Multi-Agent Systems: When One AI Isn't Enough
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Multi-Agent Systems: When One AI Isn't Enough

March 22, 20263 min read1 views

Some tasks genuinely exceed single-agent capabilities. Multi-agent architectures—where specialized AIs collaborate—can tackle complexity that monolithic approaches cannot.

When to Use Multiple Agents

Multi-agent systems add value when:

  • Different expertise is needed: Research, coding, and review require different skills
  • Tasks benefit from critique: One agent proposes, another evaluates
  • Parallelization helps: Independent subtasks can run simultaneously
  • Specialization improves quality: Focused agents outperform generalists

Agent Specialization: Divide and Conquer

interface Agent {
  name: string
  systemPrompt: string
  capabilities: string[]
  model: string
}

const agents: Agent[] = [
  {
    name: 'researcher',
    systemPrompt: 'You are a research specialist. Your job is to find and summarize relevant information. Be thorough and cite sources.',
    capabilities: ['web_search', 'read_document'],
    model: 'claude-3-sonnet', // Good at research
  },
  {
    name: 'coder',
    systemPrompt: 'You are a senior software engineer. Write clean, tested, production-ready code.',
    capabilities: ['write_code', 'run_tests'],
    model: 'claude-3-5-sonnet', // Best at coding
  },
  {
    name: 'reviewer',
    systemPrompt: 'You are a code reviewer. Find bugs, security issues, and suggest improvements. Be critical but constructive.',
    capabilities: ['analyze_code'],
    model: 'claude-3-opus', // Best at careful analysis
  },
]

Communication Protocols Between Agents

interface AgentMessage {
  from: string
  to: string
  type: 'request' | 'response' | 'feedback'
  content: any
  metadata: {
    conversationId: string
    parentMessageId?: string
    timestamp: number
  }
}

class AgentCommunication {
  private messageQueue: AgentMessage[] = []
  
  async send(message: AgentMessage) {
    this.messageQueue.push(message)
    await this.notifyAgent(message.to, message)
  }
  
  async waitForResponse(conversationId: string, timeout: number): Promise {
    return new Promise((resolve, reject) => {
      const checkInterval = setInterval(() => {
        const response = this.messageQueue.find(
          m => m.metadata.conversationId === conversationId && m.type === 'response'
        )
        if (response) {
          clearInterval(checkInterval)
          resolve(response)
        }
      }, 100)
      
      setTimeout(() => {
        clearInterval(checkInterval)
        reject(new Error('Response timeout'))
      }, timeout)
    })
  }
}

Orchestration Patterns

// Sequential: Each agent passes work to the next
async function sequentialPipeline(task: string) {
  const research = await agents.researcher.process(task)
  const code = await agents.coder.process({ task, research })
  const review = await agents.reviewer.process({ task, code })
  
  if (review.issues.length > 0) {
    const revised = await agents.coder.process({ task, code, feedback: review })
    return revised
  }
  return code
}

// Parallel: Independent tasks run simultaneously
async function parallelResearch(topics: string[]) {
  const results = await Promise.all(
    topics.map(topic => agents.researcher.process(topic))
  )
  return agents.synthesizer.process({ results })
}

// Debate: Agents argue to find best solution
async function debatePattern(problem: string, rounds: number = 3) {
  let proposals = await Promise.all(
    [agents.optimist, agents.pessimist, agents.pragmatist]
      .map(agent => agent.propose(problem))
  )
  
  for (let i = 0; i < rounds; i++) {
    const critiques = await Promise.all(
      proposals.map((p, j) => 
        agents.critic.critique(p, proposals.filter((_, k) => k !== j))
      )
    )
    proposals = await Promise.all(
      proposals.map((p, j) => agents[j].revise(p, critiques[j]))
    )
  }
  
  return agents.judge.select(proposals)
}

Debugging Multi-Agent Systems

// Comprehensive logging
class AgentLogger {
  log(event: AgentEvent) {
    console.log(JSON.stringify({
      timestamp: Date.now(),
      agent: event.agent,
      action: event.action,
      input: event.input,
      output: event.output,
      duration: event.duration,
      tokens: event.tokens,
    }))
  }
}

// Visualize agent interactions
function generateMermaidDiagram(conversation: AgentMessage[]) {
  let diagram = 'sequenceDiagram\n'
  for (const msg of conversation) {
    diagram += `  ${msg.from}->>${msg.to}: ${msg.type}\n`
  }
  return diagram
}

Key Takeaways

Specialization beats generalization. Focused agents with clear roles outperform jack-of-all-trades.

Communication protocols matter. Clear message formats prevent confusion and enable debugging.

Orchestration patterns vary. Sequential, parallel, and debate patterns suit different problems.

Debugging is harder. Comprehensive logging and visualization are essential.

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