Agentic AI adoption has grown faster than a few could have predicted, and even fewer were prepared for. It started with basic applications like copilots and assistants, and has since blossomed into autonomous systems that take action independently and operate continuously with little human oversight. 

Still, the promise of new technology is almost always met with equally daunting challenges, and in this case, the fundamental weakness in modern platforms is how identity is enforced. 

Most infrastructure was built on the basic assumption that identities are either human users or machines. The thing about AI agents is they’re not human, but they also don’t behave like traditional machine identities. They run continuously, make non-deterministic decisions, and dynamically interact across MCP servers, APIs, databases, orchestration systems and internal services. They don’t log in and log out; they behave more like always-on digital actors embedded directly in production infrastructure. 

But the result is a growing identity gap that can stifle the ability to safely scale agentic AI in production. 

Identity Needs to Be a First-Class Prerequisite 

The core problem is treating identity as an afterthought. 

Before deploying agentic systems into production in any meaningful way, you need to ensure their environments are in a trusted state. That starts with a simple principle: no anonymity where AI operates. Full stop. Why? Because you want to contain your AI agent, which means preventing it from interacting with your existing infrastructure resources: databases, logs, API endpoints, filesystems, and so on. But you can’t protect what’s anonymous. An effective policy for AI must explicitly connect agentic privileges and all other actors present in a computing environment where an AI agent is deployed. 

All actors must have an identity, meaning every: 

• Human 

• Workload 

• Server 

• Database 

• Laptop or desktop 

• Microservice 

• AI agent 

• MCP server and tool endpoint 

If there is anonymous software or hardware in your environment, you can’t expect to reliably contain or govern non-deterministic systems. You simply can’t enforce policies when you have unknown actors. Likewise, you can’t investigate incidents if you don’t know who or what performed the actions. 

This is why agentic AI, and by that I mean any non-deterministically behaving microservice, can only be contained when deployed into an infrastructure that has a built-in identity plane, or a foundational layer that treats all digital actors as first-class identities under a single, consistent model, enforced at the infrastructure level rather than bolted on afterward. 

Once everything is identified, you can effectively enforce guardrails. Agents have the exact privileges they’re allowed to have, and if non-deterministic behavior goes beyond the approved boundaries, you can constrain or revoke access in real time. 

Agentic AI Multiplies the Identity Risk 

The fact that agentic systems can act autonomously is what makes them so powerful. But that autonomy also dramatically expands the non-human identity surface area. Each agent introduces: 

• Access paths into production systems 

• Trust relationships across APIs and tools 

• Orchestration layers and execution contexts 

• Opportunities for privilege creep and misconfiguration 

AI agents aren’t static, so they might not always approach the same task in the same way. They may discover new tools or dynamically create new access paths. From an identity and access perspective, the environment constantly changes. 

This makes agents a fundamentally different class of digital actor—neither human nor script, but something that behaves continuously and sometimes unpredictably at machine speed and scale. 

The Real-World Consequences Are Already Here 

We’ve already seen what happens when this new identity gap is ignored. 

Agents with broad, static privileges are high-value targets. Once they’re compromised, they give attackers persistent, privileged access that’s difficult to detect and contain. 

Shadow MCP servers and unmanaged tool endpoints are appearing across environments, often without centralized visibility or governance, and those endpoints can leak data, secrets and sensitive context. 

Meanwhile, LLM usage is increasing as security teams struggle to reliably discover agents, trace their actions or investigate incidents. There’s no repeatable playbook for platform teams when it comes to deploying agents safely across environments. 

As usual, it’s being driven by organizational pressure. Executives want AI to quickly unlock productivity gains, leaving security and platform teams to manage systems that act continuously, make decisions and rarely “log off.” 

In other words, there’s a widening gap between deployment urgency and identity readiness. 

Why Retrofitting IAM for Agents Fails 

A somewhat natural reaction from many organizations is to try to secure agents using the legacy IAM and PAM tools and models they have in place. But these tools weren’t made for autonomous, continuously operating actors. Traditional identity systems assume: 

• Identities are either human or machine 

• Access is relatively static 

• Credentials are long-lived 

• Behavior is predictable 

• Sessions are discrete and auditable 

Those assumptions clearly don’t apply to agents. 

An agent isn’t a human, but it also doesn’t behave like a service account. It runs around the clock, continuously creating new access paths. And to be useful, it often requires privileged access across multiple systems. 

Trying to govern this behavior with legacy IAM and PAM tools becomes operationally brittle and increasingly infeasible at scale. You can’t retrofit identity controls after agents are already in production. It only creates more problems: policy sprawl, inconsistent enforcement and blind spots that attackers can exploit. 

We’ve referred to this mismatch as an agentic identity crisis: today’s identity tools and models were built for a world that no longer exists. 

Moving From Identity Types to Identity Behavior 

As AI systems begin to behave and respond to context more like humans, the distinction between human and non-human identity is becoming irrelevant. It doesn’t matter what an identity is; what matters is how it behaves. 

Organizations need to govern this based on: 

• The resources an actor can access 

• Why it has that access 

• How its behavior changes over time 

• Its potential blast radius 

• How quickly access can be adjusted or revoked 

While it sounds relatively straightforward, it’s a significant shift that turns identity into an engineering discipline. You want programmable security, dynamic access, and continuous auditability. 

How Do You Contain Risk in a Non-Deterministic World? 

A common concern I hear from organizations isn’t just external attackers; it’s their own AI misbehaving. 

There are loads of moving parts to account for. As teams deploy hundreds or thousands of agents to process data, update systems, and take actions, the new fear is that an agent will do something it wasn’t intended to do. And this is where a strong identity foundation matters most. 

When every agent is identified, access is granted dynamically with no standing privileges, and actions can be attributed, you minimize the potential damage that can be done. You can stop agents when they overstep, trace their actions and audit their behavior. 

Without this foundation, every other control becomes theater. 

Identity Will Decide Whether You Can Scale Agentic AI 

With agentic AI no longer experimental, winning organizations will be those that build identity foundations that allow agents to operate safely, visibly and with a controlled blast radius. 

If you treat identities as silos, you’ll find yourself constantly reacting. You’ll be chasing shadow tools, cleaning up over-privileged agents, and investigating incidents you might not even be able to fully trace. 

But if unified identity is treated as a prerequisite, agentic systems can scale with confidence. In the era of autonomous software, identity isn’t a supporting control. It’s part of the infrastructure that makes agentic AI viable.