result is an authorization model where the agent can act for the user without needing to become the user.
That is the important architectural shift: preserve the human principal, preserve the autonomous actor, and keep the authority connecting them narrow enough to govern.
Conclusion: Let AI Agents Act for Users Without Becoming the User
AI agents need authority to get useful work done. But giving an agent authority does not mean giving it the user's identity or every permission the user possesses.
That distinction becomes critical once agents can call APIs, invoke tools, initiate transactions, modify data, or delegate work to other agents. The authorization model needs to preserve both sides of the relationship: the principal who owns the authority and the agent actually exercising it.
Delegation provides that separation. It allows access to be narrowed around a specific agent, task, resource, action, and time period while keeping the resulting activity attributable. Impersonation can still have a place in controlled or legacy architectures, but it should be an intentional design decision rather than the default shortcut for agent access.
The principle is simple: Agent Authority ≤ Delegated Authority ≤ Principal Authority
And when authority moves downstream: Delegated Authority ≤ Delegating Authority
As AI agents become capable of taking more consequential actions, these boundaries become part of the security architecture—not an implementation detail.
LoginRadius helps businesses govern AI agents as distinct identities with scoped and delegated access, fine-grained authorization, human approval for sensitive actions, lifecycle controls, and traceable agent activity.
Don't make your AI agents become the user just to get access. Give them an identity and exactly the authority they need. Explore LoginRadius Agentic IAM or book a demo to build delegated AI agent access around your applications, APIs, and tools.
FAQs
Q: What Is AI Agent Delegation?
A: AI agent delegation allows a user or another principal to grant an identified AI agent limited authority to perform specific actions on their behalf. The agent acts for the principal without automatically inheriting the principal’s complete identity or permissions.
Q: What Is the Difference Between Delegation and Impersonation for AI Agents?
A: With delegation, the principal and AI agent can remain distinguishable while the agent exercises defined authority. With impersonation, the agent operates using a security context that represents the user as the subject, which may make actor-level attribution harder depending on the implementation.
Q: Should an AI Agent Use the User’s Access Token?
A: An AI agent should not automatically reuse a broadly scoped user access token. Where the architecture supports it, give the agent access that is appropriately scoped to its identity, task, resource, audience, and required actions.
Q: What Does “On Behalf Of” Mean for an AI Agent?
A: “On behalf of” means the agent exercises authority that originates with a user or another principal while remaining a distinct actor. The agent should receive only the portion of that authority required for the authorized task.
Q: How Does OAuth Token Exchange Support AI Agent Delegation?
A: OAuth 2.0 Token Exchange provides a standardized way to exchange one security token for another and can represent delegation between a subject and an actor. The authorization system must still determine and enforce the scope of authority granted to the agent.
Q: What Is the Difference Between Subject and Actor in OAuth Delegation?
A: The subject represents the entity whose authority or identity is represented by the token, while an actor can identify another entity acting on the subject’s behalf. For example, the customer may be the subject while an AI shopping agent is the actor.
Q: Can One AI Agent Delegate Authority to Another AI Agent?
A: Yes, an agent can delegate part of its authority downstream when the authorization architecture permits it. The downstream agent should never receive more authority than the delegating agent is permitted to pass: Delegated Authority ≤ Delegating Authority.
Q: How Does Least Privilege Apply to AI Agent Delegation?
A: Least privilege means giving an AI agent only the authority necessary for its approved task rather than copying the user’s complete permissions. A useful boundary is: Agent Authority ≤ Delegated Authority ≤ Principal Authority.
