Your first AI agent: a minimal, honest build

"AI agent" has become a word that promises more than it explains. Strip away the marketing and an agent is something quite specific and quite buildable: a language model placed in a loop, given a set of tools it can call, working toward a goal until it decides it is done. That is the whole idea. This walkthrough builds the smallest honest version of that idea, explains why each piece is there, and — more usefully — shows you where it breaks, so your first agent is one you actually understand rather than one you copied and hope works.

What an agent really is

A plain language model does one thing: you send text, it sends text back, and the exchange ends. It cannot look anything up, run a calculation, or take an action in the world. An agent adds three things to that base: tools the model is allowed to use, a loop that lets it act more than once, and a goal it is trying to reach.

The defining move is that the model stops being only a text generator and becomes a decision-maker. At each turn it chooses: do I have enough to answer, or should I call a tool first? If it calls a tool, it gets the result, reconsiders, and chooses again. That cycle — think, act, observe, repeat — is the entire mechanism. Everything fancier is a variation on this loop.

The three ingredients

Tools are functions you expose to the model: search the web, query a database, do arithmetic, send an email. To the model a tool is a name, a description of what it does, and a description of the inputs it expects. The model never runs your code itself — it requests a call, your program runs the function, and you hand the result back. That boundary matters and we will return to it.

The loop is what separates an agent from a single tool call. After a tool returns, the model sees the result and decides what to do next, possibly calling another tool, possibly answering. Without the loop you have a model that can use a tool once; with it you have a model that can chain steps.

The goal is the task you give it, plus instructions about how to behave: what it is for, which tools to prefer, when to stop. A clear goal is the difference between an agent that converges on an answer and one that wanders.

Designing the tools

The quality of an agent is decided largely by its tools, and tool design is where beginners under-invest. A model can only use a tool well if it understands, from the description alone, what the tool does and when to reach for it. Treat each tool description like a small piece of documentation written for a capable but literal reader.

Be concrete about inputs and outputs. A search tool whose description says "searches" tells the model nothing about whether it searches the web, your files, or a product catalog. Spell it out: what it searches, what a query should look like, what comes back. Name the failure cases too — what the tool returns when it finds nothing — so the model can react sensibly instead of inventing a result. Vague tools produce an agent that calls the wrong thing at the wrong time and then improvises over the confusion.

A good discipline: keep tools few and sharply distinct. Three tools that clearly do different things are easier for the model to choose between than ten that overlap. You can always add more once you have watched the agent struggle without them.

The loop, step by step

Here is the minimal loop in pseudocode, and it is worth reading slowly because everything else is decoration on this:

messages = [system_instructions, user_goal]
loop:
    response = model(messages, tools)
    if response asks to call a tool:
        result = run_the_tool(response.tool, response.inputs)
        append the model's request and the result to messages
        continue          # back to the model with new information
    else:
        return response   # the model is done; answer the user

Read what is happening. You send the conversation plus the available tools. The model either asks for a tool or gives a final answer. If it asks for a tool, you execute it, append both the request and the result to the running conversation, and call the model again — now with more information than it had a moment ago. The model keeps gaining information until it has enough to answer. The loop is not magic; it is just "give the model what it asked for and ask again."

The guardrails you cannot skip

The loop above has a hole every real agent must close: nothing stops it from running forever. A model can get stuck calling the same tool, or chase a goal it will never reach. So you add a ceiling — a maximum number of steps — after which the agent stops and reports what it has, even if incomplete. An agent that admits "I could not finish in the steps allowed" is far safer than one that loops indefinitely or, worse, runs up unbounded cost.

The second guardrail is about that boundary between deciding and doing. The model decides to call a tool, but your code executes it — and the model can be wrong, manipulated, or simply confused. If a tool can do something irreversible (delete data, spend money, send a message to a real person), do not let the loop trigger it unsupervised on your first build. Keep early tools read-only, or require a human confirmation before any action with consequences. The model is a junior colleague with initiative: useful, occasionally mistaken, and not yet to be handed the keys to anything you cannot undo.

Where first agents go wrong

A few failures show up so reliably they are worth naming in advance. The first is tool confusion: the model picks the wrong tool or feeds it malformed inputs, almost always because the tool descriptions were vague. Fix the description before blaming the model. The second is looping: the agent repeats a step because a tool keeps returning something unhelpful and the model keeps trying the same fix. Your step ceiling catches this; reading the transcript tells you why it happened. The third is overconfidence after a failed tool call — a search returns nothing and the model proceeds to answer as if it had found something. The cure is the same one that helps everywhere: tell the model explicitly what to do when a tool comes back empty.

The thread connecting all three is that you debug an agent by reading its trace. The sequence of thoughts, tool calls, and results is a transcript of the agent's reasoning. When something goes wrong, the answer is almost always sitting in that log, and an agent you cannot inspect is one you cannot improve.

The takeaway

An agent is a model in a loop with tools, pointed at a goal. Build that minimal version first: a few sharply described tools, a loop that feeds results back to the model, a hard ceiling on steps, and human confirmation before anything irreversible. Watch it work by reading its trace, and you will see exactly where it confuses tools or spins in circles. The flashy multi-agent systems are the same loop, scaled. Understand the small honest version completely, and the rest is elaboration rather than mystery.