Top AI Agent Identity Management and IAM Platforms in 2026

AI agent identity management is becoming a separate security problem.

AI agents call APIs, use credentials, access sensitive data, invoke tools, and act on behalf of users. More autonomous agents can also choose which tool to call and what action to take while a task is running.

That changes what access control needs to do.

Traditional identity and access management still matters. Enterprises need to know who an agent is, who owns it, what credentials it uses, and what broad access it should have.

But identity is only the starting point.

Once an autonomous agent starts acting, the important question becomes "should this agent be allowed to perform this specific action for this task right now?"

For autonomous agents, runtime authorization is the most direct way to answer that question.

Enterprise IAM establishes identity, ownership, lifecycle, and broad access. Non-human identity security helps discover the credentials and machine identities around an agent. Cloud-native IAM gives agents identities inside the infrastructure where they run.

Runtime authorization adds another control. It evaluates what the agent is trying to do at the moment the action happens.

That is the lens used in this guide.

We compare 10 AI agent identity management and IAM platforms in 2026. The products are not interchangeable. Some focus on identity. Some focus on governance. Some start with non-human identity discovery. Some provide cloud-native agent identity. Others move the access decision closer to runtime.

For organizations deploying autonomous agents, that distinction matters more than a long feature checklist.

Top AI Agent Identity Management and IAM Platforms at a Glance

What Is AI Agent Identity Management?

AI agent identity management is the set of systems and controls used to create, authenticate, authorize, govern, and audit the identities of autonomous or semi-autonomous AI agents.

It overlaps with traditional IAM, identity governance, non-human identity security, and workload identity.

But AI agents introduce a different operating model.

A human user usually works through a stable account and a predictable set of applications. A service account usually performs a defined technical function.

An AI agent can be more dynamic.

It can plan a task, choose a tool, generate arguments, act on behalf of another identity, and cross several systems during one workflow.

That creates new requirements around:

• Distinct agent identity
• Human ownership
• Delegated authority
• Credential handling
• Fine-grained access
• Runtime authorization
• Lifecycle governance
• Audit and attribution

AI agent identity governance covers the ownership, lifecycle, policy, access reviews, and audit controls around those identities.

AI agent security goes further. It can include identity, runtime authorization, data security, tool security, behavioral controls, and other protections around how agents operate.

Why AI Agent Identity Management Is Different From Traditional IAM

Traditional IAM is good at answering stable identity questions.

Who is the user?

Which group are they in?

Which application can they access?

Which role have they been assigned?

Those controls remain important for agents.

The problem is that autonomous agents make decisions after authentication has already happened.

An agent can authenticate correctly and still attempt an action that should not be allowed for the current task.

Consider a sales research agent with access to a CRM.

The agent may need to search customer records for one task.

The underlying credential may also allow record deletion, bulk export, or administrative changes.

A standing role can say that the credential has access.

It does not always explain whether the current task justifies the current action.

That is the gap between identity and runtime authorization.

Identity answers:

• Who is the agent?
• Who owns it?
• Which systems recognize it?
• What broad access has been assigned?

Runtime authorization answers:

• What task is the agent performing?
• Which tool is it calling?
• What action is it requesting?
• What arguments is it using?
• Which resource is involved?
• What authority has been delegated?
• Should this action run now?

For autonomous agents, the second set of questions becomes critical.

Why Autonomous AI Agents Need Runtime Authorization

The core security issue is simple.

Identity is relatively stable. Agent behavior is dynamic.

The same agent may use the same identity across many tasks.

The access required for each task can be different.

That makes standing permissions a weak control point for some autonomous workflows.

If a credential allows 20 actions but the current task needs only one, the agent should not need all 20.

Runtime authorization lets the security decision move closer to the action.

It can evaluate the agent, task, tool, arguments, resource, delegated authority, and policy before the action runs.

This is a practical application of least privilege and Zero Trust.

Authenticate the agent first.

Then keep evaluating whether the requested action has the minimum access required.

