Multi-Agent Systems Blueprint: LangGraph

Building multi-agent systems that actually work in production is very different from a LangGraph demo notebook. In this session, we'll design an enterprise-grade architecture tailored to your specific use case — covering agent orchestration, state management, human-in-the-loop checkpoints, error recovery, and observability.

You'll leave with:

1. A clear architecture diagram for your multi-agent system
2. A LangGraph starter template scaffolded to your use case
3. Guidance on agent-to-agent communication patterns (supervisor, hierarchical, or swarm)
4. Recommendations on state persistence, memory, and tool integration
5. Common production pitfalls to avoid (cost control, latency, failure handling)

This session is for you if:

1. You're building an AI agent system and need to move from prototype to production
2. You're evaluating whether to use a supervisor pattern, hierarchical agents, or a single ReAct agent
3. You're stuck on state management, tool orchestration, or agent handoffs in LangGraph
4. You're an engineering leader scoping an agentic AI initiative for your team

What to bring:

A brief description of your use case (even rough) so we can design something directly relevant not generic theory.