The newest member of the corporate workforce never sleeps, never asks for a raise, and can be cloned before lunch. It can also confidently make the wrong decision thousands of times before anyone notices.
That helps explain why some technology leaders have started talking about AI agents as employees, co-workers, or interns rather than software. Once an agent can update records, issue refunds, send communications, or act across multiple enterprise systems, deploying it no longer looks much like installing another application.
Recent Harvard Business Review articles have pushed that analogy further. One argues that agents should be treated like a new workforce, with defined roles, authority, sources of truth, supervision, and audit trails. Another recommends giving agents names and job descriptions, onboarding them, evaluating their performance, and increasing their responsibilities after they prove themselves.
But there is a catch: People may start believing the metaphor.
A separate HBR study of 1,261 managers found that when AI was framed as an employee rather than a tool, managers with experience working around AI agents took less personal responsibility for results and assigned more responsibility to the AI. They also escalated more problems and caught fewer errors. In the study, 31% of respondents said their companies already framed AI as a teammate or employee, while 23% said agents appeared on organizational or work charts.
For IT leaders, the answer may therefore be to borrow freely from the employee-management playbook — without extending the metaphor too far.
Don’t put software on the org chart
Amy Loomis, group vice president at IDC, draws a hard line around one word: responsibility.
Humans, she says, have obligations to their companies and to one another, and they bring an understanding of corporate culture and values that an agent can mimic linguistically without possessing.
That makes calling an agent a co-worker potentially more than harmless anthropomorphism. The label suggests a peer relationship and can encourage people to cede judgment to a system that’s very good at giving what was requested, Loomis says, but not necessarily at recognizing what was actually needed.
“Accountability is something that has qualities associated with it that are uniquely human,” she says. “Agents may be given permission to access data, but the accountability for what they do with that access lies with the person who gave the agent access, not the agent itself.”
That distinction still leaves plenty to borrow from employee management. It makes sense to give an agent traceable access and tightly scoped permissions — and to institute a kill switch to prevent the agent from having continued access. But Loomis finds it more effective to think in terms of giving an agent an operations charter rather than a human identity: define what the system does, what it may access, and what humans remain responsible for.
In other words, give the agent a security badge. Just don’t put its picture on it.
Raja Iqbal, founder of agentic AI governance company Ejento AI and co-author of the HBR article “To Scale AI Agents Successfully, Think of Them Like Team Members,” is closer to Loomis than the headline suggests.
Iqbal says the metaphor is intended to put CIOs, CISOs, and other technology leaders into the right “cognitive posture.” Thinking about an agent as a team member, he argues, can remind an enterprise to impose familiar management disciplines: a unique identity, clear human oversight, defined authority, spending limits, approved information sources, and an audit trail.
If another term produces the same discipline, he’s fine with that too.
In fact, Iqbal explicitly rejects synthetic employees with human personas and seats on the org chart. That creates exactly the accountability problem critics worry about: “Bob did it” becomes an excuse even though Bob is software.
“The metaphor is dangerous when it’s decorative,” Iqbal says. By that, he means companies get into trouble when they adopt the human trappings — names, personas, or the language of colleagues — without also imposing the supervision, limits, and accountability that make the analogy useful.
His preferred distinction is simpler: the agent needs a technical identity so the enterprise can reconstruct what it did, while a human must remain responsible for those actions. “The identity is the audit primitive, and the human owner is the accountability primitive,” he says. Put more plainly, the agent identity answers what acted; the human owner answers who is accountable.
The employee analogy becomes more useful again when deciding how much freedom an agent should receive. Iqbal recommends graduated autonomy. An agent might begin with a human approving every action, progress to performing low-risk actions independently, and eventually receive bounded autonomy if its observed performance justifies it. A serious error can send it back down the ladder — or out the door entirely.
That sounds a lot like a probation period for a new employee. It’s also simply good software governance for a probabilistic system.
