Building Your First AI Agent, Step by Step

An agent is an AI system that works toward a goal over several steps, deciding for itself which action to take next, such as searching, reading a file or running code. A chatbot answers once. An agent keeps going until the job is done or it hits a limit. You do not need a big framework to understand how this works.

The core loop

Almost every agent is the same cycle:

1. Give the model a goal and a list of available tools.
2. The model either answers or asks to use a tool.
3. Your code runs the tool and returns the result.
4. The model sees the result and decides what to do next.
5. Repeat until the model gives a final answer or a stop rule triggers.

The model never runs anything itself. Your code does. That is where you keep control.

Step 1: define a narrow goal

Vague goals produce wandering agents. Compare:

• Poor: "Help with my business."
• Good: "Read the customer emails in this folder and produce a table of requests, urgency and suggested replies."

Start with one task you can check easily.

Step 2: choose a few tools

Each tool is a function with a name, a description and inputs. Start with one or two, such as:

• search_notes(query) to look through your documents
• read_file(path) limited to one folder
• calculate(expression) for math

Describe each tool clearly. The model decides when to use it based almost entirely on that description.

Step 3: write the system prompt

You are a research helper. Goal: answer the user's question using only the tools provided.
Rules:
- Use search_notes before answering from memory.
- Cite the file name for every claim.
- If the notes do not contain the answer, say so.
- Stop and give a final answer once you have enough information. Never use more than 6 tool calls.

Step 4: sketch the loop

This pseudocode-style Python shows the idea. Each vendor's SDK names things differently, so check its current docs for the exact calls.

MAX_STEPS = 6
messages = [{"role": "user", "content": user_goal}]

for step in range(MAX_STEPS):
    reply = call_model(system_prompt, messages, tools)
    if reply.wants_tool:
        result = run_tool(reply.tool_name, reply.tool_args)
        messages.append(reply.as_message())
        messages.append({"role": "tool", "content": result})
    else:
        print(reply.text)
        break
else:
    print("Stopped: step limit reached.")

Step 5: add guard rails

• Step limit: as above, so it cannot loop forever.
• Budget limit: stop if tokens or spend pass a threshold.
• Allow-lists: tools may only touch approved folders or sites.
• Human approval: require a yes before anything that sends, deletes or spends.
• Logging: record every tool call and result so you can see what happened.

Step 6: test with traps

Try inputs where the answer is missing, where a tool errors, or where the question is out of scope. A good agent says "I could not find that" instead of inventing.

Step 7: improve one thing at a time

Common upgrades:

• Better tool descriptions
• A planning step ("write a short plan before acting")
• A memory file for facts that should carry over between runs
• A self-check step that reviews the draft against the goal

When not to use an agent

If a fixed sequence of steps solves the task, a plain script or workflow is cheaper, faster and more predictable. Use an agent when the next step truly depends on what was found.

Summary

Narrow goal, few well-described tools, clear rules, a capped loop, human approval for risky actions, and detailed logs. Start small and widen only after it works reliably.