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    <title>InfoQ - AI Development</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ AI Development feed</description>
    <item>
      <title>Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures</title>
      <link>https://www.infoq.com/articles/agentic-ai-context-memory-knowledge/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</link>
      <description>&lt;img src="https://res.infoq.com/articles/agentic-ai-context-memory-knowledge/en/headerimage/agentic-ai-context-memory-knowledge-header-1793173492489.jpg"/&gt;&lt;p&gt;The article argues that reliable agentic AI architecture depends on a context engine that selects, injects, compresses, and expires information before it reaches the model. It separates context, memory, and knowledge, and recommends tiered memory, curated knowledge, and token budgeting. It also covers common anti-patterns, such as context stuffing and stale data loops, with mitigations.&lt;/p&gt; &lt;i&gt;By Adi Polak&lt;/i&gt;</description>
      <category>Large language models</category>
      <category>Context Engineering</category>
      <category>Agentic AI Architecture</category>
      <category>Memory</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>article</category>
      <pubDate>Mon, 02 Nov 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/agentic-ai-context-memory-knowledge/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</guid>
      <dc:creator>Adi Polak</dc:creator>
      <dc:date>2026-11-02T11:00:00Z</dc:date>
      <dc:identifier>/articles/agentic-ai-context-memory-knowledge/en</dc:identifier>
    </item>
    <item>
      <title>Article: InfoQ AI, ML and Data Engineering Trends Report - 2026</title>
      <link>https://www.infoq.com/articles/ai-ml-data-engineering-trends-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</link>
      <description>&lt;img src="https://res.infoq.com/articles/ai-ml-data-engineering-trends-2026/en/headerimage/ai-ml-de-graph-2026-1793191771570.jpg"/&gt;&lt;p&gt;This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months.&lt;/p&gt; &lt;i&gt;By Srini Penchikala, Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals&lt;/i&gt;</description>
      <category>Robotics</category>
      <category>Model Context Protocol (MCP)</category>
      <category>InfoQ Trends Report 2026</category>
      <category>AI coding agents</category>
      <category>Large language models</category>
      <category>InfoQ Trends Report</category>
      <category>Artificial Intelligence</category>
      <category>Agent2Agent</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Fri, 30 Oct 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/ai-ml-data-engineering-trends-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</guid>
      <dc:creator>Srini Penchikala, Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals</dc:creator>
      <dc:date>2026-10-30T09:00:00Z</dc:date>
      <dc:identifier>/articles/ai-ml-data-engineering-trends-2026/en</dc:identifier>
    </item>
    <item>
      <title>Podcast: AI, ML, and Data Engineering InfoQ Trends Report 2026</title>
      <link>https://www.infoq.com/podcasts/ai-ml-data-engineering-trends-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</link>
      <description>&lt;img src="https://res.infoq.com/podcasts/ai-ml-data-engineering-trends-2026/en/smallimage/trend-reports-thumbnail-1793181579584.jpg"/&gt;&lt;p&gt;In this episode, members of the InfoQ AI and ML editorial staff and friends of InfoQ discuss the current trends in the domain of AI, ML and Data Engineering. One of the regular features of InfoQ are the trends reports, which each focus on a different aspect of software dev. These reports provide the InfoQ readers and listeners with a high-level overview of the topics to pay attention to this year.&lt;/p&gt; &lt;i&gt;By Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals&lt;/i&gt;</description>
      <category>Model Context Protocol (MCP)</category>
      <category>InfoQ Trends Report 2026</category>
      <category>InfoQ Trends Report</category>
      <category>Context Engineering</category>
      <category>Multi-Model Databases</category>
      <category>Robotics</category>
      <category>Spec Driven Development</category>
      <category>Large language models</category>
      <category>Frontier Model</category>
      <category>The InfoQ Podcast</category>
      <category>Artificial Intelligence</category>
      <category>Agent2Agent</category>
      <category>AI Harness</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>podcast</category>
      <pubDate>Fri, 30 Oct 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/podcasts/ai-ml-data-engineering-trends-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</guid>
      <dc:creator>Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals</dc:creator>
      <dc:date>2026-10-30T09:00:00Z</dc:date>
      <dc:identifier>/podcasts/ai-ml-data-engineering-trends-2026/en</dc:identifier>
    </item>
    <item>
      <title>How Anthropic Redesigned Source Control for AI-Driven Development</title>
      <link>https://www.infoq.com/news/2026/10/anthropic-source-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/10/anthropic-source-control/en/headerimage/scion-architecture-header-1792669549048.jpeg"/&gt;&lt;p&gt;Anthropic recently detailed how it migrated its monorepo from Git to Scion. Scion is a source control system that moves repository operations to the server side and uses content-addressed storage and tiered caches for agent and CI workloads. The design sacrifices offline independence for less local data and shared infrastructure.&lt;/p&gt; &lt;i&gt;By Eran Stiller&lt;/i&gt;</description>
      <category>Version Control</category>
      <category>AI Development</category>
      <category>Git</category>
      <category>Anthropic</category>
      <category>Source Control</category>
      <category>Distributed Systems</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>news</category>
      <pubDate>Tue, 27 Oct 2026 11:25:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/10/anthropic-source-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</guid>
      <dc:creator>Eran Stiller</dc:creator>
      <dc:date>2026-10-27T11:25:00Z</dc:date>
      <dc:identifier>/news/2026/10/anthropic-source-control/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Move Fast, Don’t Break Trust: Shipping Constantly with Humans and beyond</title>
      <link>https://www.infoq.com/presentations/monzo-microservices-devex/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</link>
      <description>&lt;img src="https://res.infoq.com/presentations/monzo-microservices-devex/en/mediumimage/suhail-patel-medium-1791448951499.jpg"/&gt;&lt;p&gt;Suhail Patel discusses how Monzo structures its software engineering organization and technical stack to ship safely hundreds of times a day. He shares insights into opinionated microservice abstractions, tenancy-based staging, static analysis checks, continuous profiling, demonstrating how a strong internal platform enables engineers and LLMs to iterate rapidly without sacrificing reliability.&lt;/p&gt; &lt;i&gt;By Suhail Patel&lt;/i&gt;</description>
      <category>Developer Experience</category>
      <category>AI Development</category>
      <category>Code Quality</category>
      <category>Microservices</category>
      <category>QCon London 2026</category>
      <category>Observability</category>
      <category>Go Language</category>
      <category>Security</category>
      <category>Transcripts</category>
      <category>Platform Engineering</category>
      <category>Continuous Deployment</category>
      <category>DevOps</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Fri, 23 Oct 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/monzo-microservices-devex/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Development</guid>
      <dc:creator>Suhail Patel</dc:creator>
      <dc:date>2026-10-23T11:00:00Z</dc:date>
      <dc:identifier>/presentations/monzo-microservices-devex/en</dc:identifier>
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