What it is
A chatbot answers. An agent acts. Give a model a goal, a set of tools it may use and a way to check its own work, and it can look up an order, draft the reply, update the record and stop when the job is done. That loop, think, use a tool, read what came back, decide the next step, is what people mean by an agent.
The catch is that every extra step is one more place to go wrong. The model makers' own advice is to start with the simplest thing that works: a fixed workflow where the steps are written down and AI does one or two of them, such as reading an email and sorting it. Reach for an agent when the path really changes from job to job, like support questions that each need a different lookup.
Agents reach your tools through connectors. The Model Context Protocol is an open standard for those connectors, so one integration can serve several AI apps. Whatever you build, give it narrow permissions, keep a log of what it did, and have a person approve anything that spends money or reaches a customer.
Try this week
- List one job where the steps change every time, and one where they never do.
- For the fixed job, write the steps down and mark the one AI could do.
- Decide who approves before anything leaves the building.
A plain-English overview to get you started, not legal or financial advice.