A traditional chatbot is reactive: you ask, it answers, the conversation ends there. An AI agent is built to pursue a goal across multiple steps, deciding on its own what tool to use next — searching the web, running code, calling an API — and adjusting its plan based on what it finds along the way.
This shift from "answering" to "doing" is what's behind agents that can research a topic across a dozen sources, fill out a form, or manage a multi-step workflow without a person approving every single click. The model is still the same underlying technology; what's new is the scaffolding that lets it act, observe the result, and act again.
The obvious risk is giving a system that can make mistakes real-world permissions — sending an email, spending money, deleting a file. That's why most agent platforms are converging on the same pattern: let the agent act freely for low-stakes, reversible steps, and require a human's confirmation before anything consequential.