{"id":33788,"date":"2026-06-11T08:25:10","date_gmt":"2026-06-11T15:25:10","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=event&#038;p=33788"},"modified":"2026-06-11T08:25:12","modified_gmt":"2026-06-11T15:25:12","slug":"the-memory-wall-why-ai-agents-fail","status":"publish","type":"event","link":"https:\/\/www.pingcap.com\/ko\/event\/the-memory-wall-why-ai-agents-fail\/","title":{"rendered":"The Memory Wall: Why AI Agents Fail (and How to Fix Them) (EMEA)"},"content":{"rendered":"<strong>Date: <\/strong>June 25, 2026 <strong>Time: <\/strong>11:00 &#8211; 12:00 PM CEST\n\n<!-- \/wp:post-content -->\n\nToday&#8217;s AI agents run on duct tape: Separate systems for transactions, analytics, and vectors, stitched together with brittle ETL pipelines and growing engineering overhead. That approach won&#8217;t survive the next wave of agentic AI, where enterprises need to support millions of concurrent agents, each with persistent memory, real-time data access, and consistent state. In this webinar, TiDB Principal Solutions Architect, Bernard Kavanagh, introduces the architecture that supports millions of agents: A converged distributed SQL platform that unifies OLTP, real-time analytics, and native vector search in a single ACID-compliant system, handling relational, vector, and full-text demands in one query layer. We&#8217;ll walk through the Decide-Validate-Remember loop that production teams use to reduce token costs through persistent context and scale multi-agent workflows without state drift. What you&#8217;ll learn:\n<ul>\n \t<li>Zero-ETL reasoning: How to give agents access to live operational data without the synchronization headache.<\/li>\n \t<li>The ACID advantage: Using database transactions to coordinate multiple agents without race conditions.<\/li>\n \t<li>Episodic memory: How to store learned actions as vectors alongside production data to cut token usage.<\/li>\n \t<li>Safe sandboxing: Using serverless branching to let agents test solutions before they reach production.<\/li>\n<\/ul>\n<p class=\"has-medium-font-size\"><strong style=\"font-size: 22px;\">Speakers:<\/strong><\/p>\n<!-- wp:columns {\"verticalAlignment\":\"center\"} -->\n<div class=\"wp-block-columns are-vertically-aligned-center\"><!-- wp:column {\"verticalAlignment\":\"center\",\"width\":\"33.33%\"} -->\n<div class=\"wp-block-column is-vertically-aligned-center\" style=\"flex-basis: 33.33%;\"><!-- wp:image {\"id\":20518,\"width\":\"244px\",\"height\":\"auto\",\"sizeSlug\":\"full\",\"linkDestination\":\"none\"} -->\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-33517 size-medium\" src=\"https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-300x300.png\" alt=\"\" width=\"300\" height=\"300\" srcset=\"https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-300x300.png 300w, https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-1024x1024.png 1024w, https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-150x150.png 150w, https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-768x768.png 768w, https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle-1536x1536.png 1536w, https:\/\/static.pingcap.com\/files\/2026\/05\/12111632\/Bernard_circle.png 2000w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<!-- \/wp:image --><\/div>\n<!-- \/wp:column -->\n\n<!-- wp:column {\"verticalAlignment\":\"center\",\"width\":\"66.66%\"} -->\n<div class=\"wp-block-column is-vertically-aligned-center\" style=\"flex-basis: 66.66%;\"><!-- wp:paragraph {\"style\":{\"typography\":{\"fontSize\":\"20px\"}}} -->\n<h4 style=\"font-size: 20px;\"><strong>Bernard Kavanagh, Principal Solutions Engineer at TiDB<\/strong><!-- wp:paragraph {\"style\":{\"typography\":{\"fontSize\":\"20px\"}}} --><\/h4>\nBernard is a specialist in high-performance data systems, helping organizations rapidly pivot from disjointed data stacks to unified infrastructure required for the modern agentic era. At TiDB, he architects Agent-First memory stores that consolidate Vector Search, Hybrid SQL, and Real-Time Analytics into a single platform , replacing fragmented multi-service stacks where AI agents lose context and accumulate cost. His work productionising AI workflows includes a real-time multi-agent platform for 200,000+ IoT devices that cut infrastructure costs by 65% and LLM token consumption by 10x with agents that improve diagnostic accuracy from every investigation.\n\n<\/div>\n<\/div>\n<div class=\"wp-block-columns are-vertically-aligned-center\">\n<div class=\"wp-block-column is-vertically-aligned-center\" style=\"flex-basis: 66.66%;\"><!-- \/wp:paragraph --><\/div>\n<!-- \/wp:paragraph --><\/div>\n<!-- wp:paragraph -->\n\n<!-- \/wp:paragraph --><!-- \/wp:column -->","protected":false},"excerpt":{"rendered":"<p>Date: June 25, 2026 Time: 11:00 &#8211; 12:00 PM CEST Today&#8217;s AI agents run on duct tape: Separate systems for transactions, analytics, and vectors, stitched together with brittle ETL pipelines and growing engineering overhead. That approach won&#8217;t survive the next wave of agentic AI, where enterprises need to support millions of concurrent agents, each with [&hellip;]<\/p>\n","protected":false},"featured_media":33789,"template":"","categories":[13,145],"tags":[138,391,147,16,250,301,162,9,111,31],"region":[215],"location":[207],"class_list":["post-33788","event","type-event","status-publish","has-post-thumbnail","hentry","category-product","category-thought-leadership","tag-ai","tag-artificial-intelligence","tag-distributed-sql","tag-distributed-system","tag-distributed-transactions","tag-nosql","tag-saas","tag-scalability","tag-tidb","tag-tidb-cloud","region-emea","location-virtual"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Memory Wall: Why AI Agents Fail and How to Fix It | TiDB<\/title>\n<meta name=\"description\" content=\"AI agents fail when they hit the memory wall. 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