For years, companies have treated software as something employees use to get work done. That assumption is starting to change.
AI agents can now do more than generate an answer or summarize a document. They can interact with applications, retrieve information, execute tasks, make decisions within defined workflows, and hand work from one system to another. As enterprises move from experimenting with AI to putting agents into everyday operations, a new question is becoming difficult to ignore: who manages an AI agent when it starts behaving more like a worker than a tool?
The answer is not as simple as assigning ownership to the IT team.
AI Agents Are Moving Into the Workforce
The shift from AI assistants to AI agents is important because the level of independence changes.
A chatbot generally waits for a person to ask something. An agent can be given a goal and carry out multiple steps to achieve it.
For example, an AI agent could:
- Review incoming customer requests
- Retrieve information from internal systems
- Create or update records
- Route tasks to another department
- Generate reports
- Trigger business workflows
- Work across several applications
This makes an agent part of an operational process rather than simply another productivity tool.
Recent enterprise research describes this transition as AI moving from assistance to execution, with agents spreading across knowledge work.
And once an AI system can act, questions about ownership become much more important.
An AI Agent Doesn’t Have a Job Description
An employee has a defined role, manager, access level, responsibilities, and escalation path.
An AI agent usually doesn’t.
A company may deploy an agent for customer support, another for finance, another for software development, and another for internal research. Different teams may configure them independently and give them access to different systems.
Without clear governance, nobody may have a complete picture of:
- Which agents exist
- Who created them
- What each agent is allowed to access
- What systems they can interact with
- What decisions they can make
- Who reviews their actions
- What happens when they make a mistake
That creates a management problem before it becomes a technical one.
Who Should Manage AI Agents?
There probably won’t be one universal answer.
IT may manage the infrastructure. Security teams may manage access and technical risk. Legal and compliance teams may define requirements. Business teams may own the workflows where agents operate.
Leadership, however, still needs to establish who is ultimately accountable.
This is why AI governance cannot sit entirely inside one department. Gartner has warned that applying the same governance approach to every AI agent can itself create problems because agents operate with different levels of autonomy and access.
A simple document saying “AI is approved” isn’t enough.
Companies need to understand what each agent is actually doing.
The Real Issue Is Access
The biggest management question may not be what can the agent do?
It may be:
What can the agent access while doing it?
An agent connected to a public knowledge base presents a very different risk from one connected to customer records, financial systems, employee information, source code, or confidential business documents.
The more systems and information an agent can reach, the more important it becomes to understand its boundaries.
This is where traditional approaches can become difficult. Companies have spent years managing access for employees and applications. AI agents introduce another type of actor that can operate quickly, continuously, and sometimes across multiple systems.
PwC’s 2026 research similarly highlights the need for AI agents to have verified identities, defined roles, task-specific permissions, and auditable records.
What Happens When an Agent Makes a Mistake?
Imagine an AI agent sends the wrong information to a customer.
Or updates the wrong record.
Or accesses information it wasn’t supposed to use.
Or makes a decision based on outdated information.
Who is responsible?
The agent cannot take responsibility. The organization deploying it ultimately has to answer for the outcome.
That means businesses need clear escalation and review processes before agents are given significant autonomy.
Human oversight also shouldn’t necessarily mean someone watches every action. The level of human involvement should depend on the potential impact of the task.
A low-risk administrative task may need little intervention. A decision involving financial, legal, employment, or personal information may require a human checkpoint.
Companies Also Need to Know What Their Agents Can See
There is another issue that can easily be overlooked: data visibility.
Before an organization can decide whether an AI agent should have access to information, it needs to understand what information exists in the first place.
A company may have sensitive information spread across databases, cloud storage, business applications, documents, and other environments. If that information hasn’t been properly discovered and classified, creating sensible access boundaries becomes much harder.
This makes data discovery and classification relevant to AI governance as well.
Platforms such as EzSecure can help organizations discover and classify sensitive information across their environments, giving teams a clearer understanding of what information exists before deciding how it should be accessed or used.
AI Governance Is Becoming an Operating Model
The bigger change is that AI governance can no longer be treated as a policy document created once and forgotten.
As agents become part of everyday workflows, organizations need an operating model around them.
That means answering practical questions:
Who approves an agent?
Who owns it?
What data can it access?
What actions can it take?
When does a human need to intervene?
How are its actions recorded?
What happens when its role changes?
These questions become increasingly important as organizations deploy agents across departments and platforms. BCG’s recent enterprise guidance points to the need for centralized visibility, identity, policy enforcement, and governance as AI agents scale.
The Future May Have Humans Managing a Digital Workforce
The idea of AI agents as part of the workforce may sound futuristic, but organizations are already discussing where these systems belong within operating structures. KPMG recently framed the issue directly: as agents enter the workforce, companies need to reconsider who manages them and how responsibility should be structured.
The important point isn’t whether we should literally call AI agents “employees.”
It is whether companies are prepared to manage them with the same seriousness they apply to other actors that can access information, perform work, and affect business outcomes.
AI agents may not need an employee ID card.
But they increasingly need an owner, defined permissions, clear boundaries, oversight, and an audit trail.
And the companies that establish those foundations early will be in a much stronger position to scale AI without losing control of what their systems can access or do.
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