Building AI Agents with OpenAI: A Practical Guide

"AI agent" has become a loaded term. Stripped of the hype, an agent is something simple: a language model that can decide to call tools, look at the results, and keep going until a task is done. That small shift — from "generate text" to "take actions in a loop" — is what turns a chatbot into something that can actually get work finished.

What an agent actually is

A plain LLM call takes text in and gives text out. An agent wraps that call in three extra things:

• Tools — functions the model is allowed to call (search a database, send an email, read a file, book an invoice).
• A loop — the model calls a tool, you run it, you feed the result back, and it decides what to do next.
• A stop condition — the loop ends when the model returns a final answer instead of another tool call.

That's the whole idea. Everything else is engineering around those three parts.

The core loop

With the OpenAI API, the loop looks roughly like this:

const tools = [/* your function schemas */];
let messages = [{ role: "user", content: task }];

while (true) {
  const res = await client.chat.completions.create({
    model: "gpt-4.1",
    messages,
    tools,
  });

  const msg = res.choices[0].message;
  messages.push(msg);

  if (!msg.tool_calls) break; // model gave a final answer

  for (const call of msg.tool_calls) {
    const result = await runTool(call.function.name, call.function.arguments);
    messages.push({
      role: "tool",
      tool_call_id: call.id,
      content: JSON.stringify(result),
    });
  }
}

Everything hard about agents is hiding in runTool and in what you put in tools — not in the loop itself.

Lessons from production

A few things that consistently matter once an agent leaves the demo stage:

• Narrow tools beat clever prompts. A well-named tool with a tight schema removes whole classes of mistakes. create_invoice(vendor, amount, vat_rate) is far more reliable than "figure out the accounting."
• Always keep a human in the loop for irreversible actions. Let the agent propose; let a person confirm. This one rule prevents most disasters.
• Log every step. Store the full message history for each run. When an agent does something odd, the trace is the only way to understand why.
• Set a hard limit on iterations. Loops can spin. Cap them, and fail loudly when the cap is hit.

Where to start

Don't build a general "do anything" agent. Pick one boring, repetitive task with a clear success condition — categorizing support tickets, extracting fields from documents, drafting a first-pass reply — and give the agent exactly the tools it needs for that. A narrow agent that reliably saves an hour a day is worth more than an impressive demo that no one trusts in production.

If you'd like help designing or shipping an agent for a specific workflow, get in touch.