TL;DR: AI agents are being deployed across the enterprise faster than any traditional governance model can absorb them. Each one is a non-human identity with real access to real systems. The volume, velocity, and complexity of these identities have made manual IGA onboarding economically impossible at scale. The only viable answer is using AI-powered governance to keep pace: automating the discovery, integration, and lifecycle management that human teams simply cannot sustain.

A New Class of Identity Is Outpacing Traditional Governance

Something important happened over the last eighteen months. AI agents stopped being a pilot program and became infrastructure.

Today, organizations are deploying agents that read email inboxes, update CRM records, query databases, trigger workflows, and make API calls to production systems, and they do so autonomously, at scale, and often without a ticket being opened or a review being run. Business units adopt them through SaaS vendors. Engineering teams spin them up through internal platforms. Procurement signs contracts that include agentic capabilities in the fine print.

The velocity is real. So is the governance challenge it creates.

Most IGA programs were designed around a human-centric model: employees who join, move, and leave; access that is provisioned, reviewed, and revoked on a lifecycle tied to HR events. That model worked reasonably well when the identities that mattered were people. However, it has no answer for identities that were never hired, never onboarded, and will never appear in an HR system.

AI agents are not users in the traditional sense. They do not have manager relationships or org chart positions. They do not go through a standard joiner-mover-leaver process. But they access the same systems, operate on the same data, and carry the same risk profile as any other privileged identity. And unlike human identities (which grow roughly in line with headcount) agent identities can multiply overnight every time a vendor ships a new integration or a team adopts a new platform.

Traditional IGA onboarding, which still takes weeks per application in most organizations, was never built for this rate of change.

What an AI Agent Looks Like as an Identity

Before examining the governance challenge, it helps to be precise about what we are actually managing.

An AI agent is a software process that takes actions in external systems (reading, writing, triggering, or orchestrating) in service of a goal. It is typically granted access through an API key, a service account, an OAuth token, or a delegated credential. It may operate continuously or on demand. It may be vendor-managed, internally built, or somewhere in between.

What it almost certainly is not: onboarded into your IGA platform.

Service accounts and API keys are not new problems. Non-human identity management has been an underserved area in IGA for years. What AI agents introduce is a dramatic acceleration of the underlying challenge. Where a legacy service account might have been created once and then largely forgotten, agent identities are being created constantly by vendors updating product features, by developers scaffolding new automations, by business users following a "connect your tools" flow in a SaaS product adopted last month.

The volume is different. The rate of change is different. And the access scope is often broader than anything a traditional service account carried, because agents are designed to act across multiple systems as part of their core function.

This is exactly the environment where traditional IGA breaks down and where AI-powered governance becomes not a nice-to-have, but a prerequisite.

Why the Traditional Onboarding Model Cannot Keep Up

The average enterprise application onboarding project still takes six to eight weeks, requires a team of consultants, and involves repeated back-and-forth with application owners and IAM teams. That timeline made governance challenging even before AI agents entered the picture.

Add agents to the mix and the math collapses entirely.

An enterprise that deploys a new AI-powered platform may introduce dozens of new integration points in a single procurement decision. A vendor update can change an agent's permission scope without any change request being raised. A business unit that adopts a new automation tool may create, modify, and retire agent credentials on a cadence that makes quarterly access reviews irrelevant before they even run.

The traditional model that includes human-driven connector development, manual onboarding projects, consultant-heavy implementation, simply cannot operate at that speed. It was not designed to.

The result is predictable: organizations govern the identities they can reach with the tools they have, and defer everything else. Coverage stays narrow. The backlog grows. And the gap between what the IGA program is supposed to govern and what it actually governs widens every quarter.

The Governance Questions That Still Need Answering

The fact that AI agents are hard to govern with traditional methods does not make governance optional. The fundamental questions still need answers, they just need to be answered faster and at greater scale than any manual process can support.

Who owns this agent's access? Every agent identity needs a human owner who is accountable for the entitlements it holds, can answer access review questions, and initiates deprovisioning when the agent is retired or replaced.

What can it actually do with the permissions it holds? Agents often request broad access upfront because their capabilities may expand over time. Understanding the real entitlement footprint and not just what was provisioned, but what is actively used, is essential for right-sizing access and reducing exposure.

When was this access last reviewed? An agent with broad access to production data that has not been certified in eighteen months carries the same residual risk as any other stale entitlement. The certification requirement does not disappear because the identity is non-human.

What happens to credentials when the vendor updates the integration or the business use case changes? Unlike human identity lifecycle events, which surface through HR systems, agent lifecycle events are often invisible to the IGA platform. Credentials can persist long after the use case that created them has changed or ended.

These are not new governance questions. They are the same ones that every mature IGA program asks about human identities. The challenge is building the operational model to ask them (and act on the answers) at the velocity that AI agents demand.

Why AI-Powered Governance Is the Right Answer

If the problem is that traditional, manually-intensive IGA cannot keep pace with the speed and volume of AI-driven access changes, the solution is not to work harder within the same model. It is to change the model.

AI-powered governance closes the gap by bringing the same speed and automation to identity administration that AI agents bring to business processes. Where a traditional connector development project takes weeks, AI-assisted integration design takes days. Where manual onboarding requires deep involvement from consultants and application teams, AI-driven onboarding can discover, map, and integrate access structures with minimal human overhead. Where access reviews depend on reviewers processing hundreds of entitlements without meaningful context, AI can surface usage patterns, risk signals, and peer benchmarking that make certifications produce real decisions rather than rubber-stamp approvals.

This is not about replacing the governance framework. Least privilege, lifecycle management, visibility, and access certification are still the right foundations. It is about making that framework operable at a scale and speed that the modern enterprise environment requires.

The organizations that will achieve full IGA coverage — across human and non-human identities, across legacy applications and new AI-powered platforms, across the entire application estate rather than just the highest-visibility slice of it — are the ones that bring AI to the governance layer, not just to the business layer.

The Window for Getting This Right Is Narrowing

AI agent adoption is not slowing down. Every quarter, the number of non-human identities in the average enterprise grows. Every quarter without a governance model in place, the access debt compounds.

Organizations that have already struggled to achieve full IGA coverage for their human identity population are now facing an additional identity category that is growing faster and is even less visible in traditional tooling. The organizations that move early on AI-powered governance will find the problem tractable. The ones that wait will find themselves managing a backlog that makes today's application onboarding challenges look manageable by comparison.

The good news is that the principles have not changed. What has changed is what it takes to operationalize them. The IGA programs that keep pace with the modern enterprise will be the ones that use AI to govern AI — and in doing so, finally deliver on the promise of full coverage that the industry has been chasing for years.

Klyro is an AI-powered IGA integration platform that reduces application onboarding from weeks to days and delivers consistent governance coverage across your full identity estate — including the AI-driven systems and non-human identities that traditional connectors leave behind. Talk to an expert →