That is a better control model for agents that choose tools and actions dynamically.

Why Teams Are Looking for AI Agent IAM Platforms

Agents are appearing faster than identity teams can inventory them

Agents can be created in cloud platforms, SaaS applications, developer frameworks, automation tools, MCP environments, and internal systems.

The first problem is often visibility.

If a security team cannot answer which agents exist, who owns them, and which credentials they depend on, it cannot govern their access reliably.

This is where platforms such as Astrix, Veza, Cyera/Oasis, and Okta become relevant.

Agent identity does not automatically create accountability

An agent can have a technical identity without having a clear human owner.

That creates a governance problem when the agent needs new access, changes purpose, creates risk, or should be retired.

Microsoft separates technical owners from business sponsors. Okta assigns human owners. IGA and NHI platforms also map agents to accountable people or groups.

A useful identity program should answer two questions:

Who is the agent?

Who is accountable for it?

Agents often depend on broad credentials

Many agents still use service accounts, OAuth tokens, API keys, IAM roles, and other credentials created for applications.

Those credentials may carry more authority than the current task requires.

Credential storage helps protect the secret.

It does not always reduce the authority behind the secret.

The better question is:

What authority should this credential carry for this task?

AWS uses workload identities and credential providers. Okta governs agent connections and credentials. NHI platforms discover the credentials already attached to agents. AgntID narrows access around the task and action.

Agents act on behalf of users

An agent can authenticate as itself while using authority delegated by another identity.

That creates a second identity relationship.

A useful model should preserve:

• The person or workload that initiated the task
• The agent performing the work
• The authority delegated to the agent
• The target resource
• The requested action

Ping makes delegated authority central to its agent IAM model. AWS supports delegated access for AgentCore workloads. Microsoft supports governed access through Entra workflows.

Autonomous agents make standing access harder to defend

A controlled workflow may work with predefined roles and permissions.

An autonomous agent may choose a tool and action at runtime.

That is where broad standing access becomes harder to justify.

Runtime authorization can reduce the decision to what the agent needs for the current task.

How We Evaluated AI Agent Identity Management Platforms

We did not compare these products through one feature checklist.

The category is too broad for that.

We used eight evaluation criteria.

1. Primary control point

We first looked at where the product starts.

The main approaches are:

• Runtime authorization
• Enterprise IAM and identity governance
• Non-human identity security
• Access intelligence
• Cloud-native agent identity

Several products span more than one layer. The category describes the primary starting point.

2. Distinct agent identity

We looked at whether the platform can separate the agent from:

• The human user
• The application
• The runtime
• The service account
• The credential

A distinct identity improves ownership, policy, lifecycle, and audit.

3. Ownership and lifecycle

We looked for:

• Human ownership
• Registration
• Provisioning
• Access reviews
• Suspension
• Decommissioning
• Auditability

4. Credential handling

We evaluated:

• Credential discovery
• Secret storage
• Temporary credentials
• Credential providers
• Scope narrowing
• Revocation

5. Effective access

The important question is not whether a platform supports RBAC.

It is:

What can this agent actually access, and why?

Platforms such as Veza focus heavily on this problem.

6. Delegation

We looked at whether the system can distinguish the agent's own identity from authority delegated by another identity.

7. Fine-grained and runtime authorization

We gave this more weight for autonomous-agent use cases.

A runtime control evaluates the action close to the point where it executes.

That is different from:

• Authentication
• Agent registration
• Static role assignment
• Tool discovery
• Credential storage

8. Ecosystem fit

A cloud-native identity can be the simplest option when agents live inside one cloud.

An enterprise IAM extension can fit a mature identity program.

An NHI platform can be the right starting point when visibility is missing.

A runtime layer becomes more important when authenticated agents still have too much standing authority.

1. AgntID: Runtime Authorization for AI Agents

AgntID is built around runtime authorization for autonomous AI agents.

Its core premise is An agent can be properly authenticated and still attempt an action that should not be allowed for the current task.

