<?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>HarnessEngineering on SiBlog</title><link>https://sinimite.work/tags/harnessengineering/</link><description>Recent content in HarnessEngineering on SiBlog</description><image><title>SiBlog</title><url>https://sinimite.work/images/og-default.svg?v=20260525-210321</url><link>https://sinimite.work/images/og-default.svg?v=20260525-210321</link></image><generator>Hugo -- 0.156.0</generator><language>zh-cn</language><lastBuildDate>Mon, 27 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://sinimite.work/tags/harnessengineering/rss.xml" rel="self" type="application/rss+xml"/><item><title>从 Single-Agent 到 Multi-Agent：健壮 Sub-Agent 系统设计指南</title><link>https://sinimite.work/posts/robust-multi-agent-subagent-design-2026/</link><pubDate>Mon, 27 Jul 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/posts/robust-multi-agent-subagent-design-2026/</guid><description>面向普通程序员的 Multi-Agent 与 Sub-Agent 架构、指令设计、协调机制、可靠性、安全、评测与生产落地指南</description></item><item><title>生产级 AI Agent 的上下文压缩：从消息裁剪、工具结果清理到 Hermes Agent 的分层 Compaction</title><link>https://sinimite.work/posts/ai-agent-context-compression-best-practices-2026/</link><pubDate>Sun, 26 Jul 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/posts/ai-agent-context-compression-best-practices-2026/</guid><description>一份面向生产级 AI Agent 的上下文压缩指南，覆盖上下文退化、工具结果管理、Artifact、任务检查点、供应商原生 Compaction、Hermes Agent 实现与工程评估。</description></item><item><title>腾讯 AI Leader 姚顺雨 在这个PodCast中聊了什么？</title><link>https://sinimite.work/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/</link><pubDate>Sun, 24 May 2026 16:05:10 +0900</pubDate><guid>https://sinimite.work/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/</guid><description>一篇关于 Language Agents: From Reasoning to Acting podcast 的观后感，从 ReAct、Reflexion、Tree of Thoughts、memory、benchmark 和 ACI 出发，解释 LLM agent 为什么是模型、工具、记忆、环境、评估和 UX 共同构成的工程系统。</description></item><item><title>2026 年 AI Engineer 学什么值钱</title><link>https://sinimite.work/posts/ai-engineer-skill-value-map-2026/</link><pubDate>Sun, 03 May 2026 20:09:15 +0900</pubDate><guid>https://sinimite.work/posts/ai-engineer-skill-value-map-2026/</guid><description>一篇面向 AI 应用工程师转型的技能价值地图，说明基础 RAG、prompt engineering 与框架 API 的价值变化，并解释 eval、governance 和 agentic workflow 为什么更值得投入。</description></item><item><title>如何设计出优秀的 AI Agent：从架构原则到实战模式</title><link>https://sinimite.work/posts/ai-agent-architecture-design-guide/</link><pubDate>Sat, 02 May 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/posts/ai-agent-architecture-design-guide/</guid><description>一篇面向 AI coding agent 实战的完整指南，系统解释如何通过 spec、fat skills、thin harness、deterministic tooling 与学习闭环设计出可靠且持续进化的 AI agent 系统。</description></item><item><title>大模型训练全景：一个 AI 应用工程师需要理解的一切</title><link>https://sinimite.work/posts/llm-training-for-ai-engineers/</link><pubDate>Sat, 04 Apr 2026 12:00:00 +0900</pubDate><guid>https://sinimite.work/posts/llm-training-for-ai-engineers/</guid><description>一篇面向 AI 应用工程师的大模型训练全景指南，覆盖预训练、后训练、蒸馏、Reward 设计、Agent 训练与 Harness Engineering，帮助你理解模型能力从何而来，以及这些训练决策如何影响真实应用落地。</description></item><item><title>Agent-Native 文档工程：AI Coding Agent 驱动开发的文档体系设计指南</title><link>https://sinimite.work/posts/agent-native-documentation-engineering/</link><pubDate>Sun, 29 Mar 2026 16:55:53 +0900</pubDate><guid>https://sinimite.work/posts/agent-native-documentation-engineering/</guid><description>一份面向 AI coding agent 项目的文档体系设计指南，覆盖 AGENTS.md、PRD、Architecture、Spec、Plan 的分层职责，以及 agent-native 文档的组织原则与工程价值。</description></item><item><title>LLM 的 Agentic 化：从回答问题到自主工作</title><link>https://sinimite.work/posts/llm-agentic-evolution/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/posts/llm-agentic-evolution/</guid><description>LLM 正在从&amp;#39;被动回答问题的工具&amp;#39;演化为&amp;#39;自主完成目标的 agent&amp;#39;。这不是模型能力的线性提升，而是使用范式的根本转变。本文从一个转型 AI 工程师的视角，梳理这个转变的本质、技术栈的演化、以及它对工程师意味着什么。</description></item><item><title>如何为 AI Coding Agent 写文档</title><link>https://sinimite.work/posts/ai-coding-agent-documentation-best-practices/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/posts/ai-coding-agent-documentation-best-practices/</guid><description>综合 OpenAI Harness Engineering、Anthropic 长时任务 Agent 研究、AGENTS.md 标准与多个生产团队经验，梳理为 AI coding agent 设计项目文档的最佳实践。</description></item><item><title>Agent = Model + Harness</title><link>https://sinimite.work/posts/agent-model-harness/</link><pubDate>Wed, 18 Mar 2026 00:17:00 +0900</pubDate><guid>https://sinimite.work/posts/agent-model-harness/</guid><description>从 Agent = Model + Harness 这个公式出发，重新理解 AI 应用工程师的核心工作其实是 harness engineering。</description></item></channel></rss>