AI Agents

An agent is a model in a loop with tools and a stopping condition. That is genuinely all it is — the frameworks layer convenience on top, but the mechanism is a prompt, a list of callable functions, a parser that turns the model's output into a function call, and a rule for when to stop. Understanding it at that level is what lets you debug one when it goes wrong, which it will.

These guides build agents from that base up rather than starting with a framework. You will see the tool-calling loop written out explicitly, the failure modes that show up in practice — a model that calls the same tool forever, a model that invents a tool that does not exist, a model that returns prose where structured output was required — and the guardrails that catch each one.

Worth saying plainly: most tasks people reach for an agent to do are better served by a fixed workflow with one model call in it. Agents earn their complexity when the sequence of steps genuinely cannot be known in advance.