Day 3: Multi-Agent Systems - The Supervisor Pattern

#agents #designpatterns #ai #architecture

Day 3: Multi-Agent Systems - The Supervisor Pattern

Note:

This blog post is part of 4-Day Series - Agentic AI with LangChain/LangGraph
Though this series is mainly written in JS. However, there is a python version of the code available and working end-to-end in the repository

Welcome to Day 3. We have a single agent that can use tools. But as tasks get complex, a single prompt becomes overwhelmed. It's hard to be a "Researcher, Writer, Editor, and Coder" all at once.

The solution is Multi-Agent Systems. We break the brain into specialized distinct personas.

The Architecture: Researcher & Writer

We will build a graph where two agents collaborate:

Researcher: Has access to search tools. Its system prompt is "You are a researcher...".
Writer: Has NO tools. Its system prompt is "You are a writer...".

They communicate by adding messages to the shared State.

1. Creating Specialized Agents

We use a helper function to create nodes. Notice how we bindTools only to the Researcher. The Writer doesn't even know tools exist, which prevents it from hallucinating tool calls.

const createAgent = (model, systemPrompt, tools = []) => {
  const modelWithTools = model.bindTools(tools);
  return async (state) => {
    // We prepend the system prompt to the history
    const messages = [
      new SystemMessage(systemPrompt),
      ...state.messages,
    ];
    const response = await modelWithTools.invoke(messages);
    return { messages: [response] };
  };
};

2. The Handoff (Routing)

In a sophisticated system, a "Supervisor" LLM would decide who speaks next. For this tutorial, we'll implement a deterministic flow to understand the mechanics:
Researcher -> (maybe Tools) -> Researcher -> Writer -> End

Visualizing the Team

This is implemented via edges in LangGraph:

const workflow = new StateGraph(MessagesAnnotation)
  .addNode("researcher", researcherNode)
  .addNode("tools", toolNode)
  .addNode("writer", writerNode)

  // Start with Research
  .addEdge("__start__", "researcher")

  // Researcher Logic:
  // If it calls a tool -> go to "tools"
  // If it has an answer -> hand off to "writer"
  .addConditionalEdges("researcher", (state) => {
    const last = state.messages[state.messages.length - 1];
    return last.tool_calls?.length ? "tools" : "writer";
  })

  // Tools always return results to the Researcher
  .addEdge("tools", "researcher")

  // Writer always finishes the job
  .addEdge("writer", "__end__");

The Power of Shared State

The magic happens in MessagesAnnotation.

Researcher adds: "I found X, Y, Z."
Graph transitions to Writer.
Writer receives [UserMessage, ResearcherMessage].
Writer generates: "Here is a blog post about X, Y, Z..."

The Writer didn't need to search. It just "read" what the Researcher "wrote" in the shared state. This separation of concerns allows you to tune each agent individually (e.g., set temperature: 0.8 for the Writer for creativity, but 0 for the Researcher for accuracy).