AI Agent Orchestration Patterns

Orchestrating multiple AI agents requires careful design. The way agents coordinate determines whether your system succeeds or fails. Here are proven patterns for building robust multi-agent systems.

Orchestration vs Choreography

Orchestration (Centralized)

A central orchestrator controls the flow:

Orchestrator → Agent A → Orchestrator → Agent B → Orchestrator → Agent C

Pros:

• Centralized control
• Easy to debug
• Clear workflow visibility

Cons:

• Single point of failure
• Can become bottleneck
• Less flexible

Choreography (Decentralized)

Agents coordinate among themselves:

Agent A → Agent B → Agent C → Agent A

Pros:

• More resilient
• Better scalability
• Flexible

Cons:

• Harder to debug
• Less visibility
• Complex coordination

Pattern 1: Sequential Pipeline

Simple Linear Flow

class SequentialOrchestrator {
  async execute(task: Task): Promise<Result> {
    const step1 = await this.agentA.execute(task);
    const step2 = await this.agentB.execute(step1);
    const step3 = await this.agentC.execute(step2);
    return step3;
  }
}

Use when: Steps have clear dependencies

Example: Research → Analysis → Writing → Review

With Error Handling

class RobustSequentialOrchestrator {
  async execute(task: Task): Promise<Result> {
    try {
      const step1 = await this.agentA.execute(task);
      const step2 = await this.agentB.execute(step1);
      return await this.agentC.execute(step2);
    } catch (error) {
      // Rollback previous steps
      await this.rollback();
      throw error;
    }
  }
}

Pattern 2: Parallel Execution

Independent Tasks

class ParallelOrchestrator {
  async execute(task: Task): Promise<Result> {
    // Execute agents in parallel
    const [resultA, resultB, resultC] = await Promise.all([
      this.agentA.execute(task.partA),
      this.agentB.execute(task.partB),
      this.agentC.execute(task.partC),
    ]);
    
    // Combine results
    return this.combineResults(resultA, resultB, resultC);
  }
}

Use when: Tasks are independent

Example: Fetching data from multiple APIs simultaneously

With Dependency Management

class DependencyAwareOrchestrator {
  async execute(task: Task): Promise<Result> {
    // Build dependency graph
    const graph = this.buildDependencyGraph(task);
    
    // Execute in topological order
    const results = new Map();
    
    for (const node of graph.topologicalSort()) {
      const dependencies = node.dependencies.map(dep => results.get(dep));
      const result = await this.agents[node.agent].execute({
        ...node.task,
        dependencies,
      });
      results.set(node.id, result);
    }
    
    return results.get(graph.finalNode);
  }
}

Pattern 3: Hierarchical Decomposition

Master-Slave Pattern

class HierarchicalOrchestrator {
  async execute(task: Task): Promise<Result> {
    // Master agent breaks down task
    const plan = await this.masterAgent.plan(task);
    
    // Delegate to specialist agents
    const results = await Promise.all(
      plan.subtasks.map(subtask => 
        this.routeToSpecialist(subtask)
      )
    );
    
    // Master agent synthesizes results
    return await this.masterAgent.synthesize(results);
  }
  
  private async routeToSpecialist(subtask: Subtask): Promise<Result> {
    const agent = this.selectAgent(subtask);
    return await agent.execute(subtask);
  }
}

Use when: Task can be decomposed into specialized subtasks

Pattern 4: Event-Driven

Pub/Sub Pattern

class EventDrivenOrchestrator {
  private eventBus = new EventBus();
  
  constructor() {
    // Agents subscribe to events
    this.eventBus.subscribe('task.started', this.agentA);
    this.eventBus.subscribe('step1.completed', this.agentB);
    this.eventBus.subscribe('step2.completed', this.agentC);
  }
  
  async execute(task: Task): Promise<void> {
    // Publish initial event
    await this.eventBus.publish('task.started', task);
    // Agents react to events and publish new events
  }
}

Use when: Loose coupling is desired

Workflow Engine

class WorkflowOrchestrator {
  async execute(workflow: Workflow): Promise<Result> {
    const state = { currentStep: 0, data: {} };
    
    while (state.currentStep < workflow.steps.length) {
      const step = workflow.steps[state.currentStep];
      const agent = this.agents[step.agent];
      
      // Execute step
      const result = await agent.execute({
        ...step.task,
        context: state.data,
      });
      
      // Update state
      state.data[step.id] = result;
      
