What Are AI Agents and How Do You Build One? A Clear Guide

By Modern AI Engineering · 2026-09-30 · 8 min read

An AI agent is a program in which a language model decides what to do next, takes an action through a tool, looks at the result, and repeats until the task is finished. A chatbot answers a message. An agent works towards a goal over several steps.

To build one you need four things: a model, a set of tools, a loop that connects them, and a clear rule for when to stop. This guide explains each part and the order to build them in.

What is an AI agent in simple terms?

A plain LLM call is one question and one answer. The model cannot check a fact, read a file or run code. It can only write text.

An agent wraps the model in a loop and gives it tools. Suppose you ask an agent to find out why a test is failing. It reads the test file, runs the test, reads the error, opens the function the error points to, proposes a fix, runs the test again and reports back. At each step the model chose the next action based on what it had just seen.

That choice is the defining feature. In an ordinary program the developer fixes the order of steps in code. In an agent the model decides the order while it runs.

How does the agent loop work?

Almost every agent follows the same cycle.

1. Observe. The agent gathers what it knows: the goal, the conversation so far and the results of earlier actions.
2. Think. The model reasons about what to do next.
3. Act. The model asks for a tool to be called with specific arguments. The program runs the tool.
4. Read the result. The tool output is added to the context.
5. Repeat, or stop when the goal is met or a limit is reached.

What are the parts of an AI agent?

Agents are built from a small set of parts.

How do tools and function calling work?

The model cannot run code itself. Function calling is the bridge. You describe each tool to the model: its name, what it does and what arguments it takes. When the model wants to use one, it outputs a structured request naming the tool and the arguments. Your program runs the real function and sends the result back as text.

Tool descriptions matter more than people expect. The model chooses tools by reading them, so a vague description leads to wrong choices. Keep each tool narrow, name it clearly and return errors the model can understand.

The Model Context Protocol, or MCP, is a standard way to expose tools so that different agents and applications can use the same ones without custom glue for each pair.

Common agent patterns: ReAct, plan-and-execute, reflection

A few designs appear in most agent systems.

ReAct alternates reasoning and acting. The model writes a short thought, calls a tool, reads the result and thinks again. It is simple and adapts well when the path is not known in advance.

Plan-and-execute splits the job in two. First the model writes a full plan. Then it carries out the steps one at a time. This suits longer tasks where a clear structure helps.

Reflection adds a review step. The agent checks its own output against the goal and revises it. This raises quality and costs extra model calls.

Multi-agent systems divide work among several agents with different roles, for example one that researches and one that writes. They help with large tasks, but they add coordination problems. Start with one agent.

How to build an AI agent step by step

Build the smallest version first and add parts only when you need them.

1. Pick one narrow task with a clear finish, such as answering questions from a folder of files.
2. Write down what done means and how you will check it.
3. Define two or three tools with precise descriptions.
4. Write the loop yourself: call the model, run any tool it asks for, append the result, repeat.
5. Add limits: a maximum number of steps, and timeouts on tools.
6. Log every step, so you can read exactly what the agent did.
7. Test on a fixed set of tasks and study the failures.
8. Only then add memory, planning or a framework, if the failures show you need them.

Should you use a framework such as LangChain or LangGraph?

Frameworks save time on common pieces: tool wiring, state, retries and tracing. They are useful once you know what you are building.

Write a plain loop once before you reach for one. It is a small amount of code, and it shows you what the framework does for you. After that you can read framework code and debug it, instead of treating it as magic.

Why do AI agents fail?

Agents fail in ways a single model call does not. Knowing the usual causes helps you design against them.

• Errors add up. A small mistake at step two shapes every later step.
• Loops. The agent repeats the same action without progress. A step limit is essential.
• Context overload. Long runs fill the context window, and early instructions get lost.
• Unclear stop rule. The agent stops too early or never stops.
• Unsafe actions. Text from a web page or document can contain instructions that hijack the agent. This is called prompt injection, and tools that change things need extra checks.
• No evaluation. Without a test set and traces, you cannot tell whether a change made the agent better.

Frequently asked questions

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

A chatbot replies to a message with text. An agent pursues a goal across several steps, choosing and using tools along the way and checking the results.

What is agentic AI?

Agentic AI is a general term for systems in which a model plans and takes actions with some independence, instead of only producing a single reply. AI agents are the main example.

Do I need a framework to build an AI agent?

No. A basic agent is a loop around a model call with a few tools. Frameworks help with larger systems, but building one by hand first teaches you how they work.

Which model should I use for an agent?

Use one that supports function calling and follows instructions well. Test a few on your own tasks. Stronger models make fewer mistakes over long runs, and smaller ones cost less for simple steps.