Learn Agentic AI

Agent frameworks make everything feel like magic — you call a function, tokens stream out, tools somehow get invoked. I wanted to understand the machinery underneath: the tool-call loop, structured output parsing, retrieval pipelines, evals. And I wanted to practice it without burning API credits on every exercise run.

The course's engine is llmlab — a deterministic fake LLM laboratory that mirrors the real SDK shapes (a FakeOpenAI, a FakeAnthropic, and MiniMCP for the Model Context Protocol). Exercises import it exactly like the real SDKs, but responses are deterministic, which makes them gradeable: the same input always produces the same output, so a test harness can assert on agent behavior precisely.

That one decision unlocks everything else: the course is free to run, works offline, and never flakes because a provider had a bad day. Swapping in a real SDK afterward is a one-line import change, because the shapes match.

Provider adapters and streaming, structured output with validation and retries, the tool-calling loop written from scratch, MCP client and server, RAG with prompt-injection defense, a mini agent framework that ties it together, evals for measuring agent quality, and a FastAPI + SSE capstone that streams an agent's work to the browser.

The platform reuses the architecture I built for Express Academy and FastAPI Academy: a React/Vite client talks to a FastAPI grader that spawns each submission in an isolated child runner. Grader tests here are named Python snippets executed against your module, not HTTP specs — closer to how you'd actually unit-test agent code.

Eight days, a 33-test quality gate passing end-to-end, and all three courses (Express, FastAPI, Agentic AI) run side-by-side on separate ports — a personal backend-to-AI curriculum that grades itself.