<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>LLMEvaluation on TechLLM</title><link>https://techllm.github.io/tags/llmevaluation/</link><description>Recent content in LLMEvaluation on TechLLM</description><generator>Hugo</generator><language>ko</language><lastBuildDate>Fri, 01 May 2026 21:25:00 +0900</lastBuildDate><atom:link href="https://techllm.github.io/tags/llmevaluation/index.xml" rel="self" type="application/rss+xml"/><item><title>프로덕션 AI 에이전트 모니터링: LangChain이 말하는 관측성과 평가의 핵심</title><link>https://techllm.github.io/posts/langchain-production-agent-monitoring-observability/</link><pubDate>Fri, 01 May 2026 21:25:00 +0900</pubDate><guid>https://techllm.github.io/posts/langchain-production-agent-monitoring-observability/</guid><description>LangChain의 production monitoring 글을 바탕으로, LLM 에이전트가 기존 소프트웨어와 왜 다르게 모니터링되어야 하는지, annotation queue, online evaluation, Insights Agent, dashboard/alert 설계를 정리한다.</description></item></channel></rss>