AI Agent Identity Management: Securing Credentials, OAuth and Tool Access

Non-human identities already outnumber human identities by 50 to 1 in the average enterprise, according to Okta's identity research. AI agents make that imbalance harder to manage because they do not just authenticate. They choose tools, act asynchronously, and sometimes delegate work to other agents.

Imagine a support agent that reads a CRM record, checks a billing system, drafts a refund, and sends a customer message. Which identity performed each step? Who authorized it? Which credential did it use? Could you revoke that authority without disabling an entire team?

AI agent identity management answers those questions. This guide gives you a production model for distinct agent identities, delegated authority, short-lived credentials, OAuth, tool permissions, audit trails, and revocation.

What is AI agent identity management?

AI agent identity management is the set of policies, systems, and operational controls used to identify an agent, authenticate it, delegate authority to it, authorize its access, manage its credentials, and attribute every action to an accountable owner.

A useful implementation separates three roles:

• Actor: the agent that performs an action.
• Delegating principal: the user or service that authorized the task.
• Owner: the person or team accountable for the deployed agent.

These records should remain distinct even when the agent acts on a user's behalf. If every event appears to come directly from the user, you lose the ability to tell whether the user clicked a button, an agent followed an approved task, or a compromised workflow misused a token.

Agent identity is not just a service account

Established workload identity patterns are a strong foundation, but agents introduce unusual behavior. They choose actions dynamically, operate after a human session ends, call different tools based on context, and may hand a task to a sub-agent. Their required permissions can change within one run.

That does not mean enterprises need an entirely new identity stack. It means existing IAM and OAuth controls must preserve the agent, delegating principal, purpose, and runtime context instead of reducing everything to one API key.

A production agent needs its own identity without erasing the identity of the person or service that authorized it.

Why traditional IAM assumptions break down

Traditional IAM often assumes that permissions map to a stable job and a login begins a predictable session. Tool-using agents break both assumptions.

The confused deputy problem is especially important. An agent may have legitimate access to customer records and billing actions, yet a malicious document or poorly scoped instruction can induce it to use that access for the wrong purpose. Authentication proves who called. It does not prove the call still matches the delegated task.

Multi-agent delegation adds another rule: authority must narrow, never expand. A research sub-agent should receive only the read permissions required for research, not every permission held by the parent. The delegation chain should remain traceable, and expiry should propagate wherever the infrastructure supports it.

For the wider threat model, including prompt injection and data exfiltration, read our guide to AI agent security. This article stays focused on identity, credentials, delegation, and access.

The identity chain behind every action

The NIST AI Agent Standards Initiative identifies agent authentication and identity infrastructure as core building blocks for secure agent ecosystems. In practice, every consequential tool call should connect five separate records:

1. Human or service principal: who initiated or authorized the job.
2. Agent identity: which deployed agent or workload acted.
3. Agent owner: who is accountable for its purpose and access.
4. Credential or grant: what proved its authority.
5. Execution record: what connects the run, tool, action, policy decision, and result.

The chain looks like this:

User or service
  -> explicit delegation
  -> agent identity
  -> scoped, short-lived credential
  -> tool or API
  -> structured audit event

Do not collapse authentication, authorization, and governance:

This boundary prevents category errors. An identity provider can issue a valid token but cannot decide whether issuing a refund matches the current support ticket. An operations layer can govern the run but should not pretend to be the system that cryptographically establishes identity.

Credential patterns for AI agents

Keep raw credentials out of model context

Never place secrets in system prompts, memory, retrieved documents, tool descriptions, or source code. A model should choose a named tool or approved connection. It should not read, reproduce, or transform the underlying bearer token.

Inject credentials in a trusted execution layer immediately before the outbound request. Filter tool results so headers, tokens, and secret values cannot flow back into model-visible content. This reduces exposure through logging, prompt injection, memory, and accidental output.

Prefer short-lived credentials

A good credential is narrow in scope, audience, duration, environment, and purpose. Prefer:

• Short expiry and automated rotation
• A specific target audience or resource
• The minimum required scopes
• Separate credentials for production and test
• Separate connections across agents and purposes
• Fast revocation without disabling unrelated work
• A reference in logs, never the secret value itself

Some tools still require long-lived API keys. Store those keys in a suitable secrets system, isolate them per connection, rotate them, and wrap their use with policy. A shared admin key pasted into several agents is easy to deploy and almost impossible to attribute cleanly.

