---
title: "What Is an AI Agent?"
description: "An AI agent is a system that uses a language model to decide and act in a loop toward a goal, calling tools along the way. Definition, examples and how it differs from a chatbot."
type: "glossary"
locale: "en"
term: "AI Agent"
canonical: "https://agenticschool.dev/glossary/ai-agent"
dateModified: "2026-06-13"
---

# What Is an AI Agent?

- Definition: AI Agent
- Updated: 2026-06-13
- Keywords: ai agent, what is an ai agent, ai agent definition, agent vs chatbot, tool calling
- Canonical URL: https://agenticschool.dev/glossary/ai-agent
- Locale: en

> An AI agent is a system that uses a language model to decide and act in a loop toward a goal, calling tools along the way. Definition, examples and how it differs from a chatbot.

An AI agent is a software system that uses a large language model to pursue a goal by deciding what to do next, taking an action, observing the result, and repeating that loop until the task is done. The key difference from a normal chatbot is the loop and the tools: instead of just replying with text, an agent can call tools (search the web, run code, query an API, edit a file), read what comes back, and adjust its next step. So an AI agent is the model plus the ability to act and the autonomy to choose its own steps toward an outcome you set.

## Agent vs chatbot vs assistant

A chatbot answers one message at a time and forgets to act on the world. An AI agent is given a goal and works toward it across multiple steps, choosing actions on its own. The line is autonomy plus tool use, not the underlying model.

- Chatbot: you send a message, it replies with text. No actions, no loop.
- Assistant: helps with a task but you drive each step.
- AI agent: you set a goal, it plans, calls tools, checks results and iterates until done.

## The agent loop

Almost every AI agent runs the same loop: the model reads the goal and current state, decides on an action, the system executes it (a tool call), the result is fed back in, and the loop repeats. This perceive, decide, act, observe cycle is what lets an agent handle work that a single prompt cannot, like fixing a failing test or shipping a small feature end to end.

## A concrete example

A coding agent like Claude Code is a clear example. You ask it to "add a contact form and make the tests pass". It reads your files (a tool), writes new code (a tool), runs the test suite (a tool), sees a failure, edits the code again, and reruns the tests until they are green. You set the goal once; the agent chose and executed every step in between. That is the agentic difference in practice.

## FAQ

### What is the difference between an AI agent and a chatbot?

A chatbot replies to a message with text. An AI agent is given a goal and works toward it across multiple steps, calling tools and reacting to results until the task is done. The difference is autonomy and the ability to act, not the model itself.

### Does an AI agent need tools?

In practice yes. The power of an agent comes from acting on the world, so it needs tools such as code execution, file access, web search or API calls. Without tools it is effectively just a chatbot in a loop.

### Are AI agents reliable enough to use today?

For bounded, checkable tasks with a human in the loop, yes, and coding agents are the strongest example. The honest practice is to give clear goals, keep verification (tests, reviews) and approve risky actions, rather than expecting full unattended autonomy.