Step-by-step guides for developers who build agents
Knowing how agents work conceptually is useful, but building one teaches you things no diagram can. This category is for developers who want hands-on experience: your first agent from scratch, how to wire up external tools, designing memory that actually persists across sessions, and the common implementation mistakes that waste hours of debugging. Every guide here assumes you can read code and aims to leave you with something working by the end.
You can build a working AI agent in an afternoon: install LangGraph, define a state schema, write two nodes (reason and act), attach a real tool like web search, wire the edges, and run the loop. This guide shows every step.
Memory is the hardest part of agent design. In-context memory fills up fast; vector stores add retrieval latency; episodic logs prevent repeated mistakes. Getting these layers right is the difference between a 5-step demo and a 100-step production agent.
Tools are how AI agents escape the text box and act in the world. The LLM reads a tool schema, outputs a structured function call, the runtime executes it, and the result feeds back as an observation. The quality of the schema — not the tool itself — determines whether the agent uses it correctly.