Multi-Agent Orchestration Pattern

The Agent Orchestration pattern coordinates multiple specialized agents to collaboratively complete complex tasks that exceed the capabilities of a single agent.

Overview

Pattern Description

This pattern divides complex work among specialized agents:

• Orchestrator: Coordinates the overall workflow
• Planner: Breaks down tasks into subtasks
• Executor: Performs the actual work
• Reviewer: Validates quality and correctness

Components

Orchestrator Agent

• Purpose: Central coordinator for multi-agent workflows
• Key Responsibilities:
  • Task distribution
  • Progress monitoring
  • Result aggregation
  • Error handling and recovery

Planner Agent

• Purpose: Strategic task decomposition
• Key Responsibilities:
  • Analyzing complex requests
  • Breaking down into subtasks
  • Identifying dependencies
  • Estimating requirements

Executor Agent

• Purpose: Task execution
• Key Responsibilities:
  • Executing assigned subtasks
  • Using available tools
  • Reporting progress
  • Handling execution errors

Reviewer Agent

• Purpose: Quality assurance
• Key Responsibilities:
  • Validating outputs
  • Checking for errors
  • Suggesting improvements
  • Approving or rejecting work

Shared Resources

Orchestration Patterns

Hierarchical

Orchestrator
    ├── Planner
    ├── Executor 1
    ├── Executor 2
    └── Reviewer

Pipeline

Planner → Executor → Reviewer → Orchestrator

Collaborative

Agent A ←→ Agent B ←→ Agent C
         ↓
    Orchestrator

Communication Protocol

{
  "message_type": "task_assignment",
  "from": "orchestrator",
  "to": "executor_1",
  "payload": {
    "task_id": "task_123",
    "description": "Implement user authentication",
    "context": { "relevant_files": ["auth.py"] },
    "constraints": { "time_limit": 300 }
  }
}

Workflow States

When to Use

• Tasks requiring multiple specialized capabilities
• Complex projects needing planning and review
• Workflows benefiting from parallel execution
• Systems requiring quality gates
• Long-running tasks needing coordination

Implementation Considerations

Agent Communication

• Use message queues for async communication
• Implement timeouts and retries
• Handle agent failures gracefully

Context Management

• Maintain shared context store
• Implement context windowing for long tasks
• Handle context conflicts

Error Recovery

• Define fallback strategies
• Implement checkpoint/restart
• Log all agent interactions

Benefits

Related Patterns