I've built a lot of AI demos that looked impressive in a notebook and fell apart in production. The usual culprit? Treating an LLM like a search engine, one prompt in, one answer out, instead of what it actually is: a reasoning engine you can wire into real workflows.
This tutorial is about doing it properly. We're going to build a functional AI agent using Anthropic's Claude API from the ground up, not a wrapper around a framework, but the actual mechanics: a ReAct loop, custom tool use, and a structure you can actually deploy. By the end you'll have running code and a mental model that makes every agent tutorial after this one make sense.
Let's get into it.
What We're Actually Building
The agent we're building will:
