<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>State Machines on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/state-machines/</link><description>Recent content in State Machines on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 20 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/state-machines/index.xml" rel="self" type="application/rss+xml"/><item><title>LangGraph: Building State Machines for Dynamic Agent Workflows</title><link>https://ai-blog.noorshomelab.dev/ai-agent-frameworks-2026/langgraph-state-machines/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/ai-agent-frameworks-2026/langgraph-state-machines/</guid><description>&lt;h2 id="introduction-orchestrating-agents-with-state"&gt;Introduction: Orchestrating Agents with State&lt;/h2&gt;
&lt;p&gt;Welcome back, aspiring AI architects! In our previous chapters, we explored the foundational concepts of AI agents, their components, and the challenges of building multi-step reasoning. We understood that truly intelligent agents often need to perform a sequence of actions, make decisions based on intermediate results, and even loop back to previous steps if needed. This is where the magic of orchestration frameworks comes into play.&lt;/p&gt;</description></item></channel></rss>