AgntID evaluates access before any tool call is made.

Its customer-hosted runtime can evaluate the task, tool, action, arguments, target resource, policy, and context before the action reaches the target system.

The decision can then allow the request, deny it, or narrow the credential used for that task.

This is the basis of AgntID's just-for-task access model.

Instead of giving an agent a broad credential for everything it might do, access can be narrowed around what the current task requires.

That makes AgntID different from products that primarily focus on registration, lifecycle, inventory, or standing access.

AgntID does not replace those systems.

It complements them.

An IAM platform can establish the agent's identity and ownership. An NHI platform can discover the machine identities and credentials around it. AgntID adds the runtime decision that determines what the authenticated agent may do now.

Best for

Autonomous agents that choose tools and actions dynamically and need per-action least privilege.

Strengths

• Per-action authorization
• Task-aware policy evaluation
• Just-for-task access
• Runtime credential narrowing
• Customer-hosted enforcement
• Works with existing IAM systems
• Works with existing tools and APIs
• MCP compatibility

Tradeoffs

• AgntID is not an enterprise identity registry
• Organizations still need upstream identity, ownership, and lifecycle controls
• Its strongest value appears when agent autonomy makes action-level context important

Where it fits in AI agent identity management

AgntID is the most direct fit in this comparison for organizations that want to control autonomous agent access at runtime.

For teams that want to enforce least privilege based on the task an agent is performing, runtime authorization is the strongest control point.

AgntID is built specifically around that model.

2. Okta: Enterprise AI Agent Identity and Governance

Okta extends its enterprise identity platform to AI agents.

Okta for AI Agents can discover agents, register them as first-class identities, assign human owners, govern connections, and manage access across the lifecycle.

Okta Identity Governance also supports access review and certification workflows for agent identities.

The value is continuity.

Organizations that already use Okta can bring agents into the same identity control plane used for other enterprise identities.

That gives security teams a consistent way to manage identity, ownership, lifecycle, and broad access.

Best for

Enterprises that already use Okta and want AI agents governed through the same identity program.

Strengths

• Agent discovery
• Identity registration
• Human ownership
• Lifecycle governance
• Connection governance
• Access reviews
• Credential management
• Existing enterprise IAM foundation

Tradeoffs

• Identity and lifecycle governance do not by themselves determine whether every individual action should run
• Teams with autonomous agents may still need a runtime decision close to the action
• The strongest fit is where Okta already plays a major identity role

Where it fits in AI agent identity management

Strong for agent identity, ownership, lifecycle, and broad access governance.

Okta can establish who the agent is and what access it should broadly have.

Runtime authorization can complement that foundation when action-level decisions are required.

3. Microsoft Entra Agent ID: AI Agent Identity Governance for Microsoft Environments

Microsoft Entra Agent ID gives organizations a dedicated identity model for AI agents.

Enterprises can create agent identities, assign owners and sponsors, and bring agents into Entra lifecycle and access-governance processes.

The distinction between owners and sponsors is useful.

Owners handle technical administration.

Sponsors provide business accountability for the agent and its lifecycle.

Agent identities can also participate in access packages and other governance workflows already used across Microsoft Entra.

Best for

Organizations that already use Microsoft Entra for identity and access governance.

Strengths

• Dedicated agent identities
• Technical owners
• Business sponsors
• Lifecycle governance
• Access packages
• Authentication
• Access management
• Existing Entra governance workflows

Tradeoffs

• The strongest experience is inside the Microsoft ecosystem
• Governance defines broad access and accountability, but autonomous actions may still need controls closer to execution
• The agent identity model introduces several related governance objects that teams need to manage

Where it fits in AI agent identity management

Strong for agent identity, accountability, lifecycle, and entitlement governance.

Entra provides a strong identity foundation.

Runtime authorization becomes the next layer when the decision depends on the task and action.

4. Ping Identity: Agentic IAM and Delegated Runtime Access

Ping Identity combines agent identity, delegation, and runtime authorization.

