Three AI Agent Failure Modes That Traditional Monitoring Will Never Catch

One of my agents exited cleanly at 3 AM, another sat "healthy" while doing zero useful work for four hours, and a third burned through $50 in API credits in 40 minutes without throwing a single error. Those incidents looked unrelated at first. They weren't. All three slipped past the usual stack of process checks, log watchers, and CPU or memory alerts because those tools were measuring infrastructure symptoms, not whether the agent was still doing useful work. 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