https://sinimite.work/en/tags/harnessengineering/Recent content in HarnessEngineering on SiBloghttps://sinimite.work/images/og-default.svg?v=20260525-210321https://sinimite.work/images/og-default.svg?v=20260525-210321Hugo -- 0.156.0en-USSun, 24 May 2026 16:05:10 +0900https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/Sun, 24 May 2026 16:05:10 +0900https://sinimite.work/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/Reflections on the podcast Language Agents: From Reasoning to Acting. Starting from ReAct, Reflexion, Tree of Thoughts, memory, benchmarks, and ACI, the article explains why an LLM Agent is an engineered system composed of a model, tools, memory, environment, evaluation, and UX.https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/Sun, 03 May 2026 20:09:15 +0900https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/A skill value map for engineers moving into AI application engineering, explaining how the value of basic RAG, prompt engineering, and framework APIs is changing, and why evaluation, governance, and agentic workflows deserve greater investment.https://sinimite.work/en/posts/ai-agent-architecture-design-guide/Sat, 02 May 2026 00:00:00 +0000https://sinimite.work/en/posts/ai-agent-architecture-design-guide/A practical guide to AI coding agents explaining how specs, fat skills, a thin harness, deterministic tooling, and a learning loop create reliable, continuously improving AI agent systems.https://sinimite.work/en/posts/llm-training-for-ai-engineers/Sat, 04 Apr 2026 12:00:00 +0900https://sinimite.work/en/posts/llm-training-for-ai-engineers/A panoramic guide to LLM training for AI application engineers, covering pretraining, post-training, distillation, reward design, agent training, and harness engineering, so you can understand where model capabilities come from and how training decisions affect real-world deployment.https://sinimite.work/en/posts/agent-native-documentation-engineering/Sun, 29 Mar 2026 16:55:53 +0900https://sinimite.work/en/posts/agent-native-documentation-engineering/A guide to documentation architecture for AI coding agent projects, covering the layered responsibilities of AGENTS.md, PRD, Architecture, Spec, and Plan documents, plus the organizational principles and engineering value of agent-native documentation.https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/Sat, 28 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/Drawing on OpenAI's Harness Engineering, Anthropic's research on long-running agents, the AGENTS.md standard, and experience from multiple production teams, this article presents best practices for designing project documentation for AI coding agents.https://sinimite.work/en/posts/llm-agentic-evolution/Sat, 28 Mar 2026 00:00:00 +0000https://sinimite.work/en/posts/llm-agentic-evolution/LLMs are evolving from passive question-answering tools into agents that pursue goals autonomously. This is not a linear increase in model capability but a fundamental change in the usage paradigm. This article examines the nature of that transition, the evolution of the engineering stack, and what it means for engineers.https://sinimite.work/en/posts/agent-model-harness/Wed, 18 Mar 2026 00:17:00 +0900https://sinimite.work/en/posts/agent-model-harness/Starting from the formula Agent = Model + Harness, this article reframes the core work of an AI application engineer as harness engineering.