AI Agents Need Managers, Too
Treating agents like software misses the operating problem. Treating them like unmanaged workers creates risk.
An AI agent is not an employee. It does not have judgment, loyalty, context, ethics, or accountability in the human sense.
But once an agent is assigned work, it becomes part of the workforce system. That means it needs management infrastructure.
The mistake is to treat AI agents as a technology deployment only. A company would never hire a team, give it access to client data, ask it to produce work product, and then skip role definition, supervision, quality control, and escalation paths. Yet many companies are at risk of doing the AI equivalent.
A practical agent-management model should answer six questions before deployment:
- Who owns the agent?
- What work is it allowed to perform?
- What data can it access?
- What decisions require human approval?
- How is output quality measured?
- When something goes wrong, who is accountable for remediation?
The ownership question matters most. Every agent should have a business owner, a technical owner, and a risk owner. Those may be separate people, but they cannot be imaginary people. The business owner defines the job to be done. The technical owner maintains performance and access. The risk owner monitors legal, compliance, privacy, bias, and client-impact concerns.
Agents also need performance reviews, but not in the theatrical sense. The review should be evidence-based:
- Task completion rate
- Error rate by task type
- Human override frequency
- Escalation quality
- Time saved or capacity created
- Risk events or near misses
- User trust and adoption
The point is not to anthropomorphize AI. The point is to operationalize it.
If an AI agent can affect work, clients, employees, or decisions, then it belongs inside the management system. It needs a role, a manager, a scorecard, and boundaries. Without that, the company has not built an AI workforce. It has created invisible capacity with unclear accountability.