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      <title>Article: Runtime-Agnostic AI Workflows: a Pattern for Production Durability and Fast Eval Iteration</title>
      <link>https://www.infoq.com/articles/ai-workflow-pattern/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Large+language+models-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/ai-workflow-pattern/en/headerimage/header-1785766770921.jpg"/&gt;&lt;p&gt;AI workflows have two needs that trade off directly. Running reliably in production requires persisting and distributing every step so it survives crashes, deploys, and restarts. But that same machinery is what makes runs too heavy for the fast, throwaway loop you need to check an LLM's output quality. The properties that buy durability are the ones that kill iteration speed.&lt;/p&gt; &lt;i&gt;By Mateus Moury&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
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      <pubDate>Thu, 06 Aug 2026 09:00:00 GMT</pubDate>
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      <dc:creator>Mateus Moury</dc:creator>
      <dc:date>2026-08-06T09:00:00Z</dc:date>
      <dc:identifier>/articles/ai-workflow-pattern/en</dc:identifier>
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