As companies move from trying AI tools to running AI agents in daily work, a new kind of role is appearing: the person who owns those agents. Titles vary, from AI agent manager or AI operations lead to agent owner or automation lead, and in smaller teams it is part of an existing job. What the role involves is becoming clearer. This page gives a job description template you can adapt, and explains where each responsibility comes from.
The short answer: an AI agent manager sets up, supervises and answers for a group of AI agents. They write each agent's job and limits, decide what needs human approval, measure quality, keep logs, handle incidents and train the people who work alongside the agents. The role combines domain knowledge, process design and accountability more than programming.
Why the role exists
The NIST AI Risk Management Framework calls for roles, responsibilities and lines of communication for AI risks to be documented and clear, and for assigned responsibility to supersede, disengage or deactivate AI systems that do not perform as intended (NIST AI RMF 1.0, GOVERN 2.1, MANAGE 2.4). For high-risk systems, the EU AI Act requires deployers to assign human oversight to people with the necessary competence, training and authority, and to monitor the systems and keep their logs (EU AI Act, Article 26). Someone has to hold those responsibilities. The agent manager is a name for that someone.
Job description template
Job title
AI Agent Manager (alternatives: AI Operations Lead, Agent Owner, Automation Lead)
Purpose of the role
Own the AI agents used in [team or function], so that they do useful work safely, reliably and within company policy and the law, and so that a named person is accountable for what they do.
Key responsibilities
- Agent design: write and maintain a job for each agent: purpose, inputs, allowed actions, actions needing approval, forbidden actions, quality bar and escalation rules.
- Access and permissions: give each agent only the tools and access its job needs, working with IT and security. OWASP identifies excessive functionality, permissions and autonomy as the root causes of agents doing damage (OWASP LLM06:2025 Excessive Agency).
- Human oversight: decide which steps run alone and which need approval; make sure reviewers can understand, override and stop the agents (EU AI Act, Article 14).
- Quality measurement: run test sets before launch, track error rates, severity and rework time, and set written thresholds for widening or narrowing each agent's autonomy.
- Monitoring and logs: make sure agent actions are logged and kept, review them regularly, and watch for changes in performance.
- Incident handling: pause agents when something goes wrong, lead the fix, trace the cause and change the setup.
- Data and compliance: work with legal and data protection colleagues on personal data, transparency to customers and any high-risk uses.
- Training and support: help colleagues work with the agents, including the AI literacy measures the EU AI Act expects of deployers (Article 4).
- Vendor management: assess AI tools and suppliers, and track model and terms changes.
- Reporting: report to [sponsor] on value delivered, quality, incidents and risks.
Skills and experience
- Solid experience in [the business domain], enough to judge whether an agent's output is right.
- Hands-on experience running AI tools or agents on real work.
- Clear written communication: briefs, policies and incident reports.
- Process thinking: breaking work into steps and placing human checks.
- Comfort with basic data: reading error rates, sampling and dashboards.
- Working knowledge of AI risks: confabulation, automation bias, prompt injection and data protection.
- Judgment and a willingness to say no: knowing when not to automate.
Programming is useful but often not required. In the WEF's 2025 survey, analytical thinking is the most sought-after core skill, and AI and big data are the fastest-growing skills (WEF Future of Jobs Report 2025).
Reports to
[Head of function or operations]. Works closely with IT, security, legal, data protection and the teams whose work the agents do.
How success is measured
- Agents in use have written jobs, named reviewers and working stop controls.
- Error rates are measured and within agreed thresholds.
- Time saved, net of review and rework, is measured honestly.
- Incidents are caught early, fixed and followed by setup changes.
- Colleagues know how to work with the agents and how to raise problems.
Tips for hiring or growing into the role
- Look inside first. The best candidates are often people already running agents in their own team who know the work well.
- Give real authority. The role only works if the person can pause or stop an agent without asking permission.
- Avoid making it an IT-only job. Most of the judgment is about the work, not the technology.
- Scale the scope. In a small company this may be a few hours a week; in a large one, a team.
See also introducing AI agents at work, measuring AI agent performance and digital collar careers.
Questions and answers
What does an AI agent manager do?
Sets up, supervises and answers for AI agents: writing each agent's job and limits, deciding what needs human approval, measuring quality, keeping logs, handling incidents and training colleagues.
Does an AI agent manager need to code?
Often not. Domain knowledge, process design, clear writing and judgment matter more. Technical skills help, especially for working with IT on permissions and logs.
Who should an AI agent manager report to?
Usually the head of the function whose work the agents do, with close links to IT, security, legal and data protection.
Is an AI agent manager required by law?
No law names the role. But the EU AI Act requires deployers of high-risk AI to assign competent, trained people with authority to oversee them, and NIST recommends clearly assigned AI responsibilities.
How do you measure success in this role?
By whether agents have clear jobs and stop controls, measured error rates within thresholds, honest net time savings, and incidents caught and fixed early.