{"id":19458,"date":"2026-09-09T14:08:39","date_gmt":"2026-09-09T21:08:39","guid":{"rendered":"https:\/\/selectoraistg.wpengine.com\/?post_type=resources&#038;p=19458"},"modified":"2026-09-09T14:10:17","modified_gmt":"2026-09-09T21:10:17","slug":"introducing-selector-foundry-ai-for-network-leaders-summit","status":"publish","type":"resources","link":"https:\/\/www.selector.ai\/ja\/resource\/introducing-selector-foundry-ai-for-network-leaders-summit\/","title":{"rendered":"Introducing Selector Foundry | AI for Network Leaders Summit"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"19458\" class=\"elementor elementor-19458\" data-elementor-post-type=\"resources\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ce17b14 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-parent\" data-id=\"ce17b14\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-c1d8cbe e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"c1d8cbe\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t<div class=\"elementor-element elementor-element-139f9dc e-con-full e-flex wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no wpr-column-slider-no wpr-equal-height-no e-con e-child\" data-id=\"139f9dc\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-33079d0 elementor-widget elementor-widget-image\" data-id=\"33079d0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"450\" src=\"https:\/\/www.selector.ai\/wp-content\/uploads\/2026\/09\/8-1024x576.png\" class=\"attachment-large size-large wp-image-19408\" alt=\"\" srcset=\"https:\/\/www.selector.ai\/wp-content\/uploads\/2026\/09\/8-1024x576.png 1024w, https:\/\/www.selector.ai\/wp-content\/uploads\/2026\/09\/8-300x169.png 300w, https:\/\/www.selector.ai\/wp-content\/uploads\/2026\/09\/8-768x432.png 768w, https:\/\/www.selector.ai\/wp-content\/uploads\/2026\/09\/8.png 1280w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d06e7cc elementor-widget elementor-widget-text-editor\" data-id=\"d06e7cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p dir=\"ltr\">Nitin Kumar (CTO &amp; Co-founder @ Selector), Surya Nimmagadda (Chief Data Scientist @ Selector), and Joby Rudolph (Senior Distinguished Engineer @ Selector)<\/p><p dir=\"ltr\">If agentic AI in network operations is only as good as the data it reasons over, what does it actually take to build a foundation that agents can trust \u2014 and what happens when you turn that foundation loose on a live incident?<\/p><p dir=\"ltr\">In this session, three members of Selector&#8217;s founding and engineering leadership walk through the evolution of their platform, from the original vision of a network that &#8220;talks to you&#8221; to a full fleet of specialized agents working alongside operators \u2014 closing with a live, end-to-end demo of an incident being investigated, visualized, fixed, and automated away.<\/p><p dir=\"ltr\">They cover:<\/p><ul dir=\"ltr\"><li>Selector&#8217;s origin vision \u2014 a &#8220;social network&#8221; for infrastructure \u2014 and its evolution into Rosetta, a conversational agent, and now Agent Smith, a runtime for deploying a whole roster of agents on top of the data platform<\/li><li>Why agentic AI without a strong data foundation just &#8220;hallucinates at high speed&#8221; \u2014 and Selector&#8217;s data-centric architecture: collection services, a &#8220;data hypervisor&#8221; that normalizes and enriches raw telemetry with customer-specific metadata, and machine learning models that build adaptive baselines rather than static thresholds<\/li><li>How metrics, logs, and human-generated data (configs, maintenance emails) are each processed differently, then fused into a correlation graph that captures causation and cross-device blast radius<\/li><li>The six core agents in Selector&#8217;s fleet \u2014 Rosetta (orchestrator), Loom (correlation\/root-cause), Ticketmaster (ITSM integration), Herald (reporting\/alerts), Artist (on-demand dashboard building), and Sculptor (an agent that builds other agents) \u2014 plus how the platform exposes its capabilities as MCP tools so custom agents and external MCPs (ServiceNow, PagerDuty, APM tools) can plug in<\/li><li>A full live demo: an operator named Arvind investigates a P2 incident via natural-language conversation, gets an AI-generated root cause tied to a specific bad config change, builds a custom dashboard by describing it in plain English, checks for cross-system impact through an external APM integration, creates and extends a maintenance window, verifies the fix restored compliance, and finally uses Sculptor to spin up a background agent that will catch and flag similar bad configs automatically going forward<\/li><\/ul><p dir=\"ltr\">\u00a0<\/p><p dir=\"ltr\">The throughline across all three talks: agentic AI succeeds or fails on the strength of the data and correlation work underneath it \u2014 the &#8220;boring&#8221; engineering of ingestion, normalization, and baselining is what makes the flashy conversational layer trustworthy enough to act on.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div 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