Use workload identity when the agent acts independently

When no user is present, the agent needs a workload identity rather than indefinite impersonation of the last person who signed in. Depending on the environment, that might use a cloud managed identity, SPIFFE/SVID, signed workload assertions, mutual TLS, or another proof-of-possession mechanism.

The right technology varies. The invariant is simpler: authenticate the deployed workload, bind it to an owner and purpose, and avoid reusable anonymous secrets.

Manage the whole credential lifecycle

Treat every credential as a lifecycle:

Provision -> bind -> store -> issue -> use -> monitor -> rotate -> revoke -> retire

Deleting an agent is not enough. Decommissioning should also find and revoke its OAuth grants, API keys, service accounts, tool connections, and child delegations.

OAuth for AI agents

OAuth is not one pattern. The correct flow depends on whether a user is present, whether the agent acts in the background, and whether authority must cross service boundaries.

User-delegated access

When a user authorizes an agent to access their data, use an interactive flow such as Authorization Code with PKCE. Request explicit scopes, record connection ownership, store refresh tokens outside model context, and require new consent when privileges change.

Do not interpret user consent as permission to inherit every capability that user has. The target application still needs resource-level and action-level authorization.

Background workload access

For agent-owned background work, use workload or client authentication only when the resource server's trust model supports it. Separate credentials by environment and operational purpose. Do not silently recycle a human refresh token into permanent background automation.

Token exchange and on-behalf-of access

The OAuth 2.0 Token Exchange standard defines a way to exchange authority for a downstream token. In an agent workflow, the goal is to preserve the requesting subject and acting agent while reducing scope, duration, audience, and accessible resources.

Do not pass the original broad token through every tool. Exchange it for a token intended for the next service when provider support exists.

Current OAuth guidance matters too. RFC 9700 documents OAuth 2.0 security best practices. RFC 8707 covers resource indicators, while RFC 9449 describes DPoP for sender-constrained tokens.

RFC 9700: Best Current Practice for OAuth 2.0 Security | RFC EditorThis document describes best current security practice for OAuth 2.0. It updates and extends the threat model and security advice given in RFCs 6749, 6750, and 6819 to incorporate practical experiences gathered since OAuth 2.0 was published and covers new threats relevant due to the broader application of OAuth 2.0. Further, it deprecates some modes of operation that are deemed less secure or even insecure.rfc-editor.org

Token protections that matter

Use audience restriction, resource indicators, short lifetimes, refresh-token rotation, PKCE, and sender constraints where supported. Never log bearer tokens. Treat a token accepted by the wrong service as a security defect, not a convenience.

Tool access is an authorization problem

"May use Salesforce" is not a useful production permission. A capability should describe an operation and boundary, such as:

• Read contacts in one workspace
• Draft an email but not send it
• Prepare a refund but not approve it
• Send only to approved domains
• Modify records below a transaction threshold
• Use a connection only for the declared run purpose

Tool discovery and tool authorization must remain separate. A model can know that a tool exists without being allowed to call it. The execution layer must re-authorize sensitive calls based on the actor, principal, purpose, resource, action, and current approval state.

MCP servers are trust boundaries

Treat every remote Model Context Protocol server as a separate trust boundary. The current MCP authorization specification makes resource-server responsibilities explicit.

For each server:

• Authenticate the client and server as required.
• Validate the token audience.
• Do not accept a token issued for a different service.
• Do not pass an upstream token blindly to a downstream API.
• Review exposed tools, descriptions, scopes, and dependencies.
• Log the server, tool, approved argument summary, authorization context, and result.

Tool restrictions are only one part of runtime safety. Our AI agent guardrails guide covers broader action constraints without duplicating them here.

