AI Agent

An AI agent is a system that doesn't just answer a question but pursues a goal on your behalf — breaking it into steps, using tools like search, software, or other programs, and adjusting as it goes until the task is done.

A regular AI chatbot is reactive: you ask, it answers, and the exchange ends there. If you want it to actually do something in the world — look something up, fill in a form, send a message, run a calculation — you have to carry out each step yourself and feed the results back. An AI agent closes that gap. You give it a goal rather than a single question, and it works toward that goal over multiple steps, deciding what to do next, taking actions through tools it has been given access to, checking the outcome, and continuing until the job is finished or it gets stuck. It is the difference between a vending machine and a concierge: with a vending machine you press one button and get one thing, while a concierge takes an outcome you hand them — "get me a table at a fully booked restaurant tonight" — and works out the steps themselves, trying another angle when the first one fails. The chatbot tells you how to do something; the agent attempts to get it done.

Underneath, most agents today are built around a large language model doing the "thinking" — interpreting the goal, planning a sequence of steps, and deciding which tool to reach for at each point. The tools are what let it act rather than just talk: a web search to gather current information, a calculator for reliable arithmetic, a calendar, a database, or another piece of software it can operate. Crucially, an agent runs in a loop — often called the agent loop — that alternates between reasoning about what to do next and then doing it, a back-and-forth that one widely used approach calls ReAct, a contraction of "reason" and "act." It takes a step, looks at what happened, and uses that result to decide the next move, much closer to how a person tackles an open-ended errand than to a single question-and-answer. That loop is also where agents earn their reputation for being powerful but unpredictable: small misjudgments early on can compound, so well-built agents include checkpoints, limits, and often a human sign-off before anything important or irreversible happens.

This shift from answering to acting is one of the most active areas in AI right now, but it is worth being clear-eyed about where things stand. Agents can be genuinely useful for multi-step tasks with clear goals and forgiving stakes, yet they still make mistakes, misread instructions, and occasionally take confidently wrong actions — which matters far more when an agent can spend money, send emails, or change real records than when a chatbot simply writes a wrong sentence. Whatever an agent does, it does through the tools it was handed — that list is its entire reach, so both what it can accomplish and what it can get wrong are set by what it was given access to, not by how clever the model behind it is.