<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Gemini on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/gemini/</link><description>Recent content in Gemini on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 22 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/gemini/index.xml" rel="self" type="application/rss+xml"/><item><title>The Agent Execution Loop: Architecting Goal-Driven Behavior</title><link>https://ai-blog.noorshomelab.dev/loop-engineering-2026/agent-execution-loop-architecting-goal-driven-behavior/</link><pubDate>Mon, 22 Jun 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/loop-engineering-2026/agent-execution-loop-architecting-goal-driven-behavior/</guid><description>&lt;p&gt;Building production-grade AI systems increasingly means moving beyond single-turn interactions to orchestrating complex, autonomous workflows. This chapter introduces &amp;ldquo;loop engineering,&amp;rdquo; the architectural discipline of designing goal-driven AI agent execution loops.&lt;/p&gt;
&lt;p&gt;We&amp;rsquo;ll explore how to transform basic coding assistants into robust, self-correcting systems capable of tackling real-world problems by integrating tools, managing costs, and incorporating human oversight. Understanding these architectural patterns is crucial for anyone looking to build reliable and scalable AI-powered solutions in a cloud environment like Google Cloud.&lt;/p&gt;</description></item></channel></rss>