What will you learn?

• LLM workflow vs. AI agent — what actually separates them, and a simple test for which one your problem needs.
• Where RAG runs out of road — retrieval gives you context, but it can't make decisions. We'll show exactly where the seam is, and what fills the gap.
• The perception → reasoning → action loop that turns an LLM call into a real agent (and not a fancy if-statement).
• ReAct in practice — the pattern that makes agents you can actually step through, log, and debug.
• A live preview of the multi-agent system from the full course — plus a contrarian take: most multi-agent setups in industry are overkill, and we'll show when one well-designed agent beats five.

Who is this masterclass for?

ML engineers, AI Engineers, Software Engineers, and AI practitioners who've outgrown prompt chains. If you've shipped a RAG app, wired up tool calls, or evaluated LangGraph / CrewAI / the Agents SDK and felt the abstractions don't quite fit your problem — this session names what you've been bumping into and gives you the mental model to move past it.