Editor's note — September 12, 2026: Removed false distinctions between agents and applications and qualified the recommendation to deploy agents.
AI Agents Are Becoming the New Unmanaged Endpoint
THE TAKE
The enterprise is rushing into agentic AI with the same dangerous pattern we have seen before: business pressure moves faster than governance. The difference this time is that AI agents are not passive tools. They can take action, use credentials, move data, trigger workflows, and make decisions across systems.
That changes the risk model. A chatbot gives an answer. An agent does work. Once an AI agent can access email, files, ticketing systems, source code, customer records, finance workflows, or cloud environments, it becomes part of the operating model. It needs ownership. It needs identity. It needs limits. It needs logging. It needs a kill switch.
My recommendation is to evaluate each proposed agent on business value and controllability. Where an agent can act on behalf of the organization, its authority and accountability need to be explicit.
THREE THINGS BOARDS SHOULD KNOW
AI agents are software applications with delegated authority — Their tool access and autonomy can create operational, security and reputational exposure.
Check the control gap — Test identity, audit and access controls before moving a pilot into production.
Accountability cannot be outsourced to the model — Every agent needs a human owner, a defined purpose, approved access, and clear evidence of what it did.
THE QUESTION I'M HEARING
"How do we let the business move fast with AI agents without creating another unmanaged technology risk?"
The answer is to stop treating governance as a policy document and start treating it as an operating architecture. Boards should ask management for an AI agent inventory, not a generic AI strategy. Where are agents deployed? What systems can they touch? What data can they read? What actions can they take? Who owns them? How are actions reviewed? How quickly can access be revoked?
That sounds basic, but it is exactly where many organizations are exposed. You cannot govern what you cannot see. You cannot audit what you did not log. You cannot assign accountability after an autonomous system has already made a bad decision, moved sensitive data, or triggered an operational failure.
The practical move is to create an agent control framework before scaling. Start with approved use cases. Require registration for every agent. Give each agent a unique identity. Apply least privilege. Log actions in a way legal, risk, audit, and security can understand. Define what requires human approval. Then scale autonomy only where the control environment proves it can handle the risk.
I would scale agents when management can demonstrate useful results, operating controls and a tested way to intervene.
THE BOTTOM LINE
AI agents are becoming part of the enterprise workforce. Boards should govern them before they become the next unmanaged endpoint.
Source and further reading: Primary reference. Framework guidance is not a statement that a particular law applies to every organization.