Silent Failure Detection

This skill provides a comprehensive framework for monitoring AI agents in production, specifically targeting 'silent failures' where systems remain operational but the underlying logic is broken. It enables developers to identify when agents skip mandatory tools, output nonsensical text, or enter runaway loops that spike token consumption. By implementing LLM-as-judge patterns and statistical anomaly detection, this skill ensures that agentic workflows remain reliable, cost-effective, and high-quality beyond what standard error logging can provide.

Key Features

Use Cases