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      <title>Article: Local-First AI Inference: A Cloud Architecture Pattern for Cost-Effective Document Processing</title>
      <link>https://www.infoq.com/articles/local-first-ai-inference-cloud/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/local-first-ai-inference-cloud/en/headerimage/Local-First-AI-Inference-A-Cloud-Architecture-Pattern-for-Cost-Effective-Document-Processing-header-1778141518292.jpg"/&gt;&lt;p&gt;The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.&lt;/p&gt; &lt;i&gt;By Obinna Iheanachor&lt;/i&gt;</description>
      <category>Cost Optimization</category>
      <category>Artificial Intelligence</category>
      <category>Microsoft Azure</category>
      <category>Observability</category>
      <category>Model Inference</category>
      <category>Azure</category>
      <category>GPT-4</category>
      <category>Cloud</category>
      <category>Generative AI</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Mon, 11 May 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/local-first-ai-inference-cloud/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</guid>
      <dc:creator>Obinna Iheanachor</dc:creator>
      <dc:date>2026-05-11T11:00:00Z</dc:date>
      <dc:identifier>/articles/local-first-ai-inference-cloud/en</dc:identifier>
    </item>
    <item>
      <title>Article: Three Pillars of Platform Engineering: a Virtuous Cycle</title>
      <link>https://www.infoq.com/articles/platform-reliability-cycle/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/platform-reliability-cycle/en/headerimage/platform-reliability-cycle-header-1777467114542.jpg"/&gt;&lt;p&gt;Platform engineering succeeds when reliability and ergonomics reinforce each other rather than compete. This article explores three foundational pillars: automated reliability, developer ergonomics, and operator ergonomics. Together, they establish a virtuous cycle that strengthens system stability, reduces operational burden, and empowers teams to scale infrastructure with confidence.&lt;/p&gt; &lt;i&gt;By Pratik Agarwal&lt;/i&gt;</description>
      <category>Site Reliability Engineering</category>
      <category>Platform Engineering</category>
      <category>Observability</category>
      <category>Distributed Systems</category>
      <category>Developer Experience</category>
      <category>Architecture &amp; Design</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Tue, 05 May 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/platform-reliability-cycle/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</guid>
      <dc:creator>Pratik Agarwal</dc:creator>
      <dc:date>2026-05-05T09:00:00Z</dc:date>
      <dc:identifier>/articles/platform-reliability-cycle/en</dc:identifier>
    </item>
    <item>
      <title>Article: Securing Autonomous AI Agents on Kubernetes: Trust Boundaries, Secrets, and Observability for a New Category of Cloud Workload</title>
      <link>https://www.infoq.com/articles/securing-autonomous-ai-agents-kubernetes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/securing-autonomous-ai-agents-kubernetes/en/headerimage/securing-autonomous-ai-agents-kubernetes-header-1777378848477.jpg"/&gt;&lt;p&gt;Autonomous AI agents break Kubernetes security assumptions with dynamic dependencies, multi-domain credentials, and unpredictable resource use. This article covers production-tested patterns: Job-based isolation, Vault for scoped short-lived credentials, a four-phase trust model from shadow mode to autonomous operation, and observability for non-deterministic reasoning cycles.&lt;/p&gt; &lt;i&gt;By Nik Kale&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Kubernetes</category>
      <category>Security</category>
      <category>Observability</category>
      <category>Cloud</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>article</category>
      <pubDate>Fri, 01 May 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/securing-autonomous-ai-agents-kubernetes/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Observability-articles</guid>
      <dc:creator>Nik Kale</dc:creator>
      <dc:date>2026-05-01T09:00:00Z</dc:date>
      <dc:identifier>/articles/securing-autonomous-ai-agents-kubernetes/en</dc:identifier>
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