Multi-Agent Orchestration

One agent is powerful; a team of agents is scalable. As task complexity grows, a single prompt (even with ReAct) becomes too cluttered. Multi-Agent Systems (MAS) break complex problems into sub-problems handled by specialized agents.

Why Multi-Agent?

  • Separation of Concerns: The "Writer" agent doesn't need to know how to "Execute Python Code."
  • Context Efficiency: Each agent only needs the context relevant to its sub-task.
  • Modularity: You can swap out the "Researcher" implementation without breaking the "Reporter."

Orchestration Patterns

1. The Router (Handoff) Pattern

A central "Receptionist" (Router) assesses the user's intent and hands off the conversation to a specialized agent.

User: "My payment failed." Router: "This is a billing issue. Transferring to Billing Agent." Billing Agent: "I can help with that..."

This is a State Machine. The conversation state moves Router -> Agent A -> Stop.

2. The Supervisor (Controller) Pattern

A "Boss" agent breaks down the plan and delegates tasks to workers, then aggregates the results.

  1. Supervisor: "We need to write a blog post about Q3 earnings."
  2. Delegates to Researcher: "Find Q3 data."
  3. Delegates to Analyst: "Summarize Q3 data trends."
  4. Delegates to Writer: "Write post based on analysis."

The "Boss" maintains the global state and coordinates the workers.

3. The Swarm (Autonomous) Pattern

Decentralized cooperation. Agents share a message bus and can "hear" requests, deciding if they should respond.

This is more complex to debug but allows for highly dynamic behavior (e.g., a "Safety Agent" intercepting a message from a "Design Agent" if it violates policy).

Frameworks: LangGraph & CrewAI

The industry is converging on Graph-based orchestration.

LangGraph (Stateful Graphs)

LangGraph explicitly models the agent flow as a graph: Nodes (agents/tools) and Edges (logic for transitions).

# Pseudo-code for a simple LangGraph
def router(state):
    if "billing" in state.query:
        return "billing_agent"
    return "general_agent"
 
graph.add_node("router", router)
graph.add_node("billing_agent", billing_agent)
graph.add_node("general_agent", general_agent)
 
graph.add_conditional_edges("router", router) 
# The conditional logic decides where to go next

Summary

Start with a single agent. Only move to Multi-Agent when:

  1. The context window is too full.
  2. The prompt instructions are conflicting (e.g., "Be creative" vs "Be strict").
  3. You need clear security boundaries between capabilities.