3 articles in this tag.
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.