Multi-Agent Architecture: Building Systems That Think Together

Why Multiple Agents Beat Single Agents

Here's something we learned the hard way: throwing more capabilities at a single agent doesn't scale. At some point, your super-agent becomes a confused mess trying to juggle too many responsibilities.

Think about how real teams work. You don't have one person doing sales, engineering, support, and legal. You have specialists who collaborate. Multi-agent systems work the same way. Each agent focuses on what it does best, and they coordinate to solve complex problems together.

We built our first production multi-agent system two years ago for a due diligence platform. A single agent kept getting confused between financial analysis and legal document review. When we split it into specialized agents, accuracy jumped 40% and processing time dropped by half.

The question isn't whether you need multiple agents. It's when you've outgrown a single agent and how to architect the transition.

Agent Communication Patterns

Before agents can work together, they need to talk to each other. The communication pattern you choose fundamentally shapes what your system can do.

Direct Messaging (Point-to-Point)

The simplest pattern. Agent A sends a message directly to Agent B and waits for a response.

// Research Agent asks Data Agent for information
const response = await dataAgent.query({
  from: 'research-agent',
  request: 'Get quarterly revenue for companies in healthcare sector',
  priority: 'high',
  timeout: 30000
});

When to use it:

• Two agents need tight coordination
• Low latency requirements
• Simple request-response workflows

The downside: It creates tight coupling. If Agent B goes down, Agent A is stuck waiting. It also doesn't scale well when you need to broadcast information.

Publish-Subscribe (Event-Driven)

Agents publish events to topics. Other agents subscribe to topics they care about. Nobody needs to know who's listening.

// When a new document arrives, publish an event
eventBus.publish('document.received', {
  documentId: 'doc-123',
  type: 'contract',
  source: 'client-upload',
  timestamp: Date.now()
});

// Legal Agent subscribes to contract events
eventBus.subscribe('document.received', async (event) => {
  if (event.type === 'contract') {
    await legalAgent.startReview(event.documentId);
  }
});

// Compliance Agent also subscribes
eventBus.subscribe('document.received', async (event) => {
  await complianceAgent.checkRequirements(event.documentId);
});

When to use it:

• Multiple agents need the same information
• Agents should operate independently
• You want loose coupling and scalability

The downside: Harder to debug because there's no clear call stack. Events can get lost if subscribers fail.

Shared Blackboard

All agents read and write to a shared workspace. Think of it like a whiteboard in a conference room that everyone can update.

// Blackboard structure for a research task
const blackboard = {
  task: {
    goal: 'Analyze market opportunity for AI in healthcare',
    deadline: '2025-05-20',
    status: 'in_progress'
  },
  findings: {
    marketSize: { value: '$45B', source: 'ResearchAgent', confidence: 0.85 },
    competitors: { value: [...], source: 'CompetitorAgent', confidence: 0.9 },
    regulations: { value: [...], source: 'LegalAgent', confidence: 0.95 }
  },
  openQuestions: [
    'What is the reimbursement landscape?',
    'Key partnerships to consider?'
  ]
};

When to use it:

• Complex problems requiring multiple perspectives
• Agents need to build on each other's work
• The problem structure emerges during solving

The downside: Concurrency gets tricky. Multiple agents writing to the same area can create conflicts.

Message Broker (Queue-Based)

Agents communicate through a central message broker. Messages queue up and get processed in order.

We typically use a combination. Direct messaging for time-critical coordination. Pub-sub for broadcasting status updates. Message queues for work distribution.

Coordination Strategies

Having agents that can communicate is just the start. You need strategies for how they work together.

Hierarchical Coordination

One agent acts as the manager. It receives tasks, breaks them down, assigns work to sub-agents, and aggregates results.

                    Manager Agent
                         |
        +----------------+----------------+
        |                |                |
   Research Agent   Analysis Agent   Writing Agent

class ManagerAgent {
  async handleTask(task) {
    // Break down the task
    const subtasks = await this.decompose(task);

    // Assign to specialists
    const assignments = [
      { agent: this.researchAgent, task: subtasks.research },
      { agent: this.analysisAgent, task: subtasks.analysis }
    ];

    // Execute in parallel where possible
    const results = await Promise.all(
      assignments.map(a => a.agent.execute(a.task))
    );

    // Aggregate and synthesize
    return this.synthesize(results);
  }
}

Best for: Well-defined workflows, clear task boundaries, when you need predictability.

