Building a Predictable Agentic Workflow using Langgraph

Most AI agent tutorials stop at: LLM → tool → response. But production AI systems need state, branching, persistence, human approval, recovery, observability, and workflows that can survive more than one request. In this video, I break down how I use LangGraph to build a real multi-agent competitor research system, starting from a simple graph and gradually moving toward the full production architecture. We cover: • How LangGraph nodes, edges and state work • Conditional routing and agent workflows • Checkpointing and persistent state • Human-in-the-loop with interrupts • Resuming paused workflows • Time travel and branching from previous states • Building larger multi-agent graphs • Connecting the graph to a real frontend • Streaming workflow progress • Observability, latency, token usage and cost • What actually changes when an AI prototype becomes a production system The project shown in this video uses multiple specialized agents to profile companies, discover competitors, research them, analyze the results and review the final output.