Build an AI Agent Watchdog With Python and SQLite — Catch Silent Failures Before Users Do

Everyone's deploying AI agents in 2026. Almost nobody is watching what those agents actually do . I've been running local AI agents on my homelab for months — automating emails, summarizing docs, managing tasks. They work great. Until they don't. And when an agent silently starts hallucinating responses or hitting rate limits without retrying, you only find out when someone asks "why did you send that weird email?" So I built a watchdog. It's ~120 lines of Python, stores everything in SQLite, and has saved me from at least three embarrassing agent failures. Here's how to build your own. The Problem: Agents Fail Quietly Traditional software crashes loudly — exceptions, error codes, stack traces. AI agents fail politely . They return confident-sounding garbage. They skip steps without complaining. They retry infinitely or not at all. You need three things to catch this: Structured logging of every agent action Anomaly detection on response patterns Alerts when something looks off Step 1: