BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260304T234602Z UID:F1DF0B75-61F1-448B-9AC3-9DB7A401F1AB DTSTART;TZID=Etc/UTC:20260219T120000 DTEND;TZID=Etc/UTC:20260219T130000 DESCRIPTION:[SAFE-AGENT-L: Engineering Legal-Safe\, Explainable Generative AI for Large-Scale Autonomous Systems]\n\nSpecial Presentation by Vasanth Rajendran (Amazon\, USA)\n\nHosted by the Future Networks Artificial Intel ligence & Machine Learning (AIML) Working Group\n\nDate/Time: Thursday\, 1 9 February 2025 @ 12:00 UTC (12 PM GMT)\n\nTopic:\n\nAutonomous Retail Age nts and the SAFE-AGENT-L Framework: Engineering Legal-Safe\, Explainable G enerative AI for Large-Scale Autonomous Systems\n\nAbstract:\n\nNext-gener ation autonomous generative AI systems depend on continuous\, low-latency\ , and highly reliable communication among distributed models\, services\, and agents to enable real-time decision making at scale. This talk introdu ces the SAFE-AGENT-L governance and system-design framework for building s afe\, accountable\, and explainable AI-driven retail agents\, with explici t focus on the bidirectional relationship between these agents and the und erlying communication networks. This presentation examines the concrete re quirements that large-scale autonomous agent systems impose on communicati on networks\, including ultra-low-latency coordination\, reliable and orde red message delivery\, context propagation\, distributed observability\, a nd resilience to partial failures. It also discusses how real-world networ k characteristics such as variable bandwidth\, latency spikes\, packet los s\, and asynchronous communication have shaped the design of safety contro ls\, policy enforcement\, and explainability mechanisms within the SAFE-AG ENT-L framework. Drawing from applied experience with large-scale generati ve AI systems and network-mediated workflows\, the talk shares lessons tha t directly inform the design of AI-native future networks\, including agen t-to-agent communication patterns\, adaptive routing of AI control signals \, network-supported auditability\, and trustworthy automation. The sessio n concludes with implications and recommendations for next-generation comm unication networks that natively support safe and scalable autonomous AI s ystems.\n\nSpeaker:\n\n[]\nVasanth Rajendran is an engineering leader at A mazon specializing in large-scale artificial intelligence systems and gene rative AI. He leads cross functional engineering and science teams buildin g production deployed multimodal and generative systems that power retail discovery\, personalization\, and automated content generation for hundred s of millions of customers worldwide. His work focuses on autonomous AI sy stems\, applied machine learning\, multimodal reasoning\, and responsible AI deployment at scale. Vasanth is an IEEE Senior Member and a Sigma Xi me mber\, and an active contributor to the global AI research community. He h as authored multiple peer reviewed publications and regularly serves as a reviewer\, session chair\, and invited speaker across IEEE and leading AI venues.\n\nBrochure (PDF): [Webinar-AIML-2026-02-19-Rajendran-SafeAgentL-B rochure.pdf](https://drive.google.com/file/d/1AUB9Vjzu2IIlyh5NVjaonqBcD46l i9Kl/view)\n\nCo-sponsored by: Future Networks Artificial Intelligence & M achine Learning (AIML) Working Group\n\nVirtual: https://events.vtools.iee e.org/m/522074 LOCATION:Virtual: https://events.vtools.ieee.org/m/522074 ORGANIZER:baw@ieee.org SEQUENCE:24 SUMMARY:SAFE-AGENT-L Framework: Engineering Legal-Safe\, Explainable GenAI for Large-Scale Autonomous Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/522074 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-top: .25in \;"><img src="https://events.vtools.ieee.org/vtools_ui/media/display/b750c a00-92a4-4438-ab1c-9e48453e5da7" alt="SAFE-AGENT-L: Engineering Legal-Safe \, Explainable Generative AI for Large-Scale Autonomous Systems" width="75 0" height="197"></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;">Sp ecial Presentation by<strong> Vasanth Rajendran (Amazon\, USA)</strong></p >\n<p class="MsoNormal" style="margin-top: 12.0pt\;">Hosted by the Future Networks<strong> Artificial Intelligence &amp\; Machine Learning (AIML) Wo rking Group</strong></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\; "><strong><span style="font-size: 14.0pt\; font-family: Copperplate\; mso- fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; ms o-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-lan guage: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">D