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    <title>InfoQ - AI Architecture</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ AI Architecture feed</description>
    <item>
      <title>Presentation: A Few Predicted Talks From QConAI 2030</title>
      <link>https://www.infoq.com/presentations/ai-predictions-2030/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/presentations/ai-predictions-2030/en/mediumimage/meryem-arik-medium-1785845650090.jpg"/&gt;&lt;p&gt;Meryem Arik discusses her predictions for software engineering in 2030. She explains how token spend management, parallel agent infrastructure, and non-technical builders will reshape IT. She shares insights on agent-driven vendor decisions, upcoming regulatory hurdles, and why software engineers must pivot from pure coding skills toward product leadership and multi-agent coordination.&lt;/p&gt; &lt;i&gt;By Meryem Arik&lt;/i&gt;</description>
      <category>Transcripts</category>
      <category>Infrastructure</category>
      <category>Productivity</category>
      <category>AI Architecture</category>
      <category>Technology Trends</category>
      <category>QCon AI Boston 2026</category>
      <category>Regulation</category>
      <category>Agents</category>
      <category>Governance</category>
      <category>Patterns</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Sat, 05 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/ai-predictions-2030/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Meryem Arik</dc:creator>
      <dc:date>2026-09-05T11:00:00Z</dc:date>
      <dc:identifier>/presentations/ai-predictions-2030/en</dc:identifier>
    </item>
    <item>
      <title>Mini book: Next-Gen Architecture Playbook: Insights and Patterns for the AI Era</title>
      <link>https://www.infoq.com/minibooks/next-gen-architecture-ai-era/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/minibooks/next-gen-architecture-ai-era/en/smallimage/thumb-image-emag-next-gen-architecture-playbook-1787922032442.jpg"/&gt;&lt;p&gt;This eMag examines how architects can lead with clarity in a rapidly evolving engineering world, distilling industry insights into field-tested practices for teams. Together, these stories reveal a core theme: the technology leader’s role is expanding from building systems to guiding how tech behaves and learns, while enabling engineers and organizations to bring out their best.&lt;/p&gt; &lt;i&gt;By InfoQ&lt;/i&gt;</description>
      <category>Platform Engineering</category>
      <category>AI Architecture</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>minibook</category>
      <pubDate>Fri, 04 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/minibooks/next-gen-architecture-ai-era/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>InfoQ</dc:creator>
      <dc:date>2026-09-04T11:00:00Z</dc:date>
      <dc:identifier>/minibooks/next-gen-architecture-ai-era/en</dc:identifier>
    </item>
    <item>
      <title>HCP Terraform Positions Itself as the Control Plane for AI-Driven Infrastructure</title>
      <link>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/en/headerimage/generatedHeaderImage-1787561761498.jpg"/&gt;&lt;p&gt;HashiCorp is positioning HCP Terraform as the governance and control plane for a new generation of AI-driven infrastructure, arguing that the rapid adoption of coding agents is shifting the biggest infrastructure challenge from writing configuration to verifying and safely executing it.&lt;/p&gt; &lt;i&gt;By Craig Risi&lt;/i&gt;</description>
      <category>AI Architecture</category>
      <category>Artificial Intelligence</category>
      <category>AI Development</category>
      <category>Terraform</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/hcp-terraform-ai-driven-control/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Craig Risi</dc:creator>
      <dc:date>2026-09-01T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/hcp-terraform-ai-driven-control/en</dc:identifier>
    </item>
    <item>
      <title>DoorDash’s Flux Runs 130,000 Engineering Tasks through Cloud-Based Agents</title>
      <link>https://www.infoq.com/news/2026/08/doordash-flux-cloud-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;DoorDash has moved engineering agent workloads from developer laptops to its Flux cloud platform. The platform automated 130,000 engineering tasks in one month and supports more than 25,000 automated code reviews weekly. Flux uses isolated Firecracker microVMs, an MCP gateway, reusable playbooks, and multiple invocation surfaces to run agent workflows with scoped access and centralized auditing.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Continuous Improvement</category>
      <category>Infrastructure</category>
      <category>AI Security</category>
      <category>AI Development</category>
      <category>AI Harness</category>
      <category>Cloud</category>
      <category>Code Reviews</category>
      <category>Agents</category>
      <category>AI Coding</category>
      <category>VM</category>
      <category>Software Engineering</category>
      <category>AI Architecture</category>
      <category>Developer Experience</category>
      <category>AIOps</category>
      <category>Continuous Deployment</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 31 Aug 2026 14:28:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/doordash-flux-cloud-agent/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-08-31T14:28:00Z</dc:date>
