Something interesting is happening inside modern organizations. AI agents are quietly multiplying across departments. They book meetings, respond to customers, analyze data, and trigger workflows. Nobody planned for this. It just happened.
The problem? Most companies treat AI agents like software tools. Install, run, forget. But these agents make decisions. They hold access to sensitive systems. They interact with real people. That sounds a lot less like a tool and a lot more like an employee.
This is exactly why IT needs to manage AI agents like a workforce. Without structure, you get chaos. Agents with unchecked access, no performance reviews, and zero offboarding plans become serious liabilities. IT has a real opportunity here — not just to manage risk, but to lead the conversation on responsible AI use.
Why IT Is Becoming the New HR for AI Agents
HR manages people. IT manages systems. For a long time, those were separate lanes. AI agents are merging them into one messy intersection.
Think about what HR actually does. It hires, trains, monitors, and eventually separates employees. It sets behavioral expectations. It ensures people have the right access for their roles. Now think about what AI agents need. They need provisioning, oversight, performance evaluation, and eventually retirement. The parallels are hard to ignore.
IT is already responsible for identity management, access controls, and system integrations. Those same capabilities apply directly to AI agents. When an agent gets deployed, IT decides what it can touch. When an agent misbehaves, IT investigates. When an agent is no longer needed, IT pulls the plug.
The shift is real. Gartner predicted that by 2025, agentic AI would be one of the top strategic technology trends. Organizations that treat AI agents like unmanaged scripts will fall behind. Those that apply workforce-style governance will gain reliability, auditability, and trust.
HR built its playbook over decades. IT now needs its own version — one designed for entities that never sleep, never forget, and never push back.
How IT Manages the Digital Coworker Lifecycle
Every employee goes through a lifecycle. They get hired, onboarded, developed, and eventually leave the company. AI agents follow a similar arc. Managing that arc deliberately is what separates functional AI programs from chaotic ones.
Recruiting the Right Agents
This section of the lifecycle is often skipped. Teams adopt agents fast because someone saw a demo or read a blog post. That is a risky way to build a digital workforce.
Recruiting the right agents starts with defining what problem needs solving. Not every workflow needs an AI agent. Some tasks are better handled by simple automation or, frankly, a human. Before deploying any agent, IT should work with business stakeholders to assess the use case properly. What decisions will this agent make? What data will it access? What happens when it gets something wrong?
Vendor evaluation matters just as much. Not all AI agents are built with enterprise security in mind. IT needs to evaluate agents the way procurement evaluates any critical vendor — checking for compliance certifications, data handling policies, audit logging capabilities, and integration security. A flashy demo is not a due diligence process.
There is also the question of fit. An agent built for customer support has different risk characteristics than one built for financial reporting. Understanding that distinction before deployment saves significant headaches later. Think of it as a job description for your digital coworker. If you cannot write one, you are probably not ready to hire.
Supervising and Upskilling Agents
Once an agent is live, the work is not done. Supervision is ongoing. This is where most organizations drop the ball.
AI agents drift. The model behind an agent may get updated by a vendor without warning. The data the agent relies on may become stale. Business rules change, but the agent keeps operating on old logic. Without active monitoring, these gaps go unnoticed until something breaks.
IT should establish performance baselines during deployment. What does good behavior look like? How many tasks does the agent complete per hour? What is its error rate? These are measurable. Track them. Set alerts for deviations.
Upskilling is the next layer. Just like employees benefit from training, agents benefit from refinement. That might mean updating prompts, retraining models on new data, or adjusting integration configurations. This is not a one-time effort. Build it into your operational calendar.
Some organizations are also adopting human-in-the-loop reviews for high-stakes agent decisions. This creates a natural feedback mechanism. It also builds trust across the business. When people see that IT is actively watching, they are far more willing to adopt AI tools confidently.
Offboarding and Succession Planning
Nobody talks about this enough. Agents get offboarded too. Projects end. Vendors shut down. Better solutions emerge. What happens to the agent that has been running quietly in the background for eighteen months?
Without a formal offboarding process, you risk orphaned credentials, stale integrations, and data that nobody knows what to do with. IT needs an agent registry — a living record of every deployed agent, its access level, its purpose, and its owner. When an agent is retired, that registry guides the cleanup.
Succession planning matters too. If an agent is mission-critical, what is the backup plan? What happens during a vendor outage? Treating AI agents like workforce resources means thinking about continuity. A human employee leaving the company triggers a knowledge transfer process. An agent being retired should trigger something similar.
How IT Establishes Control
Control is not about slowing things down. It is about making sure things do not spiral. AI agents operating without governance frameworks are a security and compliance risk. IT's job is to build guardrails that allow speed without recklessness.
Provisioning and Access Controls
This is the technical backbone of AI agent governance. Every agent should be treated like a privileged user. That means applying the principle of least privilege from day one.
Start with identity. Agents need service accounts, not human accounts. They should have distinct identities that are trackable in your identity management system. Using a shared human account is a bad practice that makes auditing nearly impossible.
Access should be scoped tightly. An agent that reads customer emails to generate summaries does not need write access to your CRM. An agent that processes invoices does not need access to HR data. Map out the exact permissions needed and provision accordingly. Review those permissions quarterly.
Authentication matters. Agents should use secure credentials — rotating API keys, OAuth tokens with expiry, or certificate-based authentication. Hard-coded credentials are a vulnerability. Treat agent authentication with the same rigor you apply to human authentication.
Logging is non-negotiable. Every action an agent takes should be logged. Those logs should be centralized, tamper-evident, and reviewable. When an incident happens — and at some point, one will — you need a clear audit trail. Without logs, you are guessing.
Finally, integrate agent governance into your existing security frameworks. Agents should appear in your SIEM. They should trigger alerts in your SOAR platform. They should be part of your vulnerability management process. If your security tools cannot see your agents, your security posture has a blind spot.
Conclusion
AI agents are not going away. They are becoming embedded in how work gets done. The organizations that figure out governance now will move faster and break fewer things than those that treat agents as an afterthought.
IT is uniquely positioned to lead this. The skills are already there — identity management, access control, monitoring, incident response. What is new is applying those skills to entities that think and act semi-autonomously.
Managing AI agents like a workforce is not a metaphor. It is a practical framework. Recruit carefully. Supervise actively. Offboard deliberately. Control rigorously. That is how you build an AI program that the rest of the business can actually trust.
Start by auditing what agents you already have running. You might be surprised by the number. From there, build your registry, define your governance policies, and bring IT into every future deployment conversation from the start.
