Detecting Invisible Errors in LLM‑Powered Agents with Agnost AI
Your practical guide to monitoring, debugging, and automating remediation in production pipelines
Introduction
When your autonomous assistant starts hallucinating policies, leaking private data, or silently degrading performance, the problem rarely shows up in unit tests. Agnost AI fills that blind spot by continuously watching the runtime state of LLM‑driven agents and surfacing “invisible” errors before they cost you revenue, compliance fines, or brand trust.
In this article you’ll see why classic testing fails, explore Agnost AI’s architecture, and walk through a ready‑to‑copy integration for GitHub Actions, GitLab CI, and Docker‑based development. You’ll also get concrete Python and Bash snippets for log collection, metric shipping, and real‑time alerts to Slack or Telegram, plus a quick cost‑vs‑precision comparison with OpenAI evals, LangChain, and home‑grown heuristics.
