What Enterprises Need to Govern Before Giving the Keys to Autonomous Agents

Agentic artificial intelligence requires rethinking identity, access, security and accountability.

David M. Chapman is vice president of the Central region for CDW's corporate business.

Businesses have spent the past few years learning how to govern generative artificial intelligence — AI that creates content and answers questions. But now we’re entering the age of agentic AI, and it’s a whole new ballgame.

Acting on their own, AI agents can access systems, make decisions and execute tasks. In that world, the question changes from “Can we trust this output?” to “Can we trust this agent’s actions?”

I’m seeing organizations move from AI experimentation toward workflows in which agents can interact with business applications, retrieve data, initiate processes and coordinate with other agents. That can deliver enormous productivity gains. But it also means we are effectively creating a new class of digital actors inside the enterprise.

And like any other actor, an AI agent needs an identity, defined permissions and clear boundaries. We can’t treat an agent like just another software feature. If it can access sensitive data or take consequential actions, we need to understand what it can do, why it can do it and who is responsible for the outcome.

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The Importance of Setting Boundaries For Humans and Agents

The answers start with familiar security principles, particularly identity.

That becomes more complex in an agentic organization, which must distinguish among the employee initiating an action, the agent performing it and the systems being accessed. We also need to think carefully about delegation.

It’s vital, though, because (again, just as with humans) an agent should have access only to the systems, data and actions necessary for its job. That chain of accountability needs to be visible.

Not every action requires human approval. Scheduling a meeting is different from approving a purchase or changing a customer record. Organizations need clear thresholds that determine when a human stays in the loop.

But human oversight doesn’t mean approving every AI decision. If we put a person in front of every action, we’ve simply recreated the manual process we wanted AI to eliminate. An agent should be able to operate within defined boundaries and escalate when it encounters an exception, reaches a risk threshold or faces a decision that requires human judgment.

IT teams also need visibility into what agents are doing — not just what systems they access, but what they attempt, what decisions they make and whether their behavior remains within policy.

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Keeping Humans In the Loop With Agentic AI

Ultimately, humans need to become stewards of AI. The steward understands what an agent is supposed to accomplish, what boundaries it operates within and whether its behavior and outcomes make sense. The steward steps in when an agent encounters an exception, reaches a predefined threshold or produces an unexpected result.

An AI steward doesn’t need to watch an agent every second. We don’t want people micromanaging autonomous systems. We want them positioned to make the decisions that actually require human judgment.

That could mean reviewing an agent’s performance, changing its permissions as circumstances evolve or deciding that a particular process should no longer be automated. It also means someone must have the authority to pause or shut down an agent when it behaves outside expectations.

Humans won’t compete with agents on speed or volume.

Our value will increasingly come from judgment, context and accountability — knowing when the rules don’t tell the whole story. The goal isn’t to keep AI from acting on our behalf. It’s to make sure we understand the authority we’re giving it and have the right human structure around that authority.

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