AI Agent Identity vs. User Identity: Which One Should Control Your Next Tool Call?

Your AI agent is acting on behalf of a user. That does not make the agent the user.

This distinction determines whether your next tool call is narrowly authorized or operating with an inherited set of permissions that was never designed for autonomous software.

A user identity answers who requested the work. An agent identity answers which software is performing it. Delegated authority explains what the user allowed the agent to do. Operation-level authorization decides whether this exact action can execute now.

These are different questions. Your security model must answer all four.

Agents should not simply inherit a user’s permissions. They should operate with a distinct identity, a bounded delegation, and a runtime decision for every consequential action.

Why user permissions are not agent authorization

A human user may have broad access because their job requires it. A finance director may be able to view invoices, approve payments, modify vendor records, and access financial reports. That access reflects the full scope of the person’s role.

An agent performing one task does not require the full scope of that role.

If the user asks an agent to prepare a payment report, the agent may need read access to selected invoice and vendor data. It does not automatically need permission to approve the payment, change the vendor’s banking details, or delete the underlying records.

The distinction matters:

Question	User identity	Agent authorization
Who is involved?	The human requester	The executing software
What does the identity represent?	A business role and responsibility	A workload, agent, or runtime
What access is relevant?	The user’s available permissions	The minimum scope for this task
When is the decision made?	Often at login or session creation	Before each sensitive operation
What must the evidence show?	Who requested the work	Who requested it, which agent acted, and what it was allowed to do

A user’s permissions can establish an upper boundary. They do not provide sufficient authorization for every action an agent might attempt.

User identity is context. It is not the final decision.

The four identities and authorities you must separate

1. User identity: the requesting principal

The user identity identifies the person or upstream system that initiates a task.

It answers:

• Who asked for the work?
• Which business role does that person hold?
• What responsibility does that person carry?
• What authority can the user delegate?

A user identity is essential for accountability. Without it, an audit record may show that an agent performed an action without showing who authorized the task.

But the user identity does not explain what the agent may do across every connected tool.

2. Agent identity: the executing actor

The agent identity identifies the software performing the work.

It should have its own:

• Machine or workload identity
• Lifecycle and owner
• Credential and expiration policy
• Tool inventory
• Runtime telemetry
• Policy bindings
• Audit trail

An agent identity prevents a common failure mode: a system log records “Jane Doe updated the customer record” when Jane Doe never performed the action. The agent did.

The record must distinguish Jane Doe requested the task from Agent A executed the operation.

This is a central requirement for AI agent security. The agent is not a transparent extension of the user. It is a separate actor operating within delegated boundaries.

3. Delegated authority: the permitted assignment

Delegated authority binds the user and the agent for a defined task.

It should identify:

• The delegating user
• The authorized agent
• The task or purpose
• The permitted tools
• The allowed operations
• The time limit
• Any value or data boundaries

In plain English, delegation means: “This agent may perform this class of work for this user within these limits.”

Delegation is not a blank cheque. A token that says an agent is acting on behalf of a user still requires a narrow scope.

4. Operation-level authorization: the decision at execution

Operation-level authorization evaluates the exact action before it executes.

The decision includes the:

• User identity
• Agent identity
• Tool or MCP server
• Requested operation
• Input parameters
• Data sensitivity
• Business purpose
• Current policy
• Segregation of Duties (SoD) status
• Approval requirement

This is the point at which AI agent authorization becomes operational.

A connection to a customer relationship management system does not authorize every CRM operation. Access to an MCP server does not authorize every tool exposed by that server. A valid token does not make an unsafe action acceptable.

Connection-level access is not operation-level authorization.

What should control the next MCP tool call?

The answer is neither the user identity alone nor the agent identity alone.

The next MCP or tool call should be controlled by a runtime policy decision that evaluates the combined context:

Agent A is acting for User B, using Tool C, to perform Operation D, for Purpose E, within Scope F.

That decision must happen before the tool executes.

Consider a procurement workflow:

1. A procurement manager asks an agent to prepare a purchase order.
2. The agent reads approved vendor and pricing data.
3. The agent creates a draft purchase order.
4. The agent attempts to submit the order for approval.
5. The agent attempts to change the vendor’s payment details.

These are not one action. They are three different risk levels:

• Read approved data: automatically allow.
• Create a draft: allow within the delegated scope.
• Submit or modify sensitive records: evaluate against SoD, value thresholds, and approval rules.

A secure runtime control does not block every action. It allows safe operations automatically, escalates exceptional operations, and blocks actions outside the permitted scope.

Not every action needs a human. Every action needs a decision.

Why least privilege must operate at the tool-call level

Traditional least privilege often stops at the account, role, or connection. Agentic systems require a narrower boundary.

Least privilege must apply to:

1. The user: what the person is allowed to request.
2. The agent: what the software is allowed to perform.
3. The operation: what this specific call may execute.