Market-Based Coordination

Agents bid for tasks based on their capabilities and availability. The best-suited agent wins the work.

class TaskAuction {
  async assignTask(task) {
    // Announce task to all capable agents
    const bids = await Promise.all(
      this.agents.map(agent => agent.bid(task))
    );

    // Each bid includes capability score, availability, estimated time
    // {
    //   agent: 'legal-agent-2',
    //   capability: 0.95,
    //   availability: 0.8,
    //   estimatedTime: 120,
    //   price: 0.15  // cost in compute units
    // }

    // Select winner based on scoring function
    const winner = this.selectBest(bids, {
      weights: { capability: 0.5, time: 0.3, price: 0.2 }
    });

    return winner.agent.execute(task);
  }
}

Best for: Dynamic workloads, heterogeneous agents, when load balancing matters.

Collaborative Consensus

Agents discuss and reach agreement before acting. Nobody unilaterally makes decisions.

class ConsensusGroup {
  async decide(proposal) {
    // Each agent evaluates the proposal
    const votes = await Promise.all(
      this.agents.map(agent => agent.evaluate(proposal))
    );

    // Check for consensus (e.g., 2/3 majority)
    const approvals = votes.filter(v => v.approve).length;
    const threshold = Math.ceil(this.agents.length * 0.67);

    if (approvals >= threshold) {
      return { approved: true, confidence: approvals / this.agents.length };
    }

    // If no consensus, agents share reasoning and try again
    const reasoning = votes.map(v => v.reasoning);
    return this.deliberate(proposal, reasoning);
  }
}

Best for: High-stakes decisions, when multiple perspectives matter, reducing single-agent errors.

Task Decomposition: Breaking Problems Down

Good task decomposition is an art. Break tasks too fine and you drown in coordination overhead. Break them too coarse and you lose the benefits of specialization.

Functional Decomposition

Split by the type of work. Research tasks go to research agents. Writing tasks go to writing agents.

const decompositionRules = {
  'market-analysis': {
    subtasks: [
      { type: 'research', agent: 'market-research-agent' },
      { type: 'competitor-analysis', agent: 'competitor-agent' },
      { type: 'financial-modeling', agent: 'finance-agent' },
      { type: 'report-synthesis', agent: 'writing-agent', dependsOn: ['research', 'competitor-analysis', 'financial-modeling'] }
    ]
  }
};

Data Decomposition

Split by the data being processed. Each agent handles a subset.

// Processing 10,000 documents for contract review
const documents = await getDocuments();
const chunks = chunkArray(documents, 1000);

// Distribute across agents
const results = await Promise.all(
  chunks.map((chunk, i) =>
    contractAgents[i % contractAgents.length].process(chunk)
  )
);

Recursive Decomposition

Let agents decompose their own subtasks. This handles complex, unpredictable problems.

class RecursiveAgent {
  async solve(problem) {
    // Assess if problem is solvable directly
    if (this.canSolveDirectly(problem)) {
      return this.directSolve(problem);
    }

    // Decompose into subproblems
    const subproblems = await this.decompose(problem);

    // Solve each (may recurse further)
    const solutions = await Promise.all(
      subproblems.map(sp => this.solve(sp))
    );

    // Combine solutions
    return this.combine(solutions);
  }
}

Decomposition Decision Matrix

Agent Specialization

Specialized agents consistently outperform generalists in their domain. Here's how we think about specialization.

Depth vs. Breadth Tradeoff

Generalist Agent
+------------------+
| Can do everything|
| Medium at all    |
+------------------+

Specialist Agents
+--------+ +--------+ +--------+
|Research| |Analysis| |Writing |
|Expert  | |Expert  | |Expert  |
+--------+ +--------+ +--------+

Designing Specialist Agents

Each specialist needs:

1. Domain-specific prompts tuned for their task
2. Specialized tools that generalists don't need
3. Curated knowledge relevant to their domain
4. Focused memory storing domain-specific learnings

const legalReviewAgent = {
  name: 'legal-review-agent',

  systemPrompt: `You are a legal document review specialist.
    Focus on: contract terms, liability clauses, compliance requirements.
    Flag: unusual provisions, missing standard clauses, potential risks.
    Output format: structured JSON with severity ratings.`,

  tools: [
    'legal-database-search',
    'precedent-lookup',
    'clause-comparison',
    'regulatory-checker'
  ],

  knowledge: [
    'contract-law-embeddings',
    'company-legal-policies',
    'historical-contract-reviews'
  ],

  memory: {
    store: 'legal-agent-memory',
    retain: ['common-issues-found', 'client-preferences', 'jurisdiction-rules']
  }
};

When Specialization Goes Wrong

We've seen teams over-specialize. Signs you've gone too far:

• Agents spend more time coordinating than working
• Simple tasks get bounced between 5+ agents
• Adding new capabilities requires redesigning the entire system
• Domain boundaries become unclear

The fix: Start with fewer, broader agents. Specialize only when you see clear performance gains from splitting.

Consensus Mechanisms

When multiple agents analyze the same problem, they often disagree. You need mechanisms to resolve this.

Voting Systems

class MajorityVote {
  async decide(question, agents) {
    const answers = await Promise.all(
      agents.map(a => a.analyze(question))
    );

    // Group by answer
    const votes = {};
    for (const answer of answers) {
      const key = this.normalize(answer.conclusion);
      votes[key] = votes[key] || { count: 0, supporters: [] };
      votes[key].count++;
      votes[key].supporters.push(answer);
    }

    // Find majority
    const sorted = Object.entries(votes).sort((a, b) => b[1].count - a[1].count);
    const [winner, data] = sorted[0];

    return {
      conclusion: winner,
      confidence: data.count / agents.length,
      dissent: sorted.slice(1).map(([conclusion, d]) => ({
        conclusion,
        count: d.count,
        reasoning: d.supporters[0].reasoning
      }))
    };
  }
}

Weighted Consensus

Not all agents are equal. Give more weight to specialists or high-confidence answers.

class WeightedConsensus {
  async decide(question, agents) {
    const answers = await Promise.all(
      agents.map(a => a.analyze(question))
    );

    // Weight by agent expertise and self-reported confidence
    const weighted = answers.map(a => ({
      conclusion: a.conclusion,
      weight: a.confidence * this.getAgentExpertise(a.agent, question.domain)
    }));

    // Aggregate weighted scores
    return this.aggregateWeighted(weighted);
  }

  getAgentExpertise(agent, domain) {
    // Based on historical accuracy in this domain
    return this.expertiseScores[agent.id]?.[domain] || 0.5;
  }
}

Debate and Deliberation

Agents argue their positions and update based on others' reasoning.

class DebateConsensus {
  async decide(question, agents, maxRounds = 3) {
    let round = 0;
    let positions = await this.getInitialPositions(question, agents);

    while (round < maxRounds && !this.hasConsensus(positions)) {
      // Each agent sees others' positions and reasoning
      const sharedContext = this.formatPositions(positions);

      // Agents update their positions
      positions = await Promise.all(
        agents.map(async (agent, i) => {
          const updated = await agent.reconsider(question, {
            myPosition: positions[i],
            othersPositions: sharedContext
          });
          return updated;
        })
      );

      round++;
    }

    return this.finalDecision(positions);
  }
}

Consensus Pattern Comparison

Real-World Architecture Examples

Let's look at some systems we've actually built.

Customer Support System

                        Router Agent
                             |
        +--------------------+--------------------+
        |                    |                    |
   Technical Agent     Billing Agent       General Agent
        |                    |                    |
   +----+----+          +----+----+              |
   |         |          |         |              |
 Debug    Docs      Refunds   Payment         FAQ
 Agent   Agent       Agent     Agent         Agent

Communication: Request-response for routing. Events for escalation. Shared customer context in database.

Coordination: Hierarchical with market-based fallback (if primary agent is overloaded, others can bid).