      <dc:identifier>/news/2026/08/doordash-flux-cloud-agent/en</dc:identifier>
    </item>
    <item>
      <title>Foundry Model Router Expands from Two Regions to 28, Refreshing Its Model Pool</title>
      <link>https://www.infoq.com/news/2026/08/foundry-model-router-regions/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/foundry-model-router-regions/en/headerimage/generatedHeaderImage-1787836928169.jpg"/&gt;&lt;p&gt;Microsoft expanded Foundry's model router from two regions to 28 for global standard and 21 for data zone deployments, while adding Claude Opus 4.8 and GPT-5.6 and removing four deprecated models. Default deployments receive pool changes automatically; configured subsets exclude new models until added. The effective context window equals the smallest model in the pool.&lt;/p&gt; &lt;i&gt;By Steef-Jan Wiggers&lt;/i&gt;</description>
      <category>Microsoft Azure</category>
      <category>Azure</category>
      <category>AI Architecture</category>
      <category>Generative AI</category>
      <category>Cloud</category>
      <category>Cost Optimization</category>
      <category>Governance</category>
      <category>Microsoft</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Mon, 31 Aug 2026 10:18:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/foundry-model-router-regions/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Steef-Jan Wiggers</dc:creator>
      <dc:date>2026-08-31T10:18:00Z</dc:date>
      <dc:identifier>/news/2026/08/foundry-model-router-regions/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Architecting the Data Layer for AI Agents: from Transactional Systems to MCP and Semantic Models</title>
      <link>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/presentations/enterprise-data-architecture-ai-agents/en/mediumimage/fabiane-nardon-medium-1787218382028.jpeg"/&gt;&lt;p&gt;Fabiane Nardon shares how TOTVS prepares enterprise data for token-hungry AI agents. She discusses balancing deterministic logic and non-deterministic LLMs across precision, security, and cost. Nardon details using data mesh, low-latency database architectures, semantic ontologies, and dynamic MCP tool selection to optimize context windows and reduce token overhead in transactional systems.&lt;/p&gt; &lt;i&gt;By Fabiane Nardon&lt;/i&gt;</description>
      <category>AI Cost Optimisation</category>
      <category>Transcripts</category>
      <category>AI Security</category>
      <category>AI Architecture</category>
      <category>Semantic Web</category>
      <category>QCon AI Boston 2026</category>
      <category>Agentic AI Architecture</category>
      <category>Model Context Protocol (MCP)</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>Data Mesh</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Sat, 29 Aug 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/enterprise-data-architecture-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Fabiane Nardon</dc:creator>
      <dc:date>2026-08-29T11:00:00Z</dc:date>
      <dc:identifier>/presentations/enterprise-data-architecture-ai-agents/en</dc:identifier>
    </item>
    <item>
      <title>Meta Expands its Custom Silicon Strategy from Compute into Networking</title>
      <link>https://www.infoq.com/news/2026/08/meta-hccl/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Meta has detailed MTIA 300, its first in-house accelerator optimized for training ranking and recommendation models.&lt;/p&gt; &lt;i&gt;By Matt Foster&lt;/i&gt;</description>
      <category>Supercomputer</category>
      <category>AI Architecture</category>
      <category>Hardware</category>
      <category>Networking</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Fri, 28 Aug 2026 07:43:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/meta-hccl/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Matt Foster</dc:creator>
      <dc:date>2026-08-28T07:43:00Z</dc:date>
      <dc:identifier>/news/2026/08/meta-hccl/en</dc:identifier>
    </item>
    <item>
      <title>Diagrid Catalyst 2.0 Adds Durable and Verifiable Execution for AI Agents</title>
      <link>https://www.infoq.com/news/2026/08/diagrid-catalyst-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/08/diagrid-catalyst-ai-agents/en/headerimage/header-1787430272425.jpeg"/&gt;&lt;p&gt;Diagrid Catalyst 2.0 applies Dapr-based recovery, signed workflow history and execution attestation across several agent frameworks. Architects should compare it with framework-native durability and established workflow engines, while evaluating benchmark evidence and operational trade-offs.&lt;/p&gt; &lt;i&gt;By Mark Silvester&lt;/i&gt;</description>
      <category>dapr</category>
      <category>AI Architecture</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Development</category>
      <category>DevOps</category>
      <category>news</category>
      <pubDate>Wed, 26 Aug 2026 07:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/08/diagrid-catalyst-ai-agents/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=AI+Architecture</guid>
      <dc:creator>Mark Silvester</dc:creator>
      <dc:date>2026-08-26T07:00:00Z</dc:date>
      <dc:identifier>/news/2026/08/diagrid-catalyst-ai-agents/en</dc:identifier>
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