<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Compression on Unbound Force</title><link>https://unboundforce.dev/tags/compression/</link><description>Recent content in Compression on Unbound Force</description><generator>Hugo</generator><language>en-US</language><copyright>Copyright (c) 2025-2026 Unbound Force</copyright><lastBuildDate>Sun, 23 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://unboundforce.dev/tags/compression/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompt Hardening for Compression Resilience — Engineering Agent Prompts That Survive DCP</title><link>https://unboundforce.dev/blog/prompt-hardening/</link><pubDate>Sun, 23 Aug 2026 00:00:00 +0000</pubDate><guid>https://unboundforce.dev/blog/prompt-hardening/</guid><description>&lt;h2 id="the-silent-failure-mode-nobody-talks-about"&gt;The Silent Failure Mode Nobody Talks About&lt;/h2&gt;
&lt;p&gt;AI agent systems run on prompts — long, detailed instructions that define behavior, enforce constraints, and gate quality. These prompts work flawlessly in short sessions. But as context windows fill up during complex tasks, something happens that most teams never notice: the LLM&amp;rsquo;s context compressor kicks in and &lt;em&gt;summarizes your instructions&lt;/em&gt;.&lt;/p&gt;</description></item></channel></rss>