<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Agentic Workflows on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/agentic-workflows/</link><description>Recent content in Agentic Workflows on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 17 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/agentic-workflows/index.xml" rel="self" type="application/rss+xml"/><item><title>Context Control and Large Codebases: Managing Agent Memory</title><link>https://ai-blog.noorshomelab.dev/aipack-guide-2026/context-control-large-codebases/</link><pubDate>Sun, 17 May 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/aipack-guide-2026/context-control-large-codebases/</guid><description>&lt;h2 id="introduction-the-agents-memory-challenge"&gt;Introduction: The Agent&amp;rsquo;s Memory Challenge&lt;/h2&gt;
&lt;p&gt;Imagine trying to have a productive conversation with someone who constantly forgets what you just said or only remembers a tiny fragment of your shared history. Frustrating, right? This is the core challenge AI agents face: managing their &amp;ldquo;memory&amp;rdquo; or, more technically, their &lt;em&gt;context&lt;/em&gt;. For an AI agent to perform complex tasks, especially within a sprawling project like a large codebase, it needs to access and process relevant information efficiently without getting overwhelmed.&lt;/p&gt;</description></item></channel></rss>