
  <rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
    <channel>
      <title>Duncan Leung</title>
      <link>https://www.duncanleung.com/blog</link>
      <description>📚 Learning, 👨🏻‍💻 Building, 📝 Documenting...</description>
      <language>en-us</language>
      <managingEditor>duncan@leungs.email (Duncan Leung)</managingEditor>
      <webMaster>duncan@leungs.email (Duncan Leung)</webMaster>
      <lastBuildDate>Mon, 19 Oct 2026 00:00:00 GMT</lastBuildDate>
      <atom:link href="https://www.duncanleung.com/tags/multi-agent/feed.xml" rel="self" type="application/rss+xml"/>
      
  <item>
    <guid>https://www.duncanleung.com/blog/openrig-compaction-system-why-compact-instead-of-handoff</guid>
    <title>Why We Built a Compaction System Instead of Handing Off to New Agents</title>
    <link>https://www.duncanleung.com/blog/openrig-compaction-system-why-compact-instead-of-handoff</link>
    <description>When a long-running orchestrator hits context limits, the obvious move is to hand off to a fresh agent. We tried that. The startup overhead, lost momentum, and broken continuity made it worse than the problem it solved — for our use case. So we built a compaction system instead: one that writes ground truth to disk, restores from it, and distills session knowledge into a wiki page via an API call to Haiku. Recent research shows neither approach is categorically better — compaction drops behavioral constraints, handoff carries a &#39;handoff tax&#39; — but for same-model orchestrators that accumulate judgment over time, compaction with external ground truth wins. Here&#39;s how the system works and what the literature says.</description>
    <pubDate>Mon, 19 Oct 2026 00:00:00 GMT</pubDate>
    <author>duncan@leungs.email (Duncan Leung)</author>
    <category>ai</category><category>claude-code</category><category>openrig</category><category>multi-agent</category>
  </item>

    </channel>
  </rss>
