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      <title>QCon London 2026: Wrangling Telemetry at Scale, a Guide to Self-Hosted Observability</title>
      <link>https://www.infoq.com/news/2026/03/self-hosted-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Metrics</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/03/self-hosted-observability/en/headerimage/generatedHeaderImage-1773862581289.jpg"/&gt;&lt;p&gt;At QCon London 2026, Colin Douch discussed building and operating self-hosted monitoring stacks, surveyed the current tooling landscape, and explained how to build a coherent observability setup rather than treating logs, metrics, and traces as separate pillars.&lt;/p&gt; &lt;i&gt;By Renato Losio&lt;/i&gt;</description>
      <category>OpenTelemetry</category>
      <category>Site Reliability Engineering</category>
      <category>Telemetry</category>
      <category>Distributed Tracing</category>
      <category>Metrics</category>
      <category>Logging</category>
      <category>Prometheus</category>
      <category>Observability</category>
      <category>Development</category>
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      <pubDate>Thu, 19 Mar 2026 09:17:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/03/self-hosted-observability/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Metrics</guid>
      <dc:creator>Renato Losio</dc:creator>
      <dc:date>2026-03-19T09:17:00Z</dc:date>
      <dc:identifier>/news/2026/03/self-hosted-observability/en</dc:identifier>
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    <item>
      <title>Article: Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned</title>
      <link>https://www.infoq.com/articles/evaluating-ai-agents-lessons-learned/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Metrics</link>
      <description>&lt;img src="https://res.infoq.com/articles/evaluating-ai-agents-lessons-learned/en/headerimage/evaluating-ai-agents-lessons-learned-header-1773307288872.jpg"/&gt;&lt;p&gt;This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.&lt;/p&gt; &lt;i&gt;By Amit Kumar Padhy&lt;/i&gt;</description>
      <category>Agents</category>
      <category>AI Architecture</category>
      <category>Metrics</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Mon, 16 Mar 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/evaluating-ai-agents-lessons-learned/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Metrics</guid>
      <dc:creator>Amit Kumar Padhy</dc:creator>
      <dc:date>2026-03-16T11:00:00Z</dc:date>
      <dc:identifier>/articles/evaluating-ai-agents-lessons-learned/en</dc:identifier>
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