ate/Time</span></strong><span style="font-size: 12.0pt\; font-family: 'Cal ibri'\,sans-serif\; mso-ascii-theme-font: minor-latin\; mso-fareast-font-f amily: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme- font: minor-latin\; mso-bidi-font-family: Arial\; mso-bidi-theme-font: min or-bidi\; mso-ansi-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bid i-language: AR-SA\;">: <strong>Thursday\, 19 February 2025</strong><strong > @ 12:00 UTC (12 PM GMT)</strong></span></p>\n<p class="MsoNormal" style= "margin-top: .25in\;"><strong><u><span style="font-size: 16.0pt\; font-fam ily: Copperplate\;">Topic</span></u></strong><strong><span style="font-siz e: 16.0pt\; font-family: Copperplate\;">:</span></strong></p>\n<p class="M soNormal"><strong><span style="font-size: 16pt\;">Autonomous Retail Agents and the SAFE-AGENT-L Framework: Engineering Legal-Safe\, Explainable Gene rative AI for Large-Scale Autonomous Systems&nbsp\;</span></strong></p>\n< p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span style="fo nt-size: 16.0pt\; font-family: Copperplate\;">Abstract</span></u></strong> <strong><span style="font-size: 16.0pt\; font-family: Copperplate\;">:</sp an></strong></p>\n<p>Next-generation autonomous generative AI systems depe nd on continuous\, low-latency\, and highly reliable communication among d istributed models\, services\, and agents to enable real-time decision mak ing at scale. This talk introduces the SAFE-AGENT-L governance and system- design framework for building safe\, accountable\, and explainable AI-driv en retail agents\, with explicit focus on the bidirectional relationship b etween these agents and the underlying communication networks. This presen tation examines the concrete requirements that large-scale autonomous agen t systems impose on communication networks\, including ultra-low-latency c oordination\, reliable and ordered message delivery\, context propagation\ , distributed observability\, and resilience to partial failures. It also discusses how real-world network characteristics such as variable bandwidt h\, latency spikes\, packet loss\, and asynchronous communication have sha ped the design of safety controls\, policy enforcement\, and explainabilit y mechanisms within the SAFE-AGENT-L framework. Drawing from applied exper ience with large-scale generative AI systems and network-mediated workflow s\, the talk shares lessons that directly inform the design of AI-native f uture networks\, including agent-to-agent communication patterns\, adaptiv e routing of AI control signals\, network-supported auditability\, and tru stworthy automation. The session concludes with implications and recommend ations for next-generation communication networks that natively support sa fe and scalable autonomous AI systems.</p>\n<p><strong><span style="font-s ize: 16.0pt\; font-family: Copperplate\;"><u>Speaker</u>:</span></strong>< /p>\n<table style="border-collapse: collapse\; width: 100%\;" border="1">< colgroup><col style="width: 18.522073%\;"><col style="width: 81.381958%\;" ></colgroup>\n<tbody>\n<tr>\n<td><img style="display: block\; margin-left: auto\; margin-right: auto\;" src="https://events.vtools.ieee.org/vtools_u i/media/display/2e49010b-3336-447f-ba73-1b5978fcf584" alt="" width="160" h eight="179"></td>\n<td>\n<p class="MsoNormal" style="margin-top: 6pt\; tex t-align: left\;"><strong>Vasanth Rajendran</strong> is an engineering lead er at Amazon specializing in large-scale artificial intelligence systems a nd generative AI. He leads cross functional engineering and science teams building production deployed multimodal and generative systems that power retail discovery\, personalization\, and automated content generation for hundreds of millions of customers worldwide. His work focuses on autonomou s AI systems\, applied machine learning\, multimodal reasoning\, and respo nsible AI deployment at scale. Vasanth is an IEEE Senior Member and a Sigm a Xi member\, and an active contributor to the global AI research communit y. He has authored multiple peer reviewed publications and regularly serve s as a reviewer\, session chair\, and invited speaker across IEEE and lead ing AI venues.</p>\n</td>\n</tr>\n</tbody>\n</table>\n<p>&nbsp\;</p>\n<p>< strong>Brochure (PDF)</strong>: <strong><a id="ow261" class="pastedDriveLi nk-1" href="https://drive.google.com/file/d/1AUB9Vjzu2IIlyh5NVjaonqBcD46li 9Kl/view" target="_blank" rel="noopener">Webinar-AIML-2026-02-19-Rajendran -SafeAgentL-Brochure.pdf</a>&nbsp\;</strong></p> END:VEVENT END:VCALENDAR