How to Build an AI Agent Step by Step for Beginners

If you want to know how to build an AI agent, start small. An AI agent is a program where a model decides what to do next, calls tools, and loops until a job is done. You can build a simple one in an afternoon if you pick a narrow job and add pieces one at a time. The Agents and Automation course has three short lessons on this, and you sign in to open it.

If you are new to building a business with AI, Age of AI Entrepreneur gives you short, hands-on lessons with sources, so you learn the ideas behind agents before you spend time wiring one up.

What an AI agent is and how it differs from a chatbot

A chatbot answers. You send a message, it replies, and that is the end of the turn. An agent acts. It reads a goal, picks a step, uses a tool, looks at the result, and picks the next step.

Three parts make an agent:

• A model that reasons about what to do.
• Tools it can call, such as search, a spreadsheet, or an email draft.
• A loop that keeps going until the goal is met or a stop rule fires.

The word "goal" matters here. An agent without a clear goal wanders.

Workflow or agent: which one does your task need?

This is the first lesson in the Agents and Automation course: workflows versus agents. Many tasks do not need an agent at all.

A workflow follows steps you wrote in advance. Step one, step two, step three. The model may fill in a step, but your code decides the order. A workflow is predictable, cheap and easy to debug.

An agent chooses its own steps. That makes it flexible, but it is also harder to predict and harder to test.

Use this rule of thumb:

• If you can write the steps down before the task starts, build a workflow.
• If the steps depend on what the agent finds along the way, build an agent.

Example: "Summarize each new support email and tag it" is a workflow. The steps never change. "Research this prospect and draft a tailored note, checking more sources if the first ones are thin" is closer to an agent, because the number of steps depends on what turns up.

If you are unsure, start with a workflow. You can add agent behavior later where it earns its place. A good warm-up is the lesson on automating one boring workflow in the Your First Builds course.

How to build an AI agent: pick one narrow job with a clear goal

Beginners often start with "an agent that runs my business." That fails. Start with a job a person could finish in ten to thirty minutes.

Write the goal in one sentence with a clear finish line:

Given a company name, find its website, read the pricing page, and return a three-line summary of how it charges.

That sentence tells you what the input is, what done looks like, and what the output should be. If you cannot write it that cleanly, the job is too fuzzy.

Choose a job where a real person has a real problem. If you need help finding one, see how to find a real problem for an AI startup. For small ideas you can ship fast, this list of no-code AI tool ideas may help.

Step 2: Choose a model and write the agent's instructions

Pick a model you can reach through an API. For a first build, do not agonize. The AI Product Stack course covers how to choose between big, small, open and closed models, and what a request costs in tokens. Agents make many calls in a loop, so cost adds up faster than with a single chat reply.

Then write the instructions, often called the system prompt. Keep them plain. A good set covers:

• Role and goal: what the agent is for, in one or two sentences.
• Tools: what each tool does and when to use it.
• Output format: exactly what the final answer looks like.
• Stop rule: when to finish, and what to do if it cannot find the answer.

Here is an example for the pricing job:

You research how a company charges for its product. Use the search tool to find the pricing page, then the fetch tool to read it. Return exactly three lines: the model (subscription, usage, one-off), the entry price if shown, and one catch. If you cannot find a pricing page after three searches, say so and stop. Never guess a price.

The last two sentences are the useful ones. A stop rule and a ban on guessing prevent most of the early mess.

Step 3: Give the agent tools it can call

A tool is just a function the model can ask you to run. You describe it (name, what it does, what inputs it takes), the model replies with a request to call it, your code runs it, and you pass the result back. The model never runs anything itself.

For the pricing agent you need two tools:

• search(query) returns a short list of results.
• fetch(url) returns the text of a page.

Keep tools few and sharp. Three well-described tools beat ten vague ones. Write each description as if you were explaining it to a new colleague. Say what it returns and when not to use it.

Your loop then looks like this in plain steps:

1. Send the goal, the instructions and the tool descriptions to the model.
2. If the model asks for a tool, run it and add the result to the conversation.
3. Send the updated conversation back to the model.
4. Repeat until the model gives a final answer or you hit a step limit.

That step limit is not optional. Set a maximum, say eight turns, so a confused agent cannot loop forever and run up your bill.

Step 4: Add memory, guardrails and human checkpoints

Once the basic loop works, add three things.

Memory

Inside one run, memory is the conversation so far. Keep tool results short, because long pages fill the context and hide the goal. Trim or summarize what you pass back. If the agent needs your own documents or data, the lesson on giving models your data through retrieval in the AI Product Stack course shows the approach.

Guardrails

Limit what the agent can touch. Give read-only tools first. Validate inputs before a tool runs. Cap steps and spend. If a tool can send, delete or pay, treat it with extra care.

Human checkpoints

For any action that is hard to undo, have the agent propose and a person approve. Let it draft the email, not send it. Once you trust its drafts after many reviewed runs, you can decide which checkpoints to loosen.

Step 5: Test your agent and fix common failures

The second lesson in the course is called Tools, Loops and Why Agents Fail, and the failures are fairly predictable:

• Looping: the agent repeats the same search. Fix with a step limit and an instruction to change approach after a failed attempt.
• Wrong tool or bad inputs: usually a vague tool description. Rewrite it with an example.
• Making things up: the agent fills a gap with a guess. Add "never guess" and a clear way to say "not found."
• Losing the goal: long tool outputs bury the instructions. Shorten what you feed back.

Do not judge the agent from one good run. Make a small set of test cases, say ten companies, including a few hard ones with no pricing page. Run them all, note which fail, change one thing, and run them again. The Evals course covers building an eval set and judging outputs.

Frequently asked questions

Do I need to code to build an AI agent?

Coding helps, because you write the loop and the tool functions. You can start with a workflow that has fixed steps, which needs less code, and add agent behavior once that works.

What is the easiest first AI agent to build?

A read-only research agent with two tools, search and fetch, and a clear three-line output. It cannot change anything, so mistakes are cheap.

Why do AI agents fail?

Common causes are loops with no step limit, vague tool descriptions, long tool outputs that bury the goal, and guessing when information is missing. Tests on a small set of cases show which one you have.

Should I use a workflow or an agent?

Use a workflow if you can write the steps in advance. Use an agent only when the next step depends on what the model finds.

How do I keep an agent from doing something harmful?

Give it read-only tools first, cap steps and spend, and require human approval for any action that is hard to undo.

Get started

The Agents and Automation course sits in the Advanced level. Its three lessons are Workflows Versus Agents, Tools, Loops and Why Agents Fail, and a project where you build an agent that does a real job. Each lesson takes about nine minutes. You sign in with your email and an emailed code.

Open the course after signing in, or look around first at Age of AI Entrepreneur. Pick one narrow job, write its goal in a sentence, and build the smallest version you can.