Build Your First AI Agent

The whole course fits in one sentence: an agent is just a loop plus a capability table. Notebook 02 builds that loop by hand in ~35 lines as an explicit state machine — the model requests a tool by name and JSON arguments, your code executes it and feeds the result back, max_iterations acts as a TTL against runaway loops. Notebooks 04–06 then reuse the identical loop with three different read-only capability tables: one explores a codebase with ls/cat/grep, one triages logs, one queries SQLite. The repetition is the lesson.

The back half cashes it in: RAG with a hand-rolled numpy vector store (retrieval is just another tool), serving the same tools over MCP from a separate process, structured outputs with Pydantic validation and retry, session memory with context-budget summarization, a supervisor agent whose tools are the earlier specialist agents, and finally LLM-as-judge evals plus prompt-injection guardrails.

Core notebooks depend only on the openai client (any OpenAI-compatible provider works); one notebook rebuilds the same agent in LangChain/LangGraph so you can see exactly what a framework buys you. Every notebook ships a Gradio *_with_ui twin with a live agent-trace panel for demos.