What is an AI agent? Definition and explanation.

What an AI agent does.

The key word is autonomy. Give an agent a goal (“process these incoming invoices and list the discrepancies”), and he decides for himself what steps are needed to achieve it. He reads the documents, retrieves data from another system, compares the information, and delivers a result. He doesn’t ask a question after every single action along the way.

That’s what makes an agent different from a tool that you have to control over and over again. You describe the outcome, not the path. The agent determines the route itself, as long as it stays within the limits you’ve set: which systems it’s allowed to consult, which actions it can perform on its own, and where a human needs to step in.

Agent vs. Chatbot as Co-Pilot.

This is where most of the confusion arises, so let’s be clear. A chatbot answers questions in a conversation. You type, the system responds, and that’s where it ends. A copilot goes a step further: it supports you while you work by offering suggestions and additions, but leaves the decision and execution up to you. Think of a coding assistant that suggests code for you to approve.

An agent is the difference between telling you something and doing something. While a chatbot tells you how to handle a process, an agent carries out that process. The distinction doesn’t lie in the underlying technology, since all three often run on the same language model. It lies in autonomy and oversight. If you want to delve deeper into that distinction, explains the difference between an AI copilot [OPEN: as soon as it goes live] and clarifies exactly when support is enough and when you need to act independently.

How an agent works.

Under the hood, an agent combines three elements: a language model that reasons and plans, access to tools and data, and a set of guidelines that determine what it is and isn’t allowed to do. That last point isn’t just a detail—it’s the very foundation of reliable agent behavior.

Because an agent that operates across systems needs access to those systems. And access without oversight is a risk. That’s why Cube uses MCP as the foundation for AI governance : the Model Context Protocol determines, per role, what an AI instance is allowed to see and do, regardless of which model is running underneath. An MCP server running on your own software makes your business logic accessible to the agent without data leaking between roles, clients, or projects. This way, an agent remains powerful yet constrained.

When you deploy an agent.

An agent adds value to processes that involve multiple steps, span different systems, and recur regularly. Request processing, document review, data transfer between applications: tasks where the workflow is largely fixed but involves just enough variation that a fixed script falls short.

For a single, isolated query or a “human-in-the-loop” task, a copilot is often sufficient—and more cost-effective. And irreversible actions (a payment, a deletion, a sent message) always require a human checkpoint, no matter how autonomous the agent may be. We run multiple agents in production ourselves, both in our development process and in internal business processes, and we bring that experience to projects for clients. The rule of thumb that emerges from this is: give an agent autonomy for the mundane tasks, and keep humans at the points where a mistake is costly.