At DXC, professional agents are software
Russell Jukes has had plenty of opportunity to test that distinction. As chief digital and information officer at DXC Technology, he oversees AI in an IT services organization with roughly 115,000 employees.
“We think about [an AI agent] as a superpower, not as a person,” he says.
DXC separates agents into two categories. Personal agents work for individual employees and generally operate using the employee’s identity and access. Professional agents are enterprise systems built for broader workflows. On one of DXC’s major AI platforms, employees have so far created roughly 8,000 personal agents, compared with about 100 professional agents.
The professional variety receives much stricter oversight. It can have its own nonhuman identity, credentials, permissions, and authority boundaries. Activity passes through control planes that allow DXC to see what agents are doing and, when necessary, shut them down.
Jukes thinks many of these agents will eventually disappear from users’ view altogether. An agent might scan internal resource requirements overnight and recommend employees for projects. Nobody needs to greet it in the morning or consider it a colleague; they simply receive the result.
That’s where he parts company with the worker metaphor.
Jukes says he learned early on that AI cannot be deployed like conventional SaaS. His first attempt followed the familiar software model: make the application available, let employees use it within the prescribed workflow, and expect adoption to follow. “I did that, and nobody used it,” he says. The experience convinced him that AI requires a different approach, and he vowed to “never deploy AI like it’s SaaS again.”
Conventional SaaS is largely deterministic: the software constrains users to a defined workflow and produces predictable outputs. Agentic AI is different. It can interpret a goal, choose among tools and data sources, and decide how to get to an outcome. For Jukes, that means CIOs have to design the policies, permissions, data access, guardrails, and authority around the agent rather than simply deploy another application.
That also creates a clearer chain of responsibility. If a person approves the agent’s action, Jukes says that person owns the decision. If an autonomous agent behaves according to a business policy, accountability belongs with the people responsible for that policy. If the technology fails to operate as designed, responsibility moves to IT.
The agent itself never gets handed the blame.
Maybe treat it like an intern after all
Nina Tatsiy, global CIO of Quadient, an automation technology company, takes a position somewhere between Iqbal and Jukes. She calls agents interns, but deliberately uses the term as an analogy, not an organizational designation.
Agents must be managed as software, with security, governance, and technical controls, while recognizing that their probabilistic behavior requires additional supervision. “You have to do both,” Tatsiy says.
An intern does not receive every password and authorization on the first morning. Neither should an agent. It has to be onboarded, trained, watched, retrained when its environment changes, and eventually retired when it’s obsolete.
Quadient generally avoids giving agents human names. Tatsiy worries that doing so encourages people to see them as real colleagues capable of judgment.
“Overtrust is a problem,” she says, particularly because AI tends to state conclusions with such assurance. Tatsiy prefers using AI to challenge a hypothesis or surface questions people haven’t considered rather than allowing it to make the conclusion for them.
Yet the intern analogy has proved useful operationally.
In one accounts-payable deployment, Quadient had an agent effectively shadow human workers and learn from their feedback across different scenarios. Only after its performance had been tested across increasing levels of complexity did it begin providing recommendations. Humans still made the final decision.
The agent, in other words, earned more trust. It didn’t earn accountability.
Borrow from HR, but keep humans in charge
The emerging divide may therefore be less about whether agents are employees or software than about which parts of each management model IT leaders should retain.
Agents need individual technical identities, but not human identities. They need defined responsibilities, but not jobs in the human sense. They need monitoring and evaluation, but not annual performance reviews. They can receive progressively greater authority, but not progressively greater accountability.
And the stronger they become, the more important that distinction becomes.
Treating an autonomous agent like ordinary SaaS ignores the fact that it can interpret, choose, and act in ways conventional software cannot. Treating it like Steve from Accounting creates a different problem: sooner or later somebody may start assuming “Steve” knows what he’s doing.
IT leaders may indeed need to borrow heavily from HR as agentic AI spreads across the enterprise. They just shouldn’t start issuing employee badges.