Agent Identity: Securing the future of autonomous agents

We’re entering a new era—not just smarter systems, but autonomous actors. Artificial intelligence (AI) agents are no longer confined to innovation labs or demos; they’re now embedded in critical, high-stakes workflows.

Imagine a loan application process: a customer interacts with a digital agent to submit documents (human-to-agent), which then triggers other agents to retrieve credit history from financial APIs, assess risk models, and cross-check employer data across HR systems (agent-to-agent). Finally, another agent compiles a recommendation and explains the decision to a human underwriter or the applicant (agent-to-human). 

In such a scenario, each AI agent must be authenticated, tracked, and trusted before taking any action. That is why we are excited to announce the first release of AGNTCY Agent Identity, a framework that assigns, verifies, and manages credentials for AI agents.

In this blog we will more deeply examine the current challenges around AI agent identity, the characteristics of an agent identity management solution, and the key principles any such solution must follow.

Challenge #1: Infrastructure isn’t built for AI agents

Traditional identity systems were built to serve human users interacting with applications that persist over time—often referred to as long-lived apps. These systems assume stable, ongoing relationships between users and software. In contrast, autonomous agents require identity solutions that can keep up with their unique behaviors: operating at machine speed, scaling up or down instantly, and existing only as long as necessary.

1. Delegation is too rigid

   Agents require fine-grained, task-specific permissions. But conventional delegation mechanisms (like static roles or API keys) are often too broad or too restrictive.

   Example: An HR automation agent might need temporary access to payroll data, but not to other sensitive records. Without tightly scoped and time-bound permissions, agents risk being over-permissioned or blocked from functioning.

2. Blurry identity boundaries

   AI agents increasingly operate in human-like ways and can interact with web apps, navigate GUIs, and even solve CAPTCHAs. This blurs the line between human and machine activity, complicating attribution.

   Example: A sales manager asks an AI agent like OpenAI’s Operator, a Computer-Using Agent (CUA) to generate a report. The agent logs into Salesforce, pulls the data, and shares it with the team. But who actually performed the action? Was it the manager, the agent acting on their behalf, or an admin behind the scenes? Traditional IAM can’t clearly attribute the activity, undermining audit trials and accountability.

3. Temporary data, permanent risk

   To perform tasks, agents often store sensitive identity-linked data (credentials, session cookies, or tokens) in memory. These aren’t just technical artifacts; they represent active identity states. If not properly cleared, they become liabilities.

   Example: An agent logging into a finance app might temporarily store a session token tied to a privileged identity. If that memory isn’t wiped after the task, it risks being leaked or exploited and potentially granting unauthorized access long after the agent has completed its job. Legacy Auth falls short without the granular task and temporal controls.

Challenge #2: Traditional solutions are not designed for agentic workflows

OAuth 2.0 and Security Assertion Markup Language (SAML) were groundbreaking for user-to-app and app-to-app delegation. But both assume static permissions, predefined scopes, and fixed session lengths—none of which work for agents that need just-in-time access changes based on task, context, or risk.

• OAuth uses access tokens and scopes, which are excellent for humans granting app permissions, but too rigid for agents whose roles can evolve from minute to minute.
• SAML provides session-based authentication with Extensible Markup Language (XML) assertions, but its heavy reliance on user attributes and static trust models makes it incompatible with agents that need continuous validation and contextual adaptation.

Both systems follow a "trust-once" model meaning that once authenticated; an entity is trusted for the duration of the session. That’s a dangerous assumption when dealing with agents that can change intent, be hijacked, or operate under shifting contexts. 

More fundamentally, federated identity and single sign-on (SSO) systems were built around long-lived, human-initiated sessions—not for autonomous, ephemeral, and polymorphic agents. They lack support for fine-grained, task-scoped access and, short-lived credentials.

In a world where agents act independently, spin up or down in seconds, and cross organizational boundaries, identity systems must move beyond static federation and into dynamic, verifiable, task-based and contextual controls.

The solution: Agent Identity management

AI agents aren’t traditional users or services—they represent a new identity class: autonomous, ephemeral, polymorphic, and task-driven. Identifying and securing them requires more than retrofitting legacy IAM. It demands a system built from the ground up for dynamic, machine-first environments.

Agents need identity systems that are:

• Context-aware: Adapts access in real time based on task, role, and risk.
• Accountable: Tracks and attributes actions clearly, even across identity transitions.
• Granular: Grants narrowly scoped, purpose-specific permissions.
• Ephemeral: Issues with short-lived credentials that expire and are revoked automatically.

This is not just a technical patch, but a foundational shift. AI agents must become first-class citizens in our identity and trust infrastructure.

The path forward

To secure autonomous workflows, identity must be embedded when the agent is registered and onboarded for authorized use, and then continuously verified and tightly managed throughout its lifecycle:

• Assign verifiable identities during agent registration and onboarding, before the agent is authorized for use.
• Evaluate trust based on context, not just roles.
• Operate seamlessly across APIs, clouds, and org boundaries.
• Log and audit every action with full transparency.

This is how we build trust in the age of AI—not with static credentials, but with agent identity that can be dynamic.

Core principles of Agent Identity

To secure agentic workflows, the AGTNCY is building an identity framework tailored to the needs of autonomous, ephemeral, polymorphic, and task-driven agents. This new platform is built on five core principles:

1. Identity as a lifecycle: Agents, like employees or devices, must be onboarded, governed, updated, and retired with traceability and policy enforcement.
2. Audit-ability: People and systems must be able to grant, verify, and audit agent access clearly.
3. Just-in-time task-based permissions: Agent access should shift to identity of user and agent, task intent, context, tool/API involved, and not static roles or keys.
4. Cross-ecosystem interoperability: Open standards like OAuth 2.1 power portable, interoperable identities across systems.
5. Continuous verification: Trust is not a one-time event. Ongoing checks and anomaly detection are essential to mitigate compromise or misuse.

Let’s build AI agent identity together 

We’ve been working with forward-thinking partners like Cisco Duo, Skyfire.xyz, and Permit.io and invite you to join our Agent Identity Working Group—an open forum for developers, security architects, researchers, and builders who believe trust must evolve alongside AI. 

Whether you’re hands-on with infrastructure or exploring the frontier of agent-based systems, your perspective matters.

Read more about what the AGNTCY is building: Documentation.