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.
What is AI Agent?
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. The technology is improving quickly, but for now the sensible pattern is to let an agent handle the legwork while a human stays close enough to catch it when it goes off track.
Real-world example
Say a jacket you ordered online arrived in the wrong size and you tell an AI agent, "sort out a return and get me the right one." Instead of just explaining the steps, it goes and does them: it finds the order in your email, pulls up the store's return policy, checks whether the size you need is in stock, generates the prepaid return label, books a courier pickup for a day you're home, and places the replacement order — pausing to confirm with you before it actually spends any money. Each of those is a separate step using a different tool, chosen on the fly based on what it found in the step before. A normal chatbot could describe that whole process to you; an agent is what tries to carry it out.
Related terms
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot responds to each message and then waits for your next one — it talks but doesn't act. An AI agent is given a goal and works toward it over several steps, taking actions through tools (searching, running software, sending requests) and adjusting based on what happens, until the task is done. Put simply: a chatbot answers, an agent does. Many agents use a chatbot-style language model as their "brain," but wrap it in the ability to plan and act.
Can AI agents act on their own without supervision?
They can be set up to, but for anything with real consequences it's usually unwise to let them run fully unsupervised today. Agents still misread goals and take wrong actions, and unlike a chatbot's mistake — a wrong sentence — an agent's mistake might send the wrong email or place the wrong order. That's why well-designed agents include limits and a human checkpoint before important or irreversible steps. The level of independence should match how forgiving the task is if it goes wrong.
What can AI agents actually do today?
They're most useful for multi-step tasks that have a clear goal and tolerate the occasional error — things like researching a topic across many sources and compiling the findings, working through a coding task and running tests, or handling routine digital errands that involve several tools. They're far less reliable for high-stakes, ambiguous, or judgment-heavy work where a mistake is costly. The field is moving fast, so what agents can dependably handle is expanding, but the gap between an impressive demo and something you'd trust unattended is still real.