A practical access-control model

A production policy should evaluate more than a role name. Use these fields:

• Subject: user, service, or organization authorizing the work
• Actor: agent identity executing it
• Owner: accountable person or team
• Purpose: declared task or workflow
• Resource: target system and data
• Action: requested operation
• Context: environment, time, risk, and approval state
• Constraints: amount, recipient, frequency, scope, and expiry

Here is a copy-ready policy brief for an authorization service or agent execution layer:

{
  "subject": "support-team",
  "actor": "refund-review-agent-prod",
  "owner": "customer-operations",
  "purpose": "prepare_refund_for_verified_ticket",
  "resource": "billing/refunds",
  "allowed_actions": ["read_invoice", "draft_refund"],
  "denied_actions": ["approve_refund", "change_permissions"],
  "constraints": {
    "maximum_amount_usd": 500,
    "environment": "production",
    "expires_in_minutes": 30,
    "approval_required_for": ["send_customer_message"]
  }
}

Start with deny-by-default. Approve connections explicitly. Separate read from write. Elevate access for one task, then let it expire. Review unused and excessive permissions regularly.

RBAC can establish broad job permissions, but agent access often needs attributes and relationships too. The policy may need to know that an agent is owned by customer operations, acting for a particular user, inside production, on a verified ticket, below a dollar limit.

High-impact actions such as financial transactions, irreversible deletion, publishing, and permission changes deserve step-up authorization or human approval. Our guide to human-in-the-loop AI agents explains approval patterns in depth.

Auditability, ownership, and revocation

A useful audit event captures:

• Agent identity and version
• Accountable owner
• Initiating user or service
• Delegation or authorization reference
• Tool or MCP server called
• Action and target resource
• Policy decision and approval record
• Credential identifier, never the secret
• Timestamp and run ID
• Outcome, error, and downstream side effects

Do not rely on raw model reasoning as an audit trail. It can contain sensitive information, and it is not a reliable statement of why a policy allowed an operation. Record structured inputs, decisions, approvals, calls, and outcomes.

Build several kill switches

One global off switch is too blunt. Plan to:

1. Stop the current run.
2. Disable one tool connection.
3. Revoke a token or grant.
4. Disable the agent identity.
5. Block one user-agent delegation.
6. Quarantine a compromised integration.
7. Review child delegations and downstream effects.

Identity review should follow the agent lifecycle. Trigger it when the purpose, owner, model, runtime, connected tools, or privileges change, and when the agent becomes inactive.

For organization-wide ownership and policy, use the broader AI agent governance framework.

Implementation checklist

Before deployment

• Inventory the agent and assign an accountable owner.
• Define its purpose, tools, resources, and prohibited actions.
• Separate the agent identity from human identities.
• Choose user-delegated or workload access intentionally.
• Remove secrets from prompts, memory, logs, and code.
• Set scopes, audiences, expiry, limits, and approval thresholds.
• Test that privilege cannot expand through sub-agent delegation.

During every run

• Authenticate the agent.
• Resolve the initiating principal and delegation.
• Retrieve or issue only the required credential.
• Re-authorize each sensitive tool call.
• Record policy decisions, approvals, and side effects.
• Prevent credentials from entering model-visible data.

After deployment

• Review unused and excessive access.
• Rotate credentials and refresh grants.
• Test every revocation path.
• Monitor anomalous tool use.
• Remove orphaned agents and connections.
• Reassess access whenever the workflow changes.

Where Rerun fits

Rerun is an operations and governance layer for agents that run continuously on a dedicated private cloud environment. Teams connect approved tools, watch actions happen, review logs, and pause for human input when a workflow requires it.

Rerun does not replace an identity provider, OAuth authorization server, secrets manager, or application authorization system. Those systems establish identity, issue credentials, and enforce resource permissions. Rerun's role is to operationalize approved connections and agent execution inside visible, governed workflows.

That is different from a chatbot that waits for the next message. Unlike primarily preconfigured workflow automation, including conventional Zapier, Make, or n8n flowcharts, agentic systems may choose tools and actions dynamically at runtime. Their identity, authority, and audit record must travel with the work.

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The production rule

A trustworthy agent can answer five questions for every consequential action:

1. Which agent acted?
2. Who authorized it?
3. What exact authority did it receive?
4. How long did that authority last?
5. Can the action be attributed, reviewed, and revoked?

If any answer is "the shared API key," AI agent identity management is unfinished.

Use distinct workload identities, explicit delegation, short-lived credentials, operation-level permissions, structured audit events, and tested kill switches. Then keep the identity provider, target application's authorization, and agent operations layer honest about their separate jobs.