Its Identity for AI capabilities support agent registration, authentication, lifecycle management, delegated authority, and human accountability.

Ping also evaluates authorization when agents act.

That matters because delegated authority creates two identities in the same transaction.

The system needs to know which agent is acting and whose authority the agent is using.

Ping is one of the platforms in this list that reaches beyond identity registration into runtime access decisions.

Best for

Enterprises that need agent identity and explicit delegation across workforce, customer, partner, and B2B environments.

Strengths

• Agent registration
• Authentication
• Ownership
• Lifecycle management
• Delegated authority
• Runtime authorization
• Centralized policy
• Existing enterprise identity foundation

Tradeoffs

• The architecture is broader than simple agent inventory or lifecycle management
• Teams need to design delegation and action policy deliberately
• Organizations focused only on discovery may not need the full model

Where it fits in AI agent identity management

Strong across identity, delegation, and runtime authorization.

Ping is closer to the runtime model than most traditional IAM vendors.

AgntID remains more narrowly focused on just-for-task access and task-aware runtime enforcement.

5. Cisco / Astrix: AI Agent and Non-Human Identity Discovery

Astrix approaches AI agent security through non-human identity discovery.

Its platform discovers AI agents, MCP servers, service accounts, API keys, OAuth tokens, and other machine identities.

It then maps those identities to owners, permissions, credentials, and risk.

This is valuable when the first problem is visibility.

An organization cannot govern agents it does not know exist.

Astrix is now part of Cisco, extending these capabilities into a broader enterprise security portfolio.

Best for

Organizations that first need to discover unmanaged agents, credentials, and non-human identities.

Strengths

• Agent discovery
• NHI inventory
• Credential visibility
• Ownership mapping
• Permission analysis
• Risk analysis
• MCP discovery
• Lifecycle governance

Tradeoffs

• Discovery tells teams what exists but does not automatically solve per-action authorization
• Organizations with a complete inventory may need stronger runtime controls next
• The product is now part of Cisco, so buyers should evaluate the current Cisco roadmap

Where it fits in AI agent identity management

Strong for discovery, NHI context, ownership, and risk.

Astrix helps identify the identities and credentials around an agent.

Runtime authorization addresses the next question: what should the agent be allowed to do with that access now?

6. ServiceNow / Veza: AI Agent Access Visibility and Governance

Veza approaches AI agent security through access visibility.

Its Access Graph maps relationships between identities, permissions, entitlements, and enterprise resources.

That helps answer - What can this agent access, and how did it get that access?

This matters because effective access can come from several places.

A group may grant one permission. A cloud role may grant another. An application entitlement may expand access further.

Veza brings those relationships into one graph.

Best for

Organizations that need to understand effective access across complex enterprise environments.

Strengths

• Access Graph
• Effective-access visibility
• Entitlement analysis
• Agent visibility
• Identity-risk analysis
• Access governance
• Cross-system access paths

Tradeoffs

• Veza focuses on understanding and governing access rather than issuing every agent identity
• Effective-access visibility does not by itself decide whether a specific runtime action should execute
• The model depends on connected identity and entitlement data

Where it fits in AI agent identity management

Strong for understanding effective access.

Veza explains what an agent can reach and why.

Runtime authorization can then decide whether a specific action should use that access.

7. Cyera / Oasis Security: AI Agent Identity and Data Security

Oasis Security approaches agent identity through the wider non-human identity environment.

AI agents often depend on service accounts, OAuth tokens, API keys, and other machine identities to reach enterprise systems.

Oasis discovers those identities, maps permissions and credentials, attributes ownership, and manages lifecycle.

Cyera adds data-security context.

That connects the identity question to another question:

How sensitive is the data the agent can reach?

This is useful for organizations where agent risk depends heavily on data exposure.

Best for

Organizations that need to understand agent identity and sensitive-data exposure together.

