How I Fixed My Agent’s Retry Logic to Handle 429 Errors Without Breaking the Budget

One of the most common issues with autonomous AI agents is handling API rate limits (429 errors) without accidentally burning through your budget. Here’s how I fixed it.

The Problem

When an agent encounters a 429 error, it often retries indefinitely, leading to unexpected costs. The default behavior of simply retrying on failure can result in:

• Unlimited API calls if the rate limit isn’t respected
• Budget overruns due to repeated failed attempts
• No clear stop condition for error handling

The Fix

I implemented a local fault-injecting mock to simulate 429 errors and test the retry logic before deploying it to production. This ensures that:

1. The agent respects the Retry-After header when available
2. A time budget is enforced to prevent infinite retries
3. Budget constraints are respected by tracking spend per call

Here’s a snippet of the fix:

import time
import requests
from requests.exceptions import HTTPError

def make_request_with_retry(url, max_retries=3, timeout=10):
    for attempt in range(max_retries):
        try:
            response = requests.get(url, timeout=timeout)
            response.raise_for_status()
            return response
        except HTTPError as e:
            if e.response.status_code == 429:
                retry_after = int(e.response.headers.get('Retry-After', 5))
                time.sleep(retry_after)
            else:
                raise
    raise Exception(f"Max retries ({max_retries}) exceeded")

Key Takeaways

• Always respect the Retry-After header to avoid unnecessary retries
• Set a time budget to prevent infinite loops
• Track spend per call to ensure you don’t exceed your budget

This approach ensures that your agents are resilient to rate limits while keeping costs under control.

Have you encountered similar issues with your agents? What strategies have you found effective?

Read more about managing LLM costs here.