This course contains the use of artificial intelligence. An AI agent is just an LLM in a loop that chooses its own next action. Once you can write that loop — about twenty lines of plain Python — every “agent framework” on the market stops being magic. This course is the un-magic. You build your own agent framework from scratch, one concept at a time, and walk away understanding every line of it — and able to read LangChain, CrewAI, or AutoGen like an open book.
What you’ll build, across nine short modules: the reusable agent loop with a correct stopping condition; tool use from scratch (parse, a registry and dispatch, and graceful handling when the model invents a tool); three kinds of memory — working, episodic, and semantic retrieval you build with your own cosine similarity in numpy; planning with ReAct, reflection-and-retry, and a real beam-search tree-of-thoughts; multi-agent systems
with a coordinator and a delegation cap so it can never loop forever; evaluation — the part almost no course teaches — a harness, the three metrics that matter, a failure taxonomy, and regression diffs; production hardening with cost and step caps, structured logging, and tool guardrails; and a capstone that consolidates it into one clean API and builds a flagship agent end to end.
