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Rate Limiting for AI Agents: Why Token Buckets Beat Simple Throttling

Dr. Mara Solaris
May 07, 2026 · 2 min read
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Rate Limiting for AI Agents: Why Token Buckets Beat Simple Throttling

Rate Limiting for AI Agents: Why Token Buckets Beat Simple Throttling

When you're running 200 agents through a single gateway, naive rate limiting — "max 100 requests per minute" — creates more problems than it solves. Agents burst, queue, timeout, and retry, creating cascading failures that amplify the original constraint.

The Problem with Simple Rate Limits

Fixed-window rate limiting treats all requests equally:

Token Bucket Algorithm

SpiderGate uses an adaptive token bucket approach:

How It Works

Each agent gets a "bucket" that fills with tokens at a configurable rate. Each request consumes tokens proportional to its cost (estimated input + output tokens). When the bucket is empty, requests queue until tokens replenish.

Why It's Better

Priority Tiers

SpiderGate supports 4 priority tiers:

| Tier | Use Case | Bucket Size | Refill Rate | |------|----------|-------------|-------------| | Critical | Customer-facing agents | 10,000 tokens | 2,000/s | | High | Revenue-impacting workflows | 5,000 tokens | 1,000/s | | Normal | Standard automation | 2,000 tokens | 500/s | | Low | Background/batch processing | 500 tokens | 100/s |

Backpressure Signals

When an agent's bucket runs low, SpiderGate can:

  1. Queue — hold the request and process when capacity is available

  2. Downgrade — route to a cheaper model automatically

  3. Reject — return a 429 with retry-after header

  4. Alert — notify the team that an agent is hitting limits

Results

Teams migrating from simple rate limits to token buckets see:


Token bucket rate limiting is configurable per-agent, per-team, or globally.

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