Complex tasks often benefit from multiple specialized agents working together. Multi-agent architectures enable division of labor, specialized expertise, and parallel execution.

Why Multi-Agent?

Single agents struggle with tasks that require:

• Multiple areas of expertise (research + coding + writing)
• Parallel processing of independent subtasks
• Quality assurance (separate review from creation)
• Different trust/permission levels

Multi-Agent Patterns

Supervisor pattern: A coordinator agent delegates to specialized worker agents.

Supervisor → Researcher (search, read)
           → Coder (write, test, debug)
           → Writer (draft, edit, format)
           → Reviewer (quality check, feedback)

Debate/critique pattern: Multiple agents discuss and critique each other's outputs.

• Agent A proposes a solution
• Agent B critiques and suggests improvements
• Agent A revises based on feedback
• Repeat until consensus

Pipeline pattern: Agents process sequentially, each adding value.

Data Collector → Analyzer → Report Writer → Quality Checker → Publisher

Swarm pattern: Agents self-organize to handle incoming tasks dynamically.

Implementation with LangGraph

from langgraph.graph import StateGraph

def supervisor(state):
    """Route task to appropriate agent."""
    decision = llm.invoke(f"""
    Current task: {state['current_task']}
    Available agents: researcher, coder, writer, reviewer
    
    Which agent should handle this task? Consider the task requirements
    and each agent's specialization.
    """)
    return {"next_agent": parse_decision(decision)}

def researcher(state):
    """Search and synthesize information."""
    result = research_agent.invoke(state["current_task"])
    return {"results": state["results"] + [result]}

# Build the graph
graph = StateGraph(MultiAgentState)
graph.add_node("supervisor", supervisor)
graph.add_node("researcher", researcher)
graph.add_node("coder", coder)
graph.add_node("writer", writer)

graph.add_conditional_edges("supervisor", route_to_agent)
for agent in ["researcher", "coder", "writer"]:
    graph.add_edge(agent, "supervisor")  # Return to supervisor

Communication Between Agents

Agents can communicate through:

• Shared state: All agents read/write to a common state object
• Message passing: Agents send structured messages to specific other agents
• Blackboard: A shared workspace where agents post findings and read others' contributions
• Event-driven: Agents subscribe to events and react to relevant triggers

Challenges

• Infinite loops: Agents passing work back and forth without progress. Set maximum iterations.
• Conflicting outputs: Agents disagree on the correct approach. Need a resolution mechanism.
• Context management: Each agent's context window is limited. Summarize shared state effectively.
• Cost multiplication: Multiple agents = multiple LLM calls. Budget carefully.