Building Throttling Systems for Parallel AI Agents

Running 5 agents in parallel turns a few hours of work into about 40 minutes. But without throttling, those 5 agents will hammer your API provider and hit rate limits within seconds.

The Rate Limit Problem

Each Claude Code process makes API calls independently. Five processes running simultaneously means 5x the request rate. Most API providers enforce:

• Requests per minute - typically 50-100 for standard tiers
• Tokens per minute - a hard cap on total throughput
• Concurrent connections - some providers limit simultaneous requests

When agents hit these limits, they get 429 errors, retry aggressively, and create a thundering herd problem that makes everything slower.

A Simple Throttling Architecture

The system I built uses a shared semaphore file:

1. Request queue - each agent checks a shared lock before making API calls
2. Backoff scheduling - when one agent gets rate-limited, all agents slow down
3. Priority tiers - critical path agents get higher priority than background tasks
4. Cost tracking - a running total of spend across all agents with automatic pausing at thresholds

The implementation does not need to be complex. A simple file-based mutex with exponential backoff handles 90% of cases.

Practical Rate Limit Settings

For 5 parallel Claude Code agents on the standard API tier:

• Set each agent to a maximum of 8 requests per minute (40 total, under the 50 RPM limit)
• Add 2-second minimum spacing between requests from the same agent
• Implement a shared daily budget cap across all agents
• Log every API call with timestamps for debugging

The Cost Dimension

Throttling is not just about rate limits - it is about cost control. Five unthrottled agents can burn through $200 in API credits in an afternoon. Set alerts at $20, $50, and $100 daily spend. Auto-pause all agents if you hit the limit.

Monitor and Adjust

Track your actual usage patterns for a week before optimizing. Most developers over-throttle initially, which defeats the purpose of parallelism. Find the sweet spot where agents run fast without triggering limits.

Fazm is an open source macOS AI agent. Open source on GitHub.

---

More on This Topic

• LLM Control Plane Routing and Budget
• AI Agent Development Costs with Parallel Agents
• Claude Subscription Tiers and Parallel Agent Costs