<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Task Decomposition on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/task-decomposition/</link><description>Recent content in Task Decomposition 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/task-decomposition/index.xml" rel="self" type="application/rss+xml"/><item><title>How Agents Think: Designing Planning and Task Decomposition</title><link>https://ai-blog.noorshomelab.dev/agentic-ai-guide-2026/agent-planning-strategies/</link><pubDate>Fri, 20 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/agentic-ai-guide-2026/agent-planning-strategies/</guid><description>&lt;h2 id="introduction-to-agentic-planning"&gt;Introduction to Agentic Planning&lt;/h2&gt;
&lt;p&gt;Welcome back, aspiring agent architects! In our previous chapters, we laid the groundwork for understanding what autonomous AI agents are and how Large Language Models (LLMs) serve as their powerful &amp;ldquo;brains.&amp;rdquo; But having a brain isn&amp;rsquo;t enough; an agent also needs a clear roadmap to achieve its goals. That&amp;rsquo;s where planning comes in.&lt;/p&gt;
&lt;p&gt;Imagine you&amp;rsquo;re building a complex structure – you wouldn&amp;rsquo;t just start laying bricks randomly, right? You&amp;rsquo;d need blueprints, a sequence of steps, and a way to break down the massive project into manageable phases. Agentic AI is no different. This chapter is all about teaching your agents &lt;em&gt;how to think strategically&lt;/em&gt;, transforming a high-level objective into a series of concrete, executable actions. We&amp;rsquo;ll explore core planning strategies like task decomposition and the famous ReAct pattern, giving your agents the ability to reason about their next steps.&lt;/p&gt;</description></item></channel></rss>