Building Your First AI Agent: A Step-by-Step Guide

What you will build

By the end you will have a simple research agent: question in, search tool, summarized answer out. It is deliberately small — the point is the pattern, not a production deployment.

Step 1: Define the loop

Every agent is a loop. The basic structure is:

1. Receive input — the user's question
2. Think — the model decides what to do next
3. Act — call a tool (search, read, compute)
4. Observe — process the tool's output
5. Decide — loop again or return a final answer

This is the ReAct pattern. Think of it as a while loop where the model controls the loop condition.

Step 2: Choose your tools

Your agent needs at least one tool. For a research agent, a web search tool is the obvious choice. Define each tool with a name, a description (for the model), and a function that executes it.

Keep tools simple and well-documented. The model picks tools based on their descriptions, so clarity matters more than cleverness.

Step 3: Write the prompt

Your system prompt should tell the model:

• What its role is ("You are a research assistant")
• What tools are available and when to use each one
• When to stop looping and return a final answer
• What format to use for the final output

Do not chase clever wording. If the model misuses a tool, the prompt probably never said when to use it.

Step 4: Add a stop condition

Without a stop condition, your agent loops forever (and burns your API budget). Common stop conditions:

• The model returns a "final answer" marker
• Maximum number of iterations reached
• A timeout fires

Use all three. The model's stop signal is the happy path. Max iterations and timeouts are safety nets.

Step 5: Test and iterate

Run your agent against 5–10 different inputs. Look for:

• Does it pick the right tools?
• Does it stop at the right time?
• Does the output match the format you asked for?
• Where does it hallucinate or get confused?

Adjust prompts and tool descriptions from what you observe. The code skeleton rarely changes; the prompt does, many times.

Common mistakes

• Too many tools at once — start with one or two
• No stop condition — always set a max iteration limit
• Vague tool descriptions — the model cannot use what it does not understand
• Not logging — save every step so you can debug failures

Next steps

Once your basic agent works, explore more complex architectures in our catalog. Add memory, chain agents together, or explore multi-agent systems.

Bottom line

• Every agent is a loop: think → act → observe → decide
• Start with one tool and one clear task
• Write clear prompts, not clever ones
• Always set stop conditions — more than one
• Iterate on the prompt, not the scaffolding