      // Determine next step
      state.currentStep = this.evaluateNextStep(step, result);
    }
    
    return state.data;
  }
}

Pattern 5: Request-Response Chain

Chain of Responsibility

class ChainOrchestrator {
  private chain: Agent[] = [];
  
  addAgent(agent: Agent): void {
    this.chain.push(agent);
  }
  
  async execute(task: Task): Promise<Result> {
    let currentTask = task;
    
    for (const agent of this.chain) {
      // Agent can choose to handle or pass along
      if (await agent.canHandle(currentTask)) {
        currentTask = await agent.handle(currentTask);
      } else {
        // Pass to next agent
        continue;
      }
    }
    
    return currentTask;
  }
}

Use when: Multiple agents might handle the same task

Pattern 6: Market-Based

Auction Pattern

class MarketOrchestrator {
  async execute(task: Task): Promise<Result> {
    // Agents bid on task
    const bids = await Promise.all(
      this.agents.map(agent => agent.bid(task))
    );
    
    // Select best bid
    const winner = this.selectWinner(bids);
    
    // Execute with winning agent
    return await winner.agent.execute(task);
  }
  
  private selectWinner(bids: Bid[]): Bid {
    return bids.reduce((best, current) => 
      current.score > best.score ? current : best
    );
  }
}

Use when: Multiple agents can handle task, want best fit

Pattern 7: Iterative Refinement

Feedback Loop

class IterativeOrchestrator {
  async execute(task: Task, maxIterations = 5): Promise<Result> {
    let result = await this.initialAgent.execute(task);
    
    for (let i = 0; i < maxIterations; i++) {
      // Reviewer evaluates quality
      const review = await this.reviewerAgent.review(result);
      
      if (review.quality >= task.qualityThreshold) {
        return result;
      }
      
      // Refiner improves result
      result = await this.refinerAgent.refine(result, review.feedback);
    }
    
    return result;
  }
}

Use when: Quality requires iteration

Real-World Example: Content Creation System

class ContentCreationOrchestrator {
  async createContent(brief: ContentBrief): Promise<Content> {
    // 1. Research phase (parallel)
    const [research, competitorAnalysis] = await Promise.all([
      this.researchAgent.execute(brief),
      this.competitorAgent.execute(brief),
    ]);
    
    // 2. Planning phase
    const outline = await this.outlineAgent.execute({
      brief,
      research,
      competitors: competitorAnalysis,
    });
    
    // 3. Writing phase (sequential sections)
    const sections = [];
    for (const section of outline.sections) {
      const content = await this.writingAgent.execute(section);
      sections.push(content);
    }
    
    // 4. Review phase (parallel checks)
    const [factCheck, styleCheck, seoCheck] = await Promise.all([
      this.factChecker.execute(sections),
      this.styleAgent.execute(sections),
      this.seoAgent.execute(sections),
    ]);
    
    // 5. Refinement
    if (factCheck.issues.length > 0 || styleCheck.score < 0.8) {
      return await this.refinerAgent.execute({
        sections,
        factCheck,
        styleCheck,
      });
    }
    
    return { sections, seo: seoCheck };
  }
}

Choosing the Right Pattern

Decision Matrix

Pattern	Complexity	Scalability	Flexibility	Use Case
Sequential	Low	Low	Low	Linear workflows
Parallel	Medium	High	Medium	Independent tasks
Hierarchical	High	High	High	Complex decomposition
Event-Driven	High	Very High	Very High	Loose coupling
Chain	Medium	Medium	High	Multiple handlers
Market	Medium	High	High	Best-fit selection
Iterative	Medium	Low	Medium	Quality refinement

Best Practices

1. Start simple: Begin with sequential, add complexity as needed
2. Handle failures: Plan for agent failures
3. Monitor everything: Track agent performance
4. Test patterns: Verify orchestration logic
5. Document flows: Make workflows explicit
6. Optimize bottlenecks: Identify and improve slow paths

Common Pitfalls

1. Over-engineering: Don't use complex patterns for simple tasks
2. Ignoring failures: Always handle agent failures
3. No monitoring: Can't improve what you don't measure
4. Tight coupling: Agents shouldn't know about each other
5. No rollback: Can't undo failed workflows

Conclusion

Orchestration patterns determine how agents work together. Choose based on:

• Task structure: Linear, parallel, or complex?
• Dependencies: Do tasks depend on each other?
• Quality requirements: Need iteration?
• Scalability needs: How many agents?

The right pattern makes the difference between a system that works and one that excels. Start simple, measure, and evolve your orchestration as needs grow.