This is especially important when an agent can reach multiple tools through Model Context Protocol (MCP). A single agent may have access to a CRM, ticketing platform, file store, payment system, and identity directory.

The fact that the agent can reach those systems does not mean it should use every available operation.

A least-privileged policy separates:

• read_customer_record from delete_customer_record
• create_invoice from approve_invoice
• draft_change from execute_change
• read_payroll_data from export_payroll_data
• search_repository from modify_production_code

The scope must describe the operation, not merely the destination.

A broad permission creates possibility. A narrow operation scope creates control.

Where segregation of duties changes the decision

Segregation of Duties prevents one identity or workflow from completing conflicting steps in the same business process.

An agent may prepare a payment but should not approve its own payment. It may create a vendor record but should not approve changes to that vendor’s banking information. It may recommend an access change but should not grant the access and validate the result.

The control must evaluate the relationship between actions, not only the current request.

A runtime SoD decision asks:

• Did this agent create the transaction?
• Is the current agent attempting to approve it?
• Did the requesting user already perform a conflicting step?
• Does the operation cross a financial, administrative, or regulatory boundary?
• Is a named human approver required?

If the action violates SoD, the system should BLOCK or ESCALATE before execution. A post-event alert does not restore separation after the conflicting action has already completed.

This is the difference between detection and prevention.

When should a human approve the action?

Human approval belongs at defined risk boundaries, not as a universal interruption.

Route an action to a named approver when it:

• Exceeds a transaction-value threshold
• Modifies a system of record
• Changes payment or identity data
• Crosses a SoD boundary
• Accesses regulated or highly sensitive data
• Creates a large operational blast radius
• Falls outside the agent’s normal behavior or delegated purpose

The approval request should contain the full decision context:

• User who delegated the task
• Agent that attempted the action
• Tool and operation
• Requested parameters
• Policy that triggered escalation
• Business reason
• Timestamp
• Evidence from prior steps

The approver must decide whether to allow or deny the held action. The agent must not execute while approval is pending.

A human-in-the-loop control is real only when the action remains on hold.

Why deterministic pre-execution enforcement matters

Monitoring tells you what happened. It does not necessarily stop the next action.

A model-based classifier may estimate that a tool call appears safe. An alerting system may identify unusual activity after execution. A log may show that an agent invoked a sensitive API.

None of these mechanisms provides deterministic authorization by itself.

Runtime AI security requires a control point that evaluates the action before the tool call reaches the target system. The policy returns a clear outcome:

• ALLOW: execute automatically.
• ESCALATE: hold and route to a named approver.
• BLOCK: do not execute.

LangGuard Arbiter applies this decision model at runtime. It evaluates the agent action, policy, identity, scope, and context before execution. LangGuard SCOPE-MCP provides the design-time foundation by mapping the agent’s connected tools, exposed operations, and reachable systems of record.

The sequence is deliberate:

1. Map the action surface.
2. Evaluate the requested operation.
3. Enforce the decision before execution.
4. Record the evidence for replay and audit.

Design-time mapping without runtime enforcement leaves the action exposed. Runtime enforcement without a complete action map leaves unknown paths outside policy.

What evidence should the audit record contain?

A log line says that a tool was called. Evidence explains whether the call was authorized.

For each consequential action, record:

• User identity
• Agent identity
• Delegation scope
• Tool or MCP server
• Exact operation and parameters
• Policy evaluated
• Decision returned
• SoD result
• Human approver, if required
• Timestamp
• Correlation or transaction ID
• Execution result

This record answers the questions an auditor, investigator, or risk owner will ask:

• Who authorized the action?
• Which agent acted?
• What was the agent capable of doing?
• Which policy applied?
• Was the action allowed before execution?
• Was a human actually involved?
• Can the decision be reconstructed?

A tamper-evident, replay-ready ledger turns a fragmented trail into a chain of custody.

Not a log line. Evidence.

The control model is simple: identity, scope, decision

Your next tool call should pass through three layers:

Identity

Identify the user who requested the work and the agent that performs it.

Scope

Define the delegated purpose, tool access, operation, data boundary, time limit, and value threshold.

Decision

Evaluate the exact call at runtime and return ALLOW, ESCALATE, or BLOCK before execution.

This model works with existing identity providers, agent runtimes, MCP servers, APIs, and business systems. It adds a control layer above those systems without pretending that authentication alone provides governance.

For builders, it exposes the agent’s action surface. For security teams, it enforces policy before execution. For operations, it provides traceability across tools. For audit and compliance, it produces named accountability and replay-ready evidence.

The question is not whether your agent has a user token.

The question is whether you can identify the user, the agent, the delegated authority, the exact operation, the policy decision, and the responsible approver before the action executes.

If you cannot, the agent is not operating under controlled authority. It is borrowing access.

Learn how LangGuard SCOPE-MCP and Arbiter govern the action layer for agentic systems.

Read more about identity drift in AI agents.

See why MCP servers must be treated as runtime authorities.