Results: 65% of tickets fully automated. Average resolution time dropped from 4 hours to 12 minutes for automated cases.

Research and Analysis Platform

   Orchestrator
        |
   +----+----+----+----+
   |    |    |    |    |
  Web  Data Legal Fin  Synthesis
  Agent Agent Agent Agent Agent

Communication: Shared blackboard for findings. Message queue for work distribution.

Coordination: Collaborative consensus for conclusions. Each agent contributes findings, synthesis agent resolves conflicts.

Decomposition: Functional by research domain. Recursive when initial research reveals new areas to explore.

Document Processing Pipeline

Ingestion -> Classification -> [Branch by type]
                                    |
             +----------------------+----------------------+
             |                      |                      |
       Contract Flow          Invoice Flow           Report Flow
             |                      |                      |
        [Legal Agent]          [Finance Agent]       [Analysis Agent]
             |                      |                      |
         Validation              Matching              Summarization
             |                      |                      |
        +----+                 +----+                  +----+
   Human Review           Auto-Process             Distribution

Communication: Event-driven pipeline. Each stage publishes completion events.

Coordination: Mostly sequential with parallel branches by document type.

Specialization: Document-type-specific agents with deep knowledge of their domain.

Error Handling and Resilience

Multi-agent systems fail in interesting ways. Here's what we've learned.

Failure Modes

Building Resilience

class ResilientAgentSystem {
  async executeWithFallback(task, primaryAgent, fallbackAgents) {
    try {
      return await this.withTimeout(
        primaryAgent.execute(task),
        30000
      );
    } catch (error) {
      this.logger.warn(`Primary agent failed: ${error.message}`);

      // Try fallbacks in order
      for (const fallback of fallbackAgents) {
        try {
          return await this.withTimeout(
            fallback.execute(task),
            30000
          );
        } catch (fallbackError) {
          this.logger.warn(`Fallback failed: ${fallbackError.message}`);
        }
      }

      // All agents failed - escalate to human
      return this.escalateToHuman(task, error);
    }
  }
}

Circuit Breakers

When an agent starts failing repeatedly, stop sending it work temporarily.

class CircuitBreaker {
  constructor(threshold = 5, resetTime = 60000) {
    this.failures = 0;
    this.threshold = threshold;
    this.resetTime = resetTime;
    this.state = 'closed'; // closed = normal, open = blocking
  }

  async execute(fn) {
    if (this.state === 'open') {
      throw new Error('Circuit breaker is open');
    }

    try {
      const result = await fn();
      this.failures = 0;
      return result;
    } catch (error) {
      this.failures++;
      if (this.failures >= this.threshold) {
        this.state = 'open';
        setTimeout(() => {
          this.state = 'half-open';
        }, this.resetTime);
      }
      throw error;
    }
  }
}

Scaling Multi-Agent Systems

As your system grows, you'll hit scaling challenges.

Horizontal Scaling

Run multiple instances of each agent type. Use load balancers to distribute work.

const agentPool = {
  'research-agent': {
    instances: ['research-1', 'research-2', 'research-3'],
    loadBalancer: 'round-robin'
  },
  'analysis-agent': {
    instances: ['analysis-1', 'analysis-2'],
    loadBalancer: 'least-connections'
  }
};

Scaling Metrics to Watch

Getting Started

If you're building your first multi-agent system, here's our advice:

1. Start with two agents. Manager and worker. Get coordination working before adding complexity.

2. Use simple communication first. Direct messaging is easier to debug than pub-sub.

3. Instrument everything. Log every agent decision, every message, every error. You'll need it.

4. Design for failure. Assume agents will crash. Build resilience from day one.

5. Measure coordination overhead. If agents spend more time talking than working, simplify.

The architecture patterns here aren't theoretical. We've built systems using each of them. The right choice depends on your specific problem, your team's expertise, and your scale requirements.

Multi-agent systems are harder to build than single agents. But for complex problems, they're often the only approach that works. Start simple, measure everything, and evolve your architecture based on what you learn.

If you're exploring multi-agent architectures and want to talk through your use case, reach out. We've learned a lot from the systems we've built, and we're happy to share what we know.