What Is an AI Agent?

An AI agent is software that takes a goal and works toward it. A large language model decides the next step, tools carry that step out, memory keeps the goal and the results in view, and planning revises the actions when a result comes back unexpected. You describe the outcome and the limits. You do not have to script every click.

That is the plain answer to what is an AI agent. A model that writes a reply and waits is a chatbot. A path that always runs the same boxes is a workflow. An agent chooses the next action from what just happened, inside the tools a person allowed. For the loop drawn out — perceive, reason, plan, act, then read the feedback — start with how AI agents work on the homepage.

What an AI agent actually is

The phrase gets attached to almost any product with a chat box. An AI agent holds a goal that outlives a single message. It inspects the current state, picks an action from a set of tools, observes the result, and decides again. If a person has to prompt every move, you are still using a chatbot. If the next step is fixed no matter what the last step returned, you are running a workflow that happens to call a model.

Someone still chooses the goal, the tools, and which actions need a person. The agent only acts through tools it was given, and the run needs a stop: the goal is met, a tool reports that the step cannot be done, or the next action needs a person. The same model in a chat box, with no tools and no loop, stays a chatbot.

How an AI agent differs from a chatbot and a workflow

Chatbots, workflows, and agents can all include a language model. Who chooses the next step is the difference.

A chatbot answers the message in front of it

A chatbot takes a message and returns a message, then waits. You still decide the next move. If the task needs a file, a search, or a record in another system, you fetch it and paste it back. A tool call inside one reply stays a chat turn when the product stops afterward. An agent reads that result and picks the next action without a new prompt for every step. The fuller side-by-side is on AI agent vs chatbot.

A workflow follows a path someone already drew

A workflow follows a path designed ahead of time. One step leads to the next unless a rule you wrote says otherwise. Use one when the path is stable and you want the same boxes every time. An agent sees the goal, the last result, and its notes, then chooses a next action from the tools it may use, so two runs of the same request can take different routes.

A model box inside a workflow drafts or classifies, then returns control to the diagram. An agent can also be one step in that diagram. The workflow decides when to start it. The agent decides how to finish its slice.

Question	Chatbot	Workflow	AI agent
What you provide	A message	A designed path	A goal and the limits around it
Who picks the next step	You	The diagram	The agent, inside the tools you allowed
When the situation changes	You rephrase	A branch you already wrote, or the path stops	It can choose a different action from the new result
What done looks like	A reply you accept	The last box on the path	The goal is met, or it stops and asks

LLM, tools, memory, and planning

Strip the product names away and the same four parts show up.

The language model

The LLM reads the goal, the latest observation, and the tool list, then proposes the next action. On its own it only produces text. A weak proposal should meet a tool result, a check, or a person. The model does not replace the tools, the memory, or the stop rule.

Tools

Tools are the allowed actions: read a file, search a permitted source, call an API, run a check, or draft a message and leave sending to a person. Each needs a name, a short description, and an input that is hard to misuse. A narrow set — read this folder, run this check, draft and do not send — is the boundary.

Memory

Memory is what remains after a step. The short form is this run's record: the goal, the actions, and the results. The longer form is notes for a later run, such as which source helped and which attempt failed. Keep it small and tied to the task, or the next decision follows a stale note.

Planning

Planning turns a goal into actions and changes them when a step fails, either as a short list or as one next action at a time. It includes a stop: the goal is met, another step is worth trying, or the tools cannot finish the job. Irreversible actions and missing information are reasons to ask a person.

Three ways people actually use an AI agent

These are patterns in working tools. They describe the shape of the work, not a ranking.

Changing software in a repository

Someone files a bug or a small change. The agent reads the report, edits the files, and runs the checks. A failed check becomes the next edit. A risky change, or a check that never passes, stops the run and shows the diff for a person to review. The agent chooses the edit. A scheduled workflow can run the checks, but it does not pick a new edit for a failure it was not given.

Gathering a brief from sources it can open

Someone needs an answer from documents outside the prompt: a policy, notes, a changelog, a thread of prior decisions. The agent opens the sources it may read, follows what they point to, and writes a brief that names where each claim came from. If two sources disagree, the disagreement stays visible. The next source depends on the last one, which is the part a single fixed fetch cannot do.

Preparing a handoff a person still approves

An order, a ticket, or a calendar record is the start. The agent looks it up and drafts the update. Sending the note or changing the record waits for a person. When the right next page is not on a diagram yet, the safe move is to draft, show the evidence, and stop.

Best AI agents surveys products in this shape. Claude AI agent looks at one family of tools.

FAQ

What is an AI agent in one sentence?

An AI agent is software that pursues a goal by planning steps, calling tools, and using the results, instead of only replying to the last message.

Is a chatbot with tools an AI agent?

It stays a chatbot when it answers once and waits for your next message. It behaves like an agent when it can choose a tool, read the result, and continue toward the goal without a new prompt for every step.

How is an AI agent different from a workflow?

A workflow follows a path someone designed in advance. An AI agent chooses the next step from the latest result, inside the tools it is allowed to use. A workflow can call a model as one box, and an agent can be one step inside a workflow.

What should I set up before I trust an AI agent with a task?

A clear goal, a short list of tools, a record of what it did, and a rule for when it must stop and ask. Keep any action that is hard to undo behind a person. The model reasons. The limits make the run safe to start.

See how an AI agent runs — the architecture on the homepage →

how do AI agents work — return to the complete AI agent architecture guide.

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