Building an AI Agent
You've seen the demos where an "AI agent" books a flight, fixes a bug, or files your taxes, and it feels like there's some new kind of intelligence behind the curtain. There isn't. An agent is the same language model you already know, handed two things: a set of tools it can call, and a loop that keeps running until the job is done. Once you see those three parts — model, tools, loop — the magic turns into machinery you can build, debug, and trust.
This guide takes the curtain down. We start with the mental model (it's a loop, and you write most of it), then walk the reasoning-acting cycle step by step with real function-calls, and finish with the honest part: the ways agents spiral, hallucinate tools, and burn money — and the guardrails that keep them on a leash.
How to read this
- Want the whole idea in one sitting? Read Phase 1: An Agent Is a Loop. It installs the model-plus-tools-plus-loop picture, which is most of the battle.
- Want it to actually click? Read in order. Phase 1 is the what, Phase 2 is the how (function-calling and the real cycle), and Phase 3 is the where it bites — the failure modes and the guardrails that separate a toy from something you'd let near production.
The phases
- An Agent Is a Loop — the mental model: a model that reasons, decides to call a tool, reads the result, and repeats until done. The control loop you write versus the choices the model makes.
- The Reasoning-Acting Cycle — function-calling with a schema, the turn-by-turn message exchange, how tool results feed back in, and what "memory" really means here.
- Where Agents Go Wrong — infinite loops, hallucinated tool calls, runaway cost, and the guardrails — step budgets, validation, approval gates — that keep an agent on a leash.
This guide assumes you're comfortable calling a model programmatically. If "send a request, get text back" isn't second nature yet, read Using an LLM API first — an agent is that same call, wrapped in a loop.