AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production
中文摘要
AI智能体破坏了传统MLOps监控假设。本文分析了导致监控失效的五个关键点，及为何失败任务会被误判为正常。
English Summary
AI agents break traditional MLOps monitoring assumptions. This article analyzes five ways they cause monitoring failures, misidentifying failed runs as healthy in production.
原文节选
The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy. The post AgentOps Is Not MLOps: What Breaks in Your Monitoring Stack When Agents Go to Production appeared first on Towards Data Science.