Updated and refreshed on October 8th, 2026.
Key Takeaways
- Agentic identity answers which agent is acting in every access decision and, when that agent acts for a user, on whose behalf. Human IAM and traditional workload identity were each built to answer one of those questions.
- When an agent acts with a user’s delegated authority, the agent’s identity and the user’s identity have to be evaluated together to keep attribution and least privilege intact.
- Short-lived credentials and policy evaluated on every request replace the static secrets and one-time permission grants many agents still run on.
- Start with the systems already known to be sensitive, and put identity-based access in front of those first.
What do we mean by Agentic Identity?
Agentic identity is the identity model built specifically for autonomous AI agents, distinct from both human user accounts and the static service accounts used for traditional workloads. Where conventional identity systems verify a fixed set of credentials tied to a person or a long-running service, agentic identity establishes which agent is acting and, when the agent acts on someone’s behalf, which user or process initiated the task it is carrying out. It replaces standing, long-lived credentials with access scoped to intent, duration, and specific resources, enforced by policy at the moment of each action rather than granted once at setup. That combination is what makes it possible to attribute an agent’s actions accurately, apply least-privilege access to each part of an agent individually, and keep the audit trail traditional IAM was never built to produce for autonomous, multi-step activity. Every agent starts with its own workload identity, which proves which software is running and where it’s running. An autonomous agent that isn’t acting for a user operates under that identity alone, with permissions scoped narrowly to its task. A user-driven agent adds a second dimension: the identity of the user who initiated the task. Aembit calls that combination blended identity.Agentic Identity vs. Human and Workload Identity
| Human identity | Workload identity | Agentic identity | |
| Represents | A person, such as an employee, contractor, or customer | An application, service, script, or pipeline running a defined job | An AI agent, plus user context when it acts on someone’s behalf |
| Behavior | Interactive, directed by the person | Deterministic, repeats the same task | Nondeterministic, plans its own steps toward a goal |
| Authentication | SSO, MFA, passkeys | API keys, client secrets, mTLS certificates, SPIFFE SVIDs, workload identity federation | The agent’s workload identity, plus OAuth delegation or token exchange when it carries a user’s authority |
| Credential lifespan | Sessions lasting hours to days | Often long-lived, unless federation replaces stored secrets | Short-lived, issued per task or session |
| Access decision | Roles and groups assigned at provisioning | Service roles scoped at deployment | Policy evaluated per request against the agent, runtime context, and the user when there is one |
| Audit trail must show | Which person acted | Which service called which API | Which agent acted, from which instruction or trigger, for which user if any, against which resource |
This is why choosing what kind of identity an AI agent should have is a decision to make before an agent reaches production systems.
Why AI Agents Break Traditional IAM
An AI agent is an assembly of interoperating software components, each contributing to what the agent does. A typical agent includes:- Orchestrator: Coordinates execution and keeps state or memory across interactions.
- Reasoning engine: Decides the next action based on the goal, the available context, and what previous steps returned.
- Tools and connectors: Call external services such as collaboration platforms, cloud storage, databases, and business applications.
- Runtime environment: The virtual machine, container, or function where each component runs.
The Shift Security Teams Are Making
Closing those gaps changes agent access in five areas.| Area | Traditional approach | Agentic identity approach |
| Credentials | Static API keys and secrets stored in configuration | Short-lived credentials issued just in time for one approved request |
| Identity | Agent borrows a shared service account or passes through a user’s token | Agent uses its own identity, evaluated with the user’s identity when it acts for one |
| Authorization | Permissions granted once, at setup | Policy evaluated on every request with runtime context |
| Audit | Logs show a credential was used | Logs trace the triggering instruction to agent action to API call |
| Starting point | Catalog every agent and credential first | Secure the systems already known to be sensitive |
From Static Secrets to Short-Lived Credentials
A secret stored in an agent’s configuration is available to anything that can read that configuration. Issuing credentials at request time, scoped to the resource being accessed, removes the standing secret an attacker would otherwise steal or an agent could be tricked into exposing.- Use workload identity federation where the target platform supports it, so the agent authenticates with its runtime identity instead of a stored key.
- Where a downstream service still requires a secret or token, issue it just in time, scope it to the specific resource, and let it expire when the task ends.
- Keep downstream credentials out of the agent’s reach, so the model never handles the credential it’s using.
From Borrowed Identity to Blended Identity
Many enterprise agents don’t act for themselves. A user asks an agent to update a ticket or pull a document, and the agent reaches the downstream system using delegated user authority. In an on-behalf-of OAuth flow, for example, the user’s identity and permissions can travel through the request chain. Depending on how that token is issued, the downstream API may see the user’s permissions and never see the agent. The difference between impersonation and delegation decides whether the agent stays visible.- Give the orchestrator, connectors, and other agent components their own verifiable workload identities, bound to the environment they run in.
- For user-driven requests, preserve delegation without losing the agent’s identity, so access policy and audit records can account for both the user and the agent.
- For delegated requests, evaluate agent identity and user identity together in a single policy decision, instead of treating either one as sufficient on its own.
- For autonomous agents with no user in the loop, issue each agent its own identity and task-scoped permissions, so none of them runs on a shared service account.
From One-Time Grants to Runtime Policy
An agent’s next step depends on what its last step returned, so permissions assigned at setup can’t anticipate every request it will make.- Evaluate each request against runtime context such as security posture, location, and time of access, using conditional access policies.
- Feed posture signals from security platforms already in place, such as CrowdStrike or Wiz, into the access decision.
- Scope access to the task and the tool, and revoke the session when the task ends or a posture signal changes.
From Access Logs to the Full Causal Chain
A log entry showing that an API key was used leaves an investigator guessing which agent used it and why.- Record the instruction or event that started the task, the user who issued it if there was one, the agent and component that acted, the policy decision, and the downstream API call as one linked chain.
- Centralize policy, enforcement, and audit across APIs, SaaS, cloud, and data, so the record stays consistent wherever an agent operates.
From Catalog-First to Sensitive-Systems-First
Security teams generally already know which systems hold regulated data, production infrastructure, or financial controls. Putting identity-based access in front of those systems first protects the highest-risk paths an agent can reach from the first day of enforcement.- Start with the MCP servers, APIs, and databases that agents can already reach and that hold sensitive data.
- Enforce policy in front of those resources, so every agent request is verified regardless of which framework or model produced it.
- Extend the same model to new agents and tools as they come online.
Agentic Identity in Practice
Here’s how these patterns apply to three common agent deployments:- Workforce AI assistants: A $300B investment firm eliminated long-lived credentials stored in Claude and MCP server configurations, replacing them with short-lived, policy-scoped tokens issued per session.
- Low-code enterprise agents: Microsoft Copilot Studio agents can each carry a unique blended identity, so access decisions account for both the agent and the employee using it.
- MCP servers: MCP clients connecting to MCP servers receive OAuth-based authorization, with policy applied to each request between agent and server.
