LangGraph: Building Multi-Agent Systems That Don't Break
LangGraph is the most powerful tool I’ve added to my AI stack — and also the most misunderstood. After using it on ESG scoring agents and travel booking systems, here’s how I actually structure multi-agent workflows that stay predictable in production.
Why LangChain alone isn’t enough for complex agents
Single-chain LangChain works perfectly for linear workflows. The moment you need branching logic, conditional tool calls, or multiple specialized agents collaborating, you need a state machine. That’s exactly what LangGraph provides — a directed graph where each node is an agent or tool, and edges define the flow.
The state schema is your contract
Every LangGraph workflow starts with a TypedDict state schema. This isn’t optional — it’s the contract between nodes. I define the state first, before writing a single node. Every field that flows through the graph must live here. This discipline alone eliminates 80% of hard-to-debug issues.
Supervisor pattern vs parallel agents
For most client projects, I use a supervisor pattern: one orchestrator agent decides which specialist agent handles the current task. Parallel agents sound appealing but introduce synchronization complexity. Start with supervisor, move to parallel only when latency demands it.
Human-in-the-loop without rewriting your graph
LangGraph’s interrupt_before and interrupt_after let you pause execution at any node for human review. For the ESG platform, we paused before final scoring to allow compliance officers to verify edge cases. This feature alone justified choosing LangGraph over a custom solution.
Persisting state across sessions
Use LangGraph’s built-in checkpointers (PostgresSaver for production, MemorySaver for development). This gives you conversation memory, the ability to resume interrupted runs, and full audit trails — essential for client-facing AI products.
