How to Build Your First AI Agent

An AI agent is just a loop: reason, act, observe, repeat. Build one from scratch — the four ingredients, a worked example, tool design, and the guardrails you cannot skip.

AI agent sounds like it should be complicated — some sprawling framework with memory stores, planners, and a diagram full of boxes and arrows. It isn't. At its core, an agent is a loop. Once you see the loop clearly, every agent framework on the market stops being mysterious and starts being a convenience wrapper around the same simple idea.

This guide builds that idea from scratch. By the end you'll understand what an agent actually is, be able to picture the loop in your head, and know the handful of decisions that separate an agent that works from one that spins in circles.

First, though, one honest gut-check.

Make sure you actually need an agent

Agents are exciting, which is exactly why people build them for tasks that don't need one. Before you write a line of agent code, confirm your task genuinely requires autonomy — that you can't just write the steps down in advance. If you can script the path, a workflow will be cheaper, faster, and more reliable. We wrote a whole guide on making that call: AI Agent vs Workflow vs Single Call. Read it, decide honestly, then come back.

Still need an agent? Good. Here's how it works.

The whole idea: the agentic loop

An agent has exactly four ingredients:

  1. A model — the LLM that does the reasoning.
  2. A goal — what you want accomplished, stated in plain language.
  3. A set of tools — functions the model can call to affect the world (search the web, query a database, write a file, call an API).
  4. A loop — the thing that ties it together.

The loop is the whole trick, and it's genuinely this simple:

give the model the goal and the list of available tools
repeat:
    model reasons about what to do next
    if the model calls a tool:
        run that tool
        feed the result back to the model
    else:
        the model has produced its final answer → stop

That's it. The model looks at the goal, decides on an action, you execute it, you hand back the result, and the model decides again — now smarter, because it knows what happened. It keeps going until it decides it's done. This cycle has a name: Reason → Act → Observe, over and over.

Everything else you've heard about agents — memory, planning, retrieval, multi-agent orchestration — is bolted on top of this loop. The loop is the foundation.

Walking through one turn

Say the goal is: What's the weather in the city where our next conference is being held? And the agent has two tools: lookup_conference() and get_weather(city).

  1. Reason: The model realizes it doesn't know the city yet. It decides to call lookup_conference().
  2. Act: Your code runs lookup_conference(), which returns "Lisbon".
  3. Observe: You feed "Lisbon" back to the model.
  4. Reason: Now it knows the city. It calls get_weather("Lisbon").
  5. Act: Your code runs it, returns "19°C, clear".
  6. Observe: You feed that back.
  7. Reason: The model now has everything it needs and writes the final answer: It's 19°C and clear in Lisbon, where your next conference is. No tool call this time — so the loop ends.

Notice what the model did that a single call couldn't: it chained actions where each step depended on the result of the last. You couldn't have scripted call get_weather up front, because you didn't know the city until the first tool ran. That's the exact situation agents are for.

Build the loop yourself first

Here's the most valuable piece of advice for learning this: write the loop by hand once before you touch a framework. Frameworks like the ones built into the major SDKs, or LangChain, CrewAI, and Pydantic AI, will happily write the loop for you — most SDKs now include a tool runner that runs the whole Reason → Act → Observe cycle automatically once you hand it your tools. That's great for production. But if your first agent is a framework agent, the abstractions hide the very thing you need to understand, and when it misbehaves you'll have no mental model to debug it.

So for your first one: write the while loop, print what the model decides at each step, run the tools yourself, feed the results back. It's often under 50 lines. Then graduate to a tool runner and let it handle the plumbing — now you'll know exactly what it's doing on your behalf.

Designing tools the model can actually use

An agent is only as capable as its tools, and tools are where beginners quietly sabotage themselves. A tool is a function plus a description the model reads to decide when to use it. Treat that description as part of your prompt:

  • Name it clearly. get_weather beats fetch_data_v2.
  • Describe what it does and when to use it, in plain language. The model chooses tools based on these descriptions.
  • Keep inputs simple and typed. Fewer, well-named parameters mean fewer ways for the model to call it wrong. (Enforcing the input shape with strict schemas removes a whole class of bugs — see Getting Reliable Structured Output.)
  • Return something useful. Tool output goes straight back into the model's context, so return a tight, relevant result — not a 5,000-line raw dump that drowns its reasoning.

A well-designed tool is a form of prompt engineering. Sloppy tool descriptions produce agents that reach for the wrong tool at the wrong time.

The guardrails you cannot skip

An autonomous loop that can act in the world needs limits. Three are non-negotiable:

  • A stop condition. Cap the number of iterations. Without a ceiling, a confused agent can loop forever, burning tokens and money. Decide the max up front.
  • Human-in-the-loop for high-stakes actions. Don't let the agent send the email, issue the refund, or delete the records on its own. A common, powerful pattern: the agent does 90% of the work and saves as draft, and a human clicks the final button. You get the leverage without handing over irreversible control.
  • Error handling. Tools fail — APIs time out, queries return nothing. Feed the error back to the model as an observation; a good agent will adapt. But make sure a failing tool can't crash the whole loop silently.

Where agents go next

Once your basic loop runs, the natural extensions are:

  • Memory — letting the agent carry context across turns or sessions.
  • Better tool access — instead of hand-wiring every integration, connect your agent to standardized tools through MCP (the Model Context Protocol), which we cover in What Is MCP.
  • Evaluation — the step everyone skips: how do you know your agent is doing a good job? That deserves its own discipline.

But none of that changes the core. Strip away every layer and you're left with a model, a goal, some tools, and a loop that says: reason, act, observe, repeat. Understand that, and you understand agents.

For the foundation underneath all of this — because a confused agent is usually a badly-instructed one — start with The Complete Guide to Prompt Engineering in 2026. And for prompts you can drop straight into your agent's steps, browse the prompt library.

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