An Agent Is Managed Like an Employee, Not Like Software

When you buy software, you install it, configure it once, and forget it as long as it works. The reflex is so ingrained that we apply it to AI agents. That's a mistake. An agent doesn't behave like software following fixed rules: it makes decisions in situations you didn't fully anticipate. It looks far more like a new hire than a spreadsheet.

And a new hire is managed. You give them a clear scope: here's what you can decide alone, here's what goes through a human. You supervise their first weeks closely, then loosen up as trust builds. You allow room for error, but you put in place a way to catch those errors before they cause damage. Those three management reflexes apply exactly to an AI agent.

The 2026 landscape lends weight to that caution. Deloitte noted this year that in many organizations, agents are being deployed faster than the guardrails meant to frame them. In other words, the capacity to act is advancing faster than the capacity to oversee. For a large company, that's a governance problem. For an SME, it's even more direct: a poorly framed agent that sends a bad email to a customer, or approves a spend it shouldn't have, shows up immediately.

The good news is that an SME starts with an advantage here. It's small enough for an owner to keep a real eye on what the agent does, to adjust quickly, to take back control when something's off. What large structures lack — proximity — you already have. Just use it: start with a narrow scope, watch closely, widen only when the results justify it.

For an owner, the question isn't only "what can this agent do," but "what do I let it do alone, and how will I know if it gets it wrong." An agent with no answer to that question isn't a managed employee, it's a risk you've automated.

At Paquin & Co., we define an agent's scope and supervision before it goes live — so it works within a frame, like any good employee. paquinco.com