Building Multi-Agent Systems with LLMs

Introduction

Multi-agent systems represent the next frontier in AI application development. Instead of relying on a single monolithic LLM call, we can orchestrate multiple specialized agents that collaborate, delegate, and reason together to solve complex problems.

In this post, we'll explore the core patterns behind building effective multi-agent systems and walk through a practical implementation.

Why Multi-Agent?

Single-agent architectures hit a ceiling when tasks require diverse expertise. Consider a software development workflow:

• Planning Agent breaks down requirements into tasks
• Coding Agent writes the implementation
• Review Agent checks for bugs and style issues
• Testing Agent generates and runs test cases

Each agent has a focused system prompt, its own tool set, and clear responsibilities.

Core Architecture Patterns

1. Orchestrator Pattern

The orchestrator pattern uses a central coordinator that delegates work to specialized sub-agents:

interface Agent {
  name: string;
  systemPrompt: string;
  tools: Tool[];
  execute(input: string): Promise<AgentResult>;
}

class Orchestrator {
  private agents: Map<string, Agent>;

  async run(task: string): Promise<string> {
    const plan = await this.planAgent.execute(task);

    for (const step of plan.steps) {
      const agent = this.agents.get(step.agentName);
      const result = await agent.execute(step.input);
      this.context.addResult(step.id, result);
    }

    return this.synthesize();
  }
}

2. Pipeline Pattern

Agents are chained in sequence, where each agent's output feeds into the next:

const pipeline = createPipeline([
  researchAgent,
  analysisAgent,
  writingAgent,
  reviewAgent,
]);

const result = await pipeline.execute(initialInput);

3. Debate Pattern

Multiple agents with different perspectives discuss and converge on a solution:

async function debate(
  topic: string,
  agents: Agent[],
  rounds: number,
): Promise<string> {
  let context = topic;

  for (let i = 0; i < rounds; i++) {
    for (const agent of agents) {
      const response = await agent.execute(context);
      context += `\n\n${agent.name}: ${response}`;
    }
  }

  return synthesizeDebate(context);
}

Communication Between Agents

Agents need a shared communication protocol. A message-passing system works well:

interface AgentMessage {
  from: string;
  to: string;
  type: "request" | "response" | "broadcast";
  content: string;
  metadata: Record<string, unknown>;
}

class MessageBus {
  private subscribers = new Map<string, (msg: AgentMessage) => void>();

  send(message: AgentMessage): void {
    const handler = this.subscribers.get(message.to);
    handler?.(message);
  }

  subscribe(agentId: string, handler: (msg: AgentMessage) => void): void {
    this.subscribers.set(agentId, handler);
  }
}

Practical Tips

1. Keep agents focused - Each agent should do one thing well
2. Design clear interfaces - Define input/output contracts between agents
3. Add observability - Log every agent interaction for debugging
4. Handle failures gracefully - Agents will fail; build retry and fallback logic
5. Start simple - Begin with two agents and scale up as needed

Conclusion

Multi-agent systems unlock powerful new capabilities by combining specialized AI components. Start with the orchestrator pattern, keep your agents focused, and iterate based on real-world performance.

The key insight is that coordination is harder than intelligence. Getting agents to work together effectively is the real engineering challenge.