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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11AI agents should be managed through shared oversight, not assigned wholesale to either IT or HR. IT or engineering should run the technical foundation; the business team using an agent should own its work outcomes and day-to-day review; HR should help when deployment changes jobs or performance expectations; and a cross-functional group should coordinate policy, risk, and learning.
Why AI agent management is a shared responsibility
An agent may rely on IT-managed systems and permissions, perform work designed by a business team, and affect employees’ roles or expectations. Those are different responsibilities. Putting them under one department can leave important questions unanswered: who decides whether the agent is doing useful work, who can change or stop it, and who responds to its effects on employees?
Nicholas D. Evans made this organizational question central to his August 11, 2025, CIO article, framing agent management as both orchestration and governance. Related perspectives likewise distinguish IT’s technical work from HR’s workforce concerns and the domain experts’ role in monitoring an agent after deployment. These are complementary recommendations, not evidence of a single required organization chart.
Four practical thoughts on who should manage AI agents
1. Connect technical orchestration with governance
Agents need technical controls as well as operational, ethical, and legal oversight. IT or engineering should manage the platform and its technical operations, including access, integrations, deployment, monitoring, and incident response. Governance should connect those controls to decisions about acceptable use, risk review, accountability, and escalation.
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2. Give the business function ownership of the work
The department using an agent should define what it is meant to accomplish and judge whether its behavior is useful in the actual workflow. Subject-matter experts are best placed to spot errors that may look technically valid but fail the needs of the work. Tatyana Mamut’s interview on post-deployment operations emphasizes that functional experts should be able to monitor, correct, and improve agents, while engineering or IT can build and deploy them (interview).
Business ownership does not replace technical control. It means the people accountable for the outcome have a meaningful role in evaluating performance and raising problems, rather than treating deployment as the end of their involvement.
3. Involve HR when agents change work
HR should take part when agents affect job design, responsibilities, hiring, training, employee experience, or performance expectations. Evans recommends involving HR in defining digital roles, setting expectations for agent performance, preparing the workforce, and shaping governance strategy. A related Fast Company Executive Board article also describes IT and HR oversight as shared work, with HR attentive to workplace dynamics and human-AI collaboration.
This does not make HR the operator of every agent. Its role is most relevant where agent use changes how people work or how their work is assessed.
4. Coordinate through a cross-functional governance group
A cross-functional AI center of excellence or governance group can connect technical controls, business ownership, and workforce considerations. Evans recommends expanding an existing AI, machine-learning, or generative-AI center of excellence to include agentic AI. He also suggests global business services as a possible home in organizations where that group serves multiple functions, including HR and IT.
The group’s value is coordination: establishing common policy, arranging risk review, monitoring issues, supporting escalation, and sharing what teams learn. Evans’s call to make governance a “race to the top” is a recommendation to build practices that help organizations scale safely and effectively, not a statement of a regulatory requirement.
How to divide ownership across an agent’s lifecycle
Use responsibility boundaries that make ownership explicit rather than relying on a department label. A practical division looks like this:
| Responsibility | Primary owner | What that means |
|---|---|---|
| Platform and technical operations | IT or engineering | Manage infrastructure, identity and permissions, integrations, deployment, monitoring, and technical incident response. |
| Work outcome and domain behavior | Business function using the agent | Set the intended outcome, assess whether behavior works in the real workflow, and flag or correct domain failures. |
| Workforce effects | HR, with the affected business function | Address role definitions, training and readiness, employee concerns, and performance expectations when work changes. |
| Cross-organization governance | AI center of excellence or cross-functional governance group | Coordinate policy, risk review, monitoring, escalation, and shared learning across teams. |
Before deployment, the business owner should be clear about the outcome and how the team will judge it; IT or engineering should establish the technical controls; HR should be included if the change affects work; and the governance group should coordinate review where needed. After deployment, the business function needs a route to monitor and correct behavior, while IT or engineering maintains the technical foundation and handles technical issues.
Write down who can approve changes, who can pause or stop an agent, and who must be notified when it fails. The cited perspectives support shared responsibility but do not prescribe a universal approval or escalation procedure, so organizations need to define those decisions for their own risks and workflows.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing a management model
Organizations can centralize coordination, distribute operational ownership, or combine the two. Compare the options against accountability, technical control, workforce impact, domain expertise, and the ability to apply consistent controls across departments.
| Model | Where it can help | Trade-off to manage |
|---|---|---|
| Centralized AI office | Provides a visible place to coordinate policy, risk review, and shared practices. | Can be too far from day-to-day work unless business experts retain authority to review outcomes. |
| Federated business ownership | Keeps monitoring and improvement close to the people who understand each workflow. | Needs coordination so controls and governance do not become inconsistent across departments. |
| Hybrid shared oversight | Combines common governance and technical foundations with business and HR participation where relevant. | Requires clear boundaries so teams know who owns outcomes, technical operations, workforce effects, and escalation. |
The hybrid model best matches the responsibilities described across the cited sources: common oversight does not displace domain ownership, and technical responsibility does not make IT the judge of every business outcome.
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What the available figures suggest—and what they do not
The 2025 CIO article reports that 33% of organizations had deployed at least some AI agents, up from 11% in each of the two preceding quarters, citing KPMG’s AI Quarterly Pulse Survey. It also reports that nearly nine in ten leaders think agents will require organizations to redefine performance metrics. These figures are attributed to KPMG as reported by CIO; they were not independently checked against KPMG’s original survey and should not be treated as universal rates.
The reported concern about performance metrics reinforces why responsibility cannot stop at deployment: organizations need to consider both agent performance and the effects on employee roles and expectations.
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