Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
Assembles everything from Parts 1–4 into one realistic system — a customer support triage agent with intent classification, human review gates, and SQLite persistence — explaining the reasoning behind every architectural decision.
Parts 1–3 covered LangGraph fundamentals and a working tool-using agent. This post covers multi-agent architectures (supervisor, swarm, hierarchical), human approval with interrupt(), and production-grade persistence.
Build a real tool-using agent with LangGraph's ReAct loop. Understand why loop-based control beats fixed sequences, how to cap iterations to prevent runaway costs, and how to handle tool failures gracefully.
The four concepts that everything in LangGraph builds on — State, Nodes, Edges, and reducers — explained with code and the reasoning behind why each one is shaped the way it is.
Every framework exists because something simpler broke down. Understand exactly which wall you hit that makes a graph-based approach worth the complexity — and when you don't need it at all.
Master ADK 2's graph-based Workflow Runtime. Construct deterministic execution pipelines with Sequential, Branch, and Loop agents.