In my day-to-day work I see firsthand what companies are planning as part of their AI transformation. Some projects are genuinely exciting and could reasonably be called moonshots. Others, in my view, rest on a basic misconception about what an AI agent actually is.
What I keep finding is that many companies are not really looking for an AI agent. They are looking for a business rules engine. When you ask them to describe the specific use case, they describe a deterministic process: a fixed set of steps and rules that must be followed the same way every time. Think of an invoice under a certain amount that needs three matching approvals and an approved vendor before it can be paid. That is classic rules-engine territory—predictable, auditable, and repeatable.
There is nothing wrong with a business rules engine being exactly what the business needs to unlock efficiencies. What is wrong is calling that process an “AI agent.” AI agents are probabilistic by nature. They try to reach a goal by choosing the most likely next action based on patterns learned during training. That makes them powerful for tasks that involve judgment or unstructured information—reviewing a long contract and flagging unusual clauses, for example. But when a customer needs defined logic, full auditability, and deterministic outcomes, a pure AI agent is simply the wrong primary tool.
In practice the strongest solutions often combine both: deterministic rules where guarantees and audit trails are required, and agents where flexibility and judgment add real value. The risk of mixing up the two is not just semantic. It leads to projects that look innovative on paper but struggle with compliance, consistency, or unexpected cost. Before labeling any initiative an “AI agent” project, it is worth asking a simple question: does this process need guaranteed, repeatable logic or adaptive judgment? The answer usually makes the right tool choice obvious.