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:
- The agent respects the
Retry-Afterheader when available - A time budget is enforced to prevent infinite retries
- 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-Afterheader 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?
