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      <title>Presentation: The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents</title>
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      <description>&lt;img src="https://res.infoq.com/presentations/ai-agent-harness/en/mediumimage/vino-medium-1789631870270.jpeg"/&gt;&lt;p&gt;OpenAI’s Vinoth Govindarajan discusses why production AI agents fail beyond model hallucination. Using real-world case studies like OpenClaw, he explains the key principles of reliable agent harnesses: establishing explicit state ownership, serializing concurrent state mutations, scoping execution authority, and validating actions at the user-visible edge.&lt;/p&gt; &lt;i&gt;By Vinoth Govindarajan&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Agents</category>
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      <category>QCon AI Boston 2026</category>
      <category>OpenAI</category>
      <category>Architecture</category>
      <category>Distributed Systems</category>
      <category>AI Harness</category>
      <category>AI, ML &amp; Data Engineering</category>
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      <category>Architecture &amp; Design</category>
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      <pubDate>Mon, 21 Sep 2026 11:00:00 GMT</pubDate>
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      <dc:creator>Vinoth Govindarajan</dc:creator>
      <dc:date>2026-09-21T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-agent-harness/en</dc:identifier>
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