Orchestrator-Worker Pattern

Overview

The Orchestrator-Worker pattern, documented by Anthropic in their multi-agent research system, separates coordination from execution. The orchestrator analyzes tasks and spawns specialized workers that operate in parallel, dramatically improving throughput for complex research and analysis tasks.

Architecture

        ┌─────────────────────────────────┐
        │         ORCHESTRATOR            │
        │  • Analyzes task requirements   │
        │  • Spawns specialized workers   │
        │  • Synthesizes results          │
        └────────────┬────────────────────┘
                     │
         ┌───────────┼───────────┐
         ▼           ▼           ▼
    ┌─────────┐ ┌─────────┐ ┌─────────┐
    │ Worker  │ │ Worker  │ │ Worker  │
    │  (API)  │ │ (Docs)  │ │ (Code)  │
    └─────────┘ └─────────┘ └─────────┘
         │           │           │
         └───────────┴───────────┘
                     │
              [Aggregated Results]

Key Characteristics

Dynamic Worker Spawning

The orchestrator creates workers based on task analysis:

async def process_query(query):
    # Orchestrator analyzes query
    aspects = analyze_aspects(query)

    # Spawn workers for each aspect
    workers = [
        spawn_worker(aspect, query)
        for aspect in aspects
    ]

    # Run in parallel
    results = await asyncio.gather(*workers)

    # Synthesize
    return synthesize(results)

Specialized Workers

Each worker focuses on a specific aspect:

• API Worker: Searches documentation and APIs
• Code Worker: Analyzes codebases
• Web Worker: Searches external resources
• Analysis Worker: Processes data

Result Synthesis

The orchestrator combines worker outputs:

• Deduplicates findings
• Resolves conflicts
• Structures final response
• Maintains coherence

Anthropic's Implementation

User queries flow through a lead agent that creates specialized subagents to search for different aspects simultaneously. This parallel execution significantly speeds up complex research tasks while maintaining quality.

Benefits

Parallelism

Workers execute simultaneously, reducing total latency.

Specialization

Each worker can be optimized for its specific task.

Scalability

Add more workers without changing orchestrator logic.

Fault Isolation

Worker failures don't crash the entire system.

Challenges

Emergent Behavior

Small changes to the orchestrator can unpredictably change how workers behave. Multi-agent systems have emergent properties that arise without specific programming.

Unproductive Loops

"You get into loops where agents will continue to talk, but not add anything material." Industry early adopters highlight this as a key challenge.

Context Management

Each worker has limited context; orchestrator must manage information flow effectively.

When to Use

Good fit:

• Research and analysis tasks
• Document processing at scale
• Multi-source information gathering
• Tasks with naturally parallel subtasks

Caution for:

• Simple, single-focus tasks
• Highly sequential workflows
• Real-time, low-latency requirements