混沌工程:在 AI 時代重建系統韌性
儀表板綠燈不代表系統安全。當 LLM 延遲飆升、向量資料庫狀態不一致時,傳統監控往往失效。本文探討如何從「預防」轉向「韌性驗證」,並提供在 AI 導入期建立故障注入框架的實戰指南。
儀表板綠燈不代表系統安全。當 LLM 延遲飆升、向量資料庫狀態不一致時,傳統監控往往失效。本文探討如何從「預防」轉向「韌性驗證」,並提供在 AI 導入期建立故障注入框架的實戰指南。
A green dashboard doesn’t mean the system is safe. When LLM latency spikes or vector database states become inconsistent, traditional monitoring often fails. This article explores how to shift from “prevention” to “resilience validation,” and provides a practical guide to building a fault injection framework during AI adoption.