<?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>AIAgent on SiBlog</title><link>https://sinimite.work/en/tags/aiagent/</link><description>Recent content in AIAgent 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>en-US</language><lastBuildDate>Thu, 16 Jul 2026 21:08:52 +0900</lastBuildDate><atom:link href="https://sinimite.work/en/tags/aiagent/rss.xml" rel="self" type="application/rss+xml"/><item><title>OpenTelemetry for AI Applications and AI Agents: A Beginner's Guide</title><link>https://sinimite.work/en/posts/opentelemetry-ai-agent-observability-guide/</link><pubDate>Thu, 16 Jul 2026 21:08:52 +0900</pubDate><guid>https://sinimite.work/en/posts/opentelemetry-ai-agent-observability-guide/</guid><description>A beginner&amp;#39;s guide to OpenTelemetry for AI application developers, covering traces, metrics, logs, profiles, context propagation, the Collector, GenAI semantic conventions, and production practices.</description></item><item><title>What Did Tencent AI Leader Shunyu Yao Discuss in This Podcast?</title><link>https://sinimite.work/en/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/en/posts/points-of-the-podcast-language-agents-from-reasoning-to-acting/</guid><description>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.</description></item><item><title>What AI Engineers Should Learn in 2026</title><link>https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/</link><pubDate>Sun, 03 May 2026 20:09:15 +0900</pubDate><guid>https://sinimite.work/en/posts/ai-engineer-skill-value-map-2026/</guid><description>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.</description></item><item><title>How to Design an Excellent AI Agent: From Architectural Principles to Practical Patterns</title><link>https://sinimite.work/en/posts/ai-agent-architecture-design-guide/</link><pubDate>Sat, 02 May 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/en/posts/ai-agent-architecture-design-guide/</guid><description>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.</description></item><item><title>A Core Skill in the Age of AI Coding Agents: Writing Effective Software Specs</title><link>https://sinimite.work/en/posts/ai-coding-agent-software-spec-best-practices/</link><pubDate>Thu, 09 Apr 2026 20:03:59 +0900</pubDate><guid>https://sinimite.work/en/posts/ai-coding-agent-software-spec-best-practices/</guid><description>A guide to writing software specifications in the age of AI coding agents, covering the definition of a Software Spec, its six core areas, a four-stage workflow, best practices, and a reusable template.</description></item><item><title>The Full Landscape of LLM Training: What Every AI Application Engineer Should Understand</title><link>https://sinimite.work/en/posts/llm-training-for-ai-engineers/</link><pubDate>Sat, 04 Apr 2026 12:00:00 +0900</pubDate><guid>https://sinimite.work/en/posts/llm-training-for-ai-engineers/</guid><description>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.</description></item><item><title>Agent-Native Documentation Engineering: Designing Documentation for AI Coding Agent-Driven Development</title><link>https://sinimite.work/en/posts/agent-native-documentation-engineering/</link><pubDate>Sun, 29 Mar 2026 16:55:53 +0900</pubDate><guid>https://sinimite.work/en/posts/agent-native-documentation-engineering/</guid><description>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.</description></item><item><title>How to Write Documentation for an AI Coding Agent</title><link>https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/en/posts/ai-coding-agent-documentation-best-practices/</guid><description>Drawing on OpenAI&amp;#39;s Harness Engineering, Anthropic&amp;#39;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.</description></item><item><title>The Agentic Evolution of LLMs: From Answering Questions to Working Autonomously</title><link>https://sinimite.work/en/posts/llm-agentic-evolution/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/en/posts/llm-agentic-evolution/</guid><description>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.</description></item><item><title>The Full Landscape of AI Agent Industry Standards: A Java Engineer's Perspective</title><link>https://sinimite.work/en/posts/ai-agent-standards-full-landscape/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://sinimite.work/en/posts/ai-agent-standards-full-landscape/</guid><description>At the beginning of 2026, standardization in the AI Agent field is advancing at astonishing speed. Four core standards—MCP, A2A, AGENTS.md, and SKILL.md—have each found their place, and AAIF has begun operating as a governance organization. Using analogies to the Java ecosystem, this article helps backend engineers develop a panoramic view of the standards landscape.</description></item><item><title>Spec-Driven Development Explained: From Prompt and Pray to Spec and Steer</title><link>https://sinimite.work/en/posts/spec-driven-development-guide/</link><pubDate>Thu, 19 Mar 2026 00:01:55 +0900</pubDate><guid>https://sinimite.work/en/posts/spec-driven-development-guide/</guid><description>A systematic guide to the definition, workflow, tooling ecosystem, and appropriate boundaries of Spec-Driven Development, explaining why it is replacing Prompt-and-Pray AI development.</description></item><item><title>Agent = Model + Harness</title><link>https://sinimite.work/en/posts/agent-model-harness/</link><pubDate>Wed, 18 Mar 2026 00:17:00 +0900</pubDate><guid>https://sinimite.work/en/posts/agent-model-harness/</guid><description>Starting from the formula Agent = Model + Harness, this article reframes the core work of an AI application engineer as harness engineering.</description></item><item><title>The Complete Best-Practices Guide to Prompt Engineering</title><link>https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/</link><pubDate>Wed, 11 Mar 2026 11:00:00 +0900</pubDate><guid>https://sinimite.work/en/posts/prompt-engineering-best-practices-guide/</guid><description>A systematic guide to production-grade Prompt Engineering and Context Engineering practices for 2025–2026, covering Claude, GPT, long context, Prompt Chaining, Tool Use, and evaluation workflows.</description></item></channel></rss>