Why this track exists
The gap between "I can call the OpenAI API" and "I can build a reliable, observable AI system" is large. This track closes it.
It is where the foundations from Track 1 meet the practical engineering from Track 2 — and turn into systems that actually work in production.
What you will learn
- How LLMs really work, what they are good at, and when they fail
- Semantic search and RAG: building systems that retrieve the right context
- AI agents with LangGraph: stateful workflows, tool use, human-in-the-loop patterns
- MCP (Model Context Protocol): connecting agents to your tools and data safely
- AI observability: tracing, cost tracking, error handling, budget limits
Who this is for
- Engineers building AI-powered products or internal tools
- Developers who want to go beyond prompting to building real agent systems
- Anyone who wants their AI systems to be observable, debuggable, and production-safe
What this unlocks
- Personal AI OS — the capstone track, where everything combines into a real running system