<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Harness-Engineering on Armstrong Yan</title><link>https://yanqian.github.io/tags/harness-engineering/</link><description>Recent content in Harness-Engineering on Armstrong Yan</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 04 Jun 2026 10:46:50 +0800</lastBuildDate><atom:link href="https://yanqian.github.io/tags/harness-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>I Built a Small Harness to Stop AI Coding Projects From Forgetting State</title><link>https://yanqian.github.io/posts/publish/i-built-a-small-harness-to-stop-ai-coding-projects-from-forgetting-state/</link><pubDate>Thu, 04 Jun 2026 10:46:50 +0800</pubDate><guid>https://yanqian.github.io/posts/publish/i-built-a-small-harness-to-stop-ai-coding-projects-from-forgetting-state/</guid><description>&lt;p>AI coding agents are powerful.&lt;/p>
&lt;p>But long-running AI coding projects break in a very specific way:&lt;/p>
&lt;ul>
&lt;li>the session is interrupted&lt;/li>
&lt;li>the context becomes too long&lt;/li>
&lt;li>the weekly quota runs out&lt;/li>
&lt;li>tomorrow&amp;rsquo;s agent forgets yesterday&amp;rsquo;s decisions&lt;/li>
&lt;li>the agent changes unrelated files&lt;/li>
&lt;li>the agent marks work done too early&lt;/li>
&lt;/ul>
&lt;p>The problem is not that AI cannot write code.&lt;/p>
&lt;p>The problem is that AI coding projects often do not have durable project state.&lt;/p></description></item><item><title>AI-Native Software Engineering, Part 2: Harness Engineering and Correctness</title><link>https://yanqian.github.io/posts/publish/harness-engineering-is-about-limiting-ai-not-empowering-it/</link><pubDate>Sat, 23 May 2026 20:33:47 +0800</pubDate><guid>https://yanqian.github.io/posts/publish/harness-engineering-is-about-limiting-ai-not-empowering-it/</guid><description>&lt;p>Why the most important part of AI-native software engineering may not be generation, but constraint.&lt;/p>
&lt;p>This is Part 2 of the AI-Native Software Engineering series.&lt;/p>
&lt;p>It continues from &lt;a href="https://yanqian.github.io/posts/publish/agentic-coding-mental-models-and-the-new-depth-of-software-engineering/" >AI-Native Software Engineering, Part 1: Mental Models in Agentic Coding&lt;/a>.&lt;/p>
&lt;p>The previous question was:&lt;/p>
&lt;div class="highlight">&lt;pre tabindex="0" class="chroma">&lt;code class="language-text" data-lang="text">&lt;span class="line">&lt;span class="cl">If understanding no longer comes mainly from writing code,
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">how do humans build mental models in an agentic workflow?
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/div>&lt;p>The next question is:&lt;/p>
&lt;div class="highlight">&lt;pre tabindex="0" class="chroma">&lt;code class="language-text" data-lang="text">&lt;span class="line">&lt;span class="cl">If implementation is delegated,
&lt;/span>&lt;/span>&lt;span class="line">&lt;span class="cl">where does correctness come from?
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/div>&lt;p>The conversation around AI-assisted software development often focuses on one thing:&lt;/p></description></item></channel></rss>