Claude Agent Swarms: Horizontal Fan-Out & Parallel Execution

1. Swarms vs Teams: Horizontal Scaling Mechanics

While an Agent Team operates as a small council of specialized experts, an Agent Swarm is composed of dozens or hundreds of homogenous worker instances executing parallel operations across distributed data shards:

flowchart TD
    Queue[Task Queue: 10,000 Source Files] --> Gov[Token & Rate-Limit Governor]
    Gov --> W1[Worker Agent 1: Haiku 5.5]
    Gov --> W2[Worker Agent 2: Haiku 5.5]
    Gov --> W3[Worker Agent 3: Haiku 5.5]
    Gov --> Wn[Worker Agent N: Haiku 5.5]
    W1 --> Res1[Extracted AST Telemetry]
    W2 --> Res2[Extracted AST Telemetry]
    W3 --> Res3[Extracted AST Telemetry]
    Wn --> Resn[Extracted AST Telemetry]
    Res1 --> Reducer[Consolidator Agent: Sonnet 5.5]
    Res2 --> Reducer
    Res3 --> Reducer
    Resn --> Reducer
    Reducer --> Report[Master Enterprise Audit Matrix]

2. High-Throughput Concurrency Governor

Executing hundreds of concurrent agent requests against the Anthropic API requires a rate-limit token bucket governor to maximize throughput without triggering HTTP 429 backpressure:

swarm_governor.py

import asyncio
import os
from anthropic import AsyncAnthropic

client = AsyncAnthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

async def worker_task(file_id: int, semaphore: asyncio.Semaphore):
    async with semaphore:
        # Claude Haiku 5.5 handles high-velocity parallel scanning
        response = await client.messages.create(
            model="claude-3-5-haiku-20241022",
            max_tokens=512,
            messages=[{
                "role": "user",
                "content": f"Scan mock document #{file_id} for exposed secrets or credentials. Return JSON: {{'clean': bool, 'findings': []}}"
            }]
        )
        print(f"[WORKER {file_id:03d}]: Finished audit")
        return response.content[0].text

async def run_swarm(total_tasks=50, max_concurrent=10):
    semaphore = asyncio.Semaphore(max_concurrent)
    tasks = [worker_task(i, semaphore) for i in range(1, total_tasks + 1)]
    results = await asyncio.gather(*tasks)
    print(f"\nSwarm complete: Processed {len(results)} tasks concurrently.")

if __name__ == "__main__":
    asyncio.run(run_swarm(30, 8))

1. Swarms vs Teams: Horizontal Scaling Mechanics

2. High-Throughput Concurrency Governor

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