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    <title>Planning on Hullabalooing</title>
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    <description>Recent content in Planning on Hullabalooing</description>
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    <lastBuildDate>Thu, 22 Jan 2026 13:30:56 +0000</lastBuildDate>
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      <title>Task-Decoupled Agents: Breaking the Monolithic Context Problem</title>
      <link>https://cruver.ai/writing/posts/task-decoupled-agents-1769113856/</link>
      <pubDate>Thu, 22 Jan 2026 13:30:56 +0000</pubDate>
      <guid>https://cruver.ai/writing/posts/task-decoupled-agents-1769113856/</guid>
      <description>&lt;p&gt;A recent paper from Tsinghua and Renmin University caught my attention: &lt;a href=&#34;https://arxiv.org/abs/2601.07577&#34;&gt;Task-Decoupled Planning for Long-Horizon Agents&lt;/a&gt;. The core insight resonated with a problem I&amp;rsquo;ve been wrestling with on a Java code modernization project at work, and I think the pattern deserves more attention.&lt;/p&gt;&#xA;&lt;h2 id=&#34;the-entangled-context-problem&#34;&gt;The Entangled Context Problem&lt;a class=&#34;anchor&#34; href=&#34;#the-entangled-context-problem&#34;&gt;#&lt;/a&gt;&lt;/h2&gt;&#xA;&lt;p&gt;Most LLM agent architectures fall into two camps: step-wise planning (reactive, one step at a time) or one-shot planning (generate the whole plan upfront). Both share a fatal flaw: they maintain a monolithic reasoning context that spans the entire task history.&lt;/p&gt;</description>
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