Strengths

• Agent discovery
• NHI mapping
• Ownership
• Credential visibility
• Permission analysis
• Data-security context
• Risk analysis
• Lifecycle governance

Tradeoffs

• The platform covers a broader security problem than agent IAM alone
• Data and identity visibility do not automatically enforce task-specific access
• Organizations may still need a runtime control for autonomous agent actions

Where it fits in AI agent identity management

Strong for connecting agent identity risk with data exposure.

This helps teams understand the consequence of access.

Runtime authorization determines whether a specific action should use that access.

8. One Identity: AI Agent Identity Governance and IGA

One Identity extends established identity governance processes to AI agents and other non-human identities.

Identity Manager applies familiar IGA controls to agent identities.

Organizations can assign ownership, provision access, review permissions, apply policy, govern lifecycle, and maintain audit evidence.

This is useful for enterprises that already operate formal identity-governance programs.

Best for

Organizations that want AI agents governed through an existing IGA program.

Strengths

• Identity governance
• Ownership
• Lifecycle management
• Access reviews
• Policy
• NHI governance
• Auditability
• Established IGA model

Tradeoffs

• One Identity is governance-first rather than runtime-first
• Teams that need per-action authorization may need another enforcement layer
• The strongest fit is for organizations with mature IGA processes

Where it fits in AI agent identity management

Strong for lifecycle governance, accountability, and auditability.

It provides the governance foundation around the agent.

It does not replace runtime decisions when access needs to change with the task.

9. Google Cloud: Cloud-Native AI Agent Identity

Google Cloud Agent Identity gives AI agents their own first-class IAM principal type.

The identity is distinct from human accounts and generic service accounts.

Google Cloud IAM policies can be applied directly to the agent principal.

That makes the agent itself a native security identity inside Google Cloud.

The model also supports agent lifecycle, credential handling, and auditing.

Best for

Organizations building agents primarily inside Google Cloud.

Strengths

• First-class agent principal
• Distinct identity from human accounts
• Native IAM policies
• Cryptographic identity
• Lifecycle
• Credential handling
• Auditing
• Cloud-native integration

Tradeoffs

• The strongest fit is inside Google Cloud
• Multi-cloud organizations still need broader enterprise governance
• Native identity defines who the agent is and what access it has, but action-specific controls may still be needed for highly autonomous agents

Where it fits in AI agent identity management

Strong for cloud-native agent identity.

Google solves the identity problem close to the infrastructure.

Runtime authorization becomes relevant when the decision needs task and action context beyond the standing IAM policy.

10. AWS AgentCore Identity: AI Agent Workload Identity and Credentials

Amazon Bedrock AgentCore Identity provides identity and credential management for AI agents and automated workloads.

Agent identities are implemented as workload identities.

Each workload identity acts as a stable anchor across IAM roles, OAuth tokens, API keys, external services, and deployment environments.

AWS also provides a centralized identity directory and credential-management capabilities.

Agents can authenticate as themselves while accessing resources on behalf of users when delegation is required.

Best for

Teams using Amazon Bedrock AgentCore or agents that need one workload identity across AWS and external systems.

Strengths

• Workload identity
• Centralized identity directory
• Credential providers
• OAuth integration
• API-key support
• AWS IAM integration
• User delegation
• Support for multiple deployment environments

Tradeoffs

• The deepest integration is with AWS and AgentCore
• Teams still need to define the policies that determine what credentials and resources an agent may use
• Static workload identity does not always answer whether one specific autonomous action should run

Where it fits in AI agent identity management

Strong for cloud-native workload identity and credential orchestration.

AWS provides a strong identity and credential foundation.

A runtime authorization layer can complement it when access needs to narrow around the current task.

AI Agent IAM vs. NHI Security vs. Runtime Authorization

These categories overlap.

They solve different questions.

Enterprise IAM asks: Who is the agent?

Okta, Microsoft Entra Agent ID, Ping Identity, and One Identity focus heavily on identity, ownership, lifecycle, governance, and broad access.

These controls establish who the agent is and who is responsible for it.

