AI agents and traditional automation both take work off your team, and the words get used interchangeably. They are not the same thing. Automation follows fixed rules; an agent decides what to do, step by step. Choosing the wrong one is expensive in both directions.
An AI agent sounds like magic and is, underneath, a fairly simple loop: the model decides on an action, the action runs, the model sees the result, and it decides again. Understanding that loop tells you what agents are good at, what they cost, and where they break.
The mistake teams make with AI automation is not being too cautious. It is automating the wrong things — pointing an expensive model at work that rules did better, while leaving the hard judgment work manual. Here is a framework for deciding.