Why Traditional Identity Governance Struggles to Track AI Agents

Identity lifecycle management was built for people: employees who are hired, reassigned, and eventually offboarded through HR processes. As organizations deploy more autonomous AI agents, security teams are finding that this long-established model does not fit a new class of non-human principals.

A system designed around human employment records

Conventional identity governance and administration (IGA) tools assume that every identity has a manager, a job title, and a clear relationship to a workforce system such as Workday, SAP SuccessFactors, or ServiceNow HR. A new hire triggers provisioning, a promotion changes entitlements, and a termination event starts deactivation workflows. These steps create the audit trail used to support compliance requirements and access reviews.

That structure works because human access usually follows predictable patterns. Role-based access control can map a job function to a known set of permissions, while certification campaigns and segregation-of-duties checks provide additional oversight.

Why AI agents do not fit the lifecycle model

AI agents are created in a very different way. They are often launched by developers, automation platforms, or orchestration frameworks rather than HR systems. Instead of inheriting access through a formal joiner-mover-leaver process, they may be provisioned with service accounts, API keys, or OAuth grants at the time they are built.

Once running, these agents can behave in ways that are difficult to predict. An agent designed for one task may expand its reach by calling tools, retrieving data, or chaining actions across multiple systems. That runtime growth in scope is hard for identity tools to interpret, especially when the agent is treated as a static machine account rather than an autonomous actor.

Governance gaps that emerge at scale

The article also notes that agents can operate in parallel across cloud services, containers, and SaaS platforms, sometimes with separate credentials and session contexts. In multi-agent setups, one agent may even delegate work to another, making it even harder to understand who or what is using access at any given moment.

For security teams, the concern is not just provisioning, but visibility and control over how access changes during execution. Traditional IGA products were not designed to track principals that can fork, multiply, and interact with systems outside a fixed job description.

• Human-centric lifecycle models depend on HR events.
• AI agents are often created outside governed onboarding workflows.
• Runtime behavior can expand access beyond initial permissions.
• Existing IGA tools may miss agent sprawl across environments.

As AI agents become more common in enterprise environments, the article argues that identity governance will need to evolve beyond assumptions built for human employees.