All Articles
Architecture // BUILDING ON NODES•19 JUL 2025•6 min read

The Multi-Agent Graph Prototype: What Worked and What Broke

Building our initial system using LangChain and LangGraph with specialized task nodes. Why nested sub-graphs introduced high latency and debugging nightmares.

Instead of simple prompts, we dove into complex orchestration. We built our system using LangChain and LangGraph, designing multiple specialized AI sub-graphs to handle specific tasks (like spec writing, coding, review, and verification). All of these sub-graphs were coordinated by a single global orchestrator agent. While this multi-agent graph design looked impressive on a whiteboard and allowed complex multi-step execution flows, it was highly fragile. Loops got stuck, latency was high, and debugging a nested graph of LLM calls became a developer's nightmare. We realized that structural complexity was masking core execution weaknesses.
Interactive Demonstration // Architecture

Demonstration of the initial LangGraph/LangChain multi-agent prototype.

Key Lessons & Engineering Principles

  • 1
    Nested task-specific sub-graphs introduce exponential latency and debugging friction.
  • 2
    A single global orchestrator easily becomes a bottleneck for dynamic, iterative tasks.
  • 3
    LangGraph states are powerful but can become difficult to trace as graphs scale.