NHI security asks: What identities and credentials does the agent depend on?

Astrix and Oasis start with machine identities and credentials.

They help teams discover unmanaged agents, service accounts, tokens, API keys, and permission relationships.

Access intelligence asks: What can the agent reach?

Veza focuses on effective access.

Cyera adds the sensitivity of the data behind that access.

These platforms help teams understand exposure.

Cloud-native IAM asks: What is the agent's native security principal?

Google Cloud and AWS build agent identity directly into the infrastructure where agents run.

This can provide a cleaner foundation than representing every agent through a generic service account.

Runtime authorization asks: Should this action run now?

This is the most important access question for autonomous agents.

The agent may already be authenticated.

It may already have a valid identity.

It may already have a credential.

The remaining question is whether the current task justifies the current action.

AgntID is built around that decision.

Why Runtime Authorization Is the Best Control Model for Autonomous Agents

For autonomous agents, access decisions should move closer to the action.

The reason is simple.

The identity may stay the same while the task changes.

A customer-support agent may need to read an account record in one workflow and update a billing field in another.

The same underlying credential may have broader rights than either task requires.

A standing role answers what the identity can do in general.

Runtime authorization can answer what the agent needs to do now.

That makes least privilege more precise.

Instead of asking:

What can this agent do?

the system can ask:

What does this agent need to do for this task?

For autonomous agent access, that is the stronger control model.

Where AgntID Fits

AgntID is not a replacement for enterprise IAM.

It is not a replacement for NHI discovery.

It is not a replacement for cloud-native workload identity.

It is the runtime authorization layer that complements those systems.

A practical architecture can look like this:

1. Okta, Microsoft Entra, Google Cloud, AWS, or another identity system establishes the agent identity.
2. An IGA or NHI platform establishes ownership, lifecycle, credentials, and broad access.
3. AgntID evaluates the specific task and action at runtime.
4. The agent receives only the access required for that task.
5. The action is allowed or denied before it reaches the target system.

The identity platform answers who.

The runtime layer answers what now.

For autonomous agents, both matter.

How to Choose an AI Agent Identity Management Platform

Start with the problem you are trying to solve.

If you do not know which agents exist

Start with discovery.

Astrix, Veza, Cyera/Oasis, and Okta are relevant.

If agents lack clear ownership

Start with enterprise IAM or IGA.

Microsoft Entra Agent ID, Okta, One Identity, and Ping are strong options.

If you cannot explain effective access

Start with access intelligence.

Veza is built around this question.

If sensitive-data exposure is the main concern

Look at Cyera/Oasis.

If agents live primarily in one cloud

Evaluate Google Cloud or AWS native identity first.

If agents act on behalf of users

Evaluate delegation.

Ping, Microsoft, AWS, and Google Cloud all address this problem from different parts of the stack.

If authenticated agents still have too much standing authority

Use runtime authorization.

This is where AgntID has the clearest fit.

If agents choose tools and actions dynamically

Treat runtime authorization as a primary control.

For this problem, AgntID is the most direct fit in this comparison.

Which AI Agent Identity Management Approach Is Best for Autonomous Agents?

There is no single platform that replaces every identity and security layer.

But there is a stronger control point for autonomous access.

Enterprise IAM is necessary for identity, ownership, and lifecycle.

NHI security is useful for discovery, credentials, and machine-identity risk.

Cloud-native identity provides a strong infrastructure foundation.

Access intelligence explains what an agent can reach.

For autonomous agents, the remaining risk is dynamic.

The agent can choose a tool, generate arguments, and attempt an action after authentication has already happened.

That makes runtime authorization the strongest access-control point for autonomous behavior.

The question becomes:

Should this agent be allowed to perform this action for this task right now?

AgntID is built around that question.

It evaluates the task and requested action, then narrows access to what the task requires.

For organizations deploying autonomous agents across tools, APIs, MCP servers, and enterprise systems, that makes AgntID the most direct platform in this comparison for runtime least privilege.

Frequently Asked Questions