You want to build AI agents, but there are too many tutorials and no clear learning path. You discover LangGraph through a multi-agent post, get confused by StateGraph syntax, backtrack to find LangChain basics, and end up reading about RAG when you just wanted tool calling. Sound familiar?

Most AI agent learning failures aren't comprehension problems — they're sequencing problems. This series has dependencies: LCEL chains make LangGraph's node model intuitive, the classic agent loop explains what LangGraph's ReAct pattern improves, and the bridge post explains why stateful workflows need graphs at all. Without the right order, concepts accumulate chaotically and nothing sticks.

TLDR: This roadmap gives you three clear learning paths based on your experience: complete beginner (start with LangChain foundations), knows LangChain (jump to LangGraph), or production-focused (advanced deployment track).