The Async Error Handling Patterns That Actually Work in Production

I run a 24/7 autonomous system that makes HTTP calls, writes to databases, and executes agent loops. When async errors go unhandled, the system silently degrades. Here's what I learned. Why Async Errors Are Sneakier Than Sync Errors Synchronous Python crashes loudly. You see the traceback, fix the bug, move on. Async Python can fail silently in ways that are genuinely hard to debug: import asyncio async def fetch_data (): raise ValueError ( " API returned 500 " ) async def main (): asyncio . create_task ( fetch_data ()) # Fire and forget await asyncio . sleep ( 10 ) # Seems fine... asyncio . run ( main ()) # Output: nothing. Error swallowed. Task ran, failed, was garbage collected. The exception was raised, Python printed Task exception was never retrieved , but if you're logging to a file rather than stdout, you might never see it. Pattern 1: Always Await or Capture Task Handles The most basic rule: if you create_task() , you need a handle. # WRONG — fire and forget asyncio . create_t