IAM for AI Agents: How Identity and Access Management Must Evolve for Autonomous Workflows

Identity and access management was designed around a set of assumptions about the actor it governs. A user logs in occasionally, holds a relatively stable role, acts at human speed, and can be asked to re-authenticate when something looks wrong. Autonomous AI agents violate every one of those assumptions, and that is why bolting agents onto human-era IAM is already producing audit findings and near-misses across the enterprise.

Gartner projects that by 2026 around 30% of enterprises will run AI agents acting with minimal human intervention. When an actor operates continuously, at machine speed, across many systems, and cannot pause to answer a verification prompt, the identity model has to change. The good news is that the direction of travel is now clear.

Where human IAM breaks down

Three properties of agents expose the limits of traditional IAM. First, scale: agents and the sub-agents they spawn create non-human identities far faster than any joiner-mover-leaver process was built to handle. Second, speed: an agent can perform thousands of privileged actions in the time a human performs one, so a single over-broad grant has outsized blast radius. Third, delegation: agents routinely act on behalf of a user or another agent, and the authority being exercised must be carried with the request, not assumed.

Reuse of human credentials makes all three worse. A token minted for a person carries broad context, role, department, application entitlements, that is reasonable for a human and dangerous for an agent. Hand that token to an autonomous process and you have granted standing, wide-scope access to something that never sleeps.

The traditional safety net of asking a user to re-authenticate or approve a prompt also disappears. An agent cannot stop to prove it is still trustworthy, so assurance has to be designed into the credential and the access decision itself rather than added at the moment of doubt. That single difference reshapes how identity must work for autonomous actors.

Agents as first-class identities

The foundational shift, echoed in emerging frameworks such as CoSAI's work on agentic identity, is to treat agents as first-class identities. An agent should have its own identity primitive, its own lifecycle, and its own governance, rather than inheriting a person's account or hiding behind a shared service credential.

First-class identity means an agent is provisioned, governed and de-provisioned deliberately. It can be enrolled, granted a cryptographically strong credential, monitored, and retired, with every step recorded. Crucially, it makes accountability possible: each action traces back to a named agent and to the principal whose authority it carried.

The new access model: scoped, just-in-time, delegated

Once agents are first-class identities, access can be redesigned around them. The pattern the industry is converging on rests on a few principles.

• Least privilege by default. An agent receives only the permissions its current task requires, nothing more.
• Just-in-time, short-lived credentials. Access is granted for the moment it is needed and expires automatically, so there are no standing keys to steal.
• Scoped delegation. Using mechanisms like OAuth token exchange and on-behalf-of flows, a broad human token is swapped for a narrow token bound to one task and one principal.
• Real-time revocation. When behaviour looks wrong, access can be pulled immediately rather than waiting for a token to expire.
• Full auditability. Every authentication, authorisation and delegation is logged so the complete identity and delegation context can be reconstructed.

What this means for choosing an IAM platform

For buyers evaluating identity platforms, the relevant question in 2026 is no longer only "how well does this handle our employees?" It is "is this platform ready for autonomous, non-human actors?" The capabilities to probe are clear: can it issue and govern distinct agent identities at scale, enforce least-privilege and just-in-time access, support delegated and token-exchange flows, revoke access in real time, and produce an audit trail that survives a regulator's scrutiny under regimes from the DPDP Act to MAS TRM guidance?

eMudhra's SecurePass is built around these requirements, combining strong authentication, fine-grained access governance and privileged access controls that extend from human users to the machine and agent identities now multiplying across enterprise workflows.

There is also a sequencing advantage in acting early. Retrofitting identity onto a fleet of agents already in production is far harder than provisioning each agent with a governed identity at the moment it is created. Enterprises that establish agent identity standards now, while deployments are still in the dozens rather than the thousands, set a foundation that scales cleanly. Organisations that modernise IAM for agents ahead of that curve will adopt autonomy with confidence; those that defer will find their identity layer is the bottleneck, and the audit risk, that holds the whole programme back.

Is your IAM ready for AI agents?

eMudhra SecurePass extends least-privilege, just-in-time and delegated access from your people to your AI agents and machine identities.

Talk to eMudhra → https://emudhra.com/en/contact-us