Three AI Agent Failure Modes That Traditional Monitoring Will Never Catch

I run several AI agents in production — trading bots, data scrapers, monitoring agents. They run 24/7, unattended. Over the past few months, I've hit three failure modes that my existing monitoring (process checks, log watchers, CPU/memory alerts) completely missed. These aren't exotic edge cases. If you're running any long-lived AI agent, you'll probably hit all three eventually. Failure #1: The Silent Exit One of my agents exited cleanly at 3 AM. No traceback. No error log. No crash dump. The Python process simply stopped. My log monitoring saw nothing because there was nothing to log. I found out six hours later when I noticed the bot hadn't posted since 3 AM. What happened The OS killed the process for memory. The agent was slowly leaking — a library was caching LLM responses in memory without any eviction policy. RSS grew from 200MB to 4GB over a few days. The OOM killer sent SIGKILL, which leaves no Python traceback. Why traditional monitoring missed it Process monitoring (system