{"id":31327,"date":"2026-01-15T16:12:17","date_gmt":"2026-01-16T00:12:17","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=ebook-whitepaper&#038;p=31327"},"modified":"2026-01-15T16:12:18","modified_gmt":"2026-01-16T00:12:18","slug":"oreilly-report-agentic-ai-data-architectures","status":"publish","type":"ebook-whitepaper","link":"https:\/\/www.pingcap.com\/ko\/ebook-whitepaper\/oreilly-report-agentic-ai-data-architectures\/","title":{"rendered":"O&#8217;Reilly Report: Agentic AI Data Architectures"},"content":{"rendered":"<p>As AI shifts from one-off prompts to autonomous, context-aware agents, enterprise infrastructure is hitting a new ceiling. The bottleneck is not compute. It is memory.<\/p>\n\n\n\n<p>In this latest O&#8217;Reilly report, you&#8217;ll discover how intelligent systems can retrieve, recall, and reason over interrelated data while staying fast, consistent, and explainable. You&#8217;ll also explore why distributed SQL is emerging as the backbone of AI-ready data layers, unifying structured, semantic, and temporal retrieval at scale.<\/p>\n\n\n\n<p>Built for data architects, AI infrastructure engineers, and technology leaders, this free report delivers practical guidance and proven patterns, including RAG pipelines, long-term memory graphs, and hybrid transactional + analytical architectures, so you can design memory systems that hold up in production.<\/p>\n\n\n\n<p>Download the full report to learn how to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify why memory, not compute, is the limiting factor for agentic AI<\/li>\n\n\n\n<li>Unify structured data, vectors, and history into a single retrieval layer<\/li>\n\n\n\n<li>Evaluate trade-offs between distributed SQL, vector databases, and hybrid stacks<\/li>\n\n\n\n<li>Design scalable architectures for persistent, contextual AI workloads<\/li>\n\n\n\n<li>Modernize your data foundation to support intelligent, adaptive systems<\/li>\n<\/ul>","protected":false},"excerpt":{"rendered":"<p>As AI shifts from one-off prompts to autonomous, context-aware agents, enterprise infrastructure is hitting a new ceiling. The bottleneck is not compute. It is memory. In this latest O&#8217;Reilly report, you&#8217;ll discover how intelligent systems can retrieve, recall, and reason over interrelated data while staying fast, consistent, and explainable. You&#8217;ll also explore why distributed SQL [&hellip;]<\/p>\n","protected":false},"featured_media":31334,"template":"","categories":[145],"tags":[441,147,298,9,297],"class_list":["post-31327","ebook-whitepaper","type-ebook-whitepaper","status-publish","has-post-thumbnail","hentry","category-thought-leadership","tag-agentic-ai","tag-distributed-sql","tag-rag","tag-scalability","tag-vector-search"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Agentic AI Data Architectures: Distributed SQL for AI Memory<\/title>\n<meta name=\"description\" content=\"See how distributed SQL powers agentic AI data architectures: unify structured data, vector search, and history for RAG + long-term context.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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