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DTSTART;TZID=America/Los_Angeles:20260715T110000
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SUMMARY:Artificial Intelligence\, Using Safely
DESCRIPTION:Social gathering\, presentation and round table discussion. Speakers to be determined. Audience participation welcome.\nAgenda:\nSocial gathering followed by food service and presentations. Select from menu\, or buffet depending upon the number of registrants.\nBldg: Golf course restaurant\, not pro shop\, Beeb's Sports Bar and Grill\, 915 Club House Drive\, Livermore\, California\, United States\, 94550
URL:https://svec.org/event/artificial-intelligence-using-safely/
LOCATION:Bldg: Golf course restaurant\, not pro shop\, Beeb's Sports Bar and Grill\, 915 Club House Drive\, Livermore\, California\, United States\, 94550
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260715T180000
DTEND;TZID=America/Los_Angeles:20260715T190000
DTSTAMP:20260619T020331
CREATED:20260612T163313Z
LAST-MODIFIED:20260612T163313Z
UID:78519-1784138400-1784142000@svec.org
SUMMARY:Beyond the Model: The Industry Playbook for Scalable AI Systems
DESCRIPTION:As artificial intelligence transitions from experimental deployments to mission-critical production infrastructure\, organizations face a fundamental shift from model-centric optimization to system-centric engineering. While advances in model architectures and accelerator technologies have driven recent AI breakthroughs\, long-term performance\, reliability\, and sustainability increasingly depend on the interaction between compute\, memory\, networking\, software runtimes\, operations\, and governance. This talk presents an industry roadmap for building scalable AI systems that move beyond isolated model optimization toward adaptive\, software-defined AI platforms. The roadmap explores five interconnected layers—compute\, memory and data\, interconnect\, runtime and operating systems\, and operations and governance—and demonstrates how these layers collectively influence throughput\, latency\, cost\, energy efficiency\, reliability\, and compliance. The discussion introduces workload-aware architectures for inference\, retrieval-augmented generation (RAG)\, agentic workflows\, multimodal applications\, and edge AI\, highlighting the growing importance of memory hierarchies\, topology-aware scheduling\, adaptive control loops\, and cluster-scale orchestration. A practical AI systems maturity model is proposed to help organizations assess current capabilities and prioritize investments\, progressing from ad hoc experimentation to autonomous\, policy-governed AI fabrics. The presentation concludes with a pragmatic execution framework and industry best practices for achieving predictable service levels\, operational resilience\, and sustainable AI economics. The central thesis is that future AI leadership will be determined not by model performance alone\, but by the ability to design\, operate\, and govern AI as an integrated systems platform\nCo-sponsored by: Vishnu S. Pendyala\, San Jose State University\nSpeaker(s): Sujit Reddy Thumma\nVirtual: https://events.vtools.ieee.org/m/563401
URL:https://svec.org/event/beyond-the-model-the-industry-playbook-for-scalable-ai-systems/
LOCATION:Virtual: https://events.vtools.ieee.org/m/563401
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DTSTART;TZID=-07:00:20260715T190000
DTEND;TZID=-07:00:20260715T210000
DTSTAMP:20260619T020331
CREATED:20260420T210319Z
LAST-MODIFIED:20260420T210319Z
UID:78271-1784142000-1784149200@svec.org
SUMMARY:Multi-Agent Systems at Scale as a Shared Platform for the enterprises
DESCRIPTION:AI Agent Infrastructure as a Shared Platform: Patterns for Multi-Agent Systems at Scale for the enterprise. \nLOCATION ADDRESS (Hybrid\, in person or by zoom\, you choose)\nValley Research Park\n319 North Bernardo Avenue\nMountain View\, CA CA 93043\nDon’t use the front door. When facing the front door\, turn right along the front of the building. Turn left around the building corner. The 2nd door should be open and have a banner and event registration. \nIf you want to join remotely\, you can submit questions via Zoom Q&A. The zoom link:\n[Zoom](https://acm-org.zoom.us/j/92225957844?pwd=E1L50oEkTFvwai73PYfGoqsPdi9xIL.1) (updated 6:55 pm)\nJoin via YouTube:\nhttps://www.youtube.com/watch?v=pO72Hb30fKw \nAGENDA\n6:30 Door opens\, food and networking (we invite honor system contributions)\n7:00 SFBayACM upcoming events\, introduce the speaker\n7:15 Speaker presents.\n8:30 – 8:45 finish\, depending on Q&A \nJoin SF Bay ACM Chapter for an insightful discussion on: \n### **Abstract & Overview** \nAn agent is simple: Prompt + Tools + Model + Boilerplate. The first three are where product teams create value. The last one—state management\, history compression\, streaming\, cancellation\, tracing\, memory\, persistence—is 80% of the code but 0% of the differentiation.\nAt ThoughtSpot\, we built an Agent Platform that draws a hard line between agent logic and agent infrastructure\, letting product teams ship customer-facing agents faster by owning only what matters: their prompts and their tools.\nThis talk covers the infrastructure patterns behind that separation:\n**State management across tool calls.** Stateless tools (state on the agent\, passed as arguments) give you testability and let the LLM reason about state. Stateful tools (state in the tool service) avoid serialization overhead. I’ll walk through flow diagrams\, show how we propagate state via tool response metadata\, and discuss when each pattern fits.\n**Configuration-driven agent definitions.** Agents defined entirely through config—templated prompts\, tool endpoints\, sub-agent rules\, compression strategies. Teams ship agents without writing orchestration code.\n**Inter-agent communication.** Two patterns: agents-as-tools (sub-agent called like any tool\, returns structured output) and agent handoff (full conversation transfer). The platform handles routing and context—teams just declare delegation rules.\n**Shared memory across agents.** Memory in the platform\, not individual agents\, means knowledge accumulates across agent boundaries. Tiered scoping (tenant\, org\, user) with retrieval that surfaces relevant context regardless of which agent captured it.\n**Tool protocol design.** MCP as the base\, with patterns layered on top: cancellation semantics\, progress streaming\, context variable propagation\, and adapters for existing services.\nBuilding for customer-facing scale adds constraints—high concurrency\, encryption\, tenant isolation\, auditability—that shaped our API design throughout.\n**Takeaways:** \n* Mental model for separating agent value from infrastructure\n* State patterns: agent-side vs. tool-side tradeoffs\n* Inter-agent communication: tools vs. handoff\n* Shared memory architecture across agent boundaries\n* MCP extensions for production systems. \nSpeaker Bio\nAshish Shubham is Fellow/Vice President of Engineering at ThoughtSpot\, where he leads the architecture of enterprise-scale AI and embedded analytics platforms used by Fortune 500 organizations. He is the author of *Architecting AI Data Systems* and an inventor on multiple U.S. patents in natural-language-to-SQL\, generative AI interfaces\, and intelligent analytics. Ashish is an IEEE Senior Member and an active reviewer and committee contributor for leading IEEE and ACM conferences and workshops. His work bridges academic research and real-world deployment\, with a focus on building scalable\, trustworthy\, and developer-centric AI systems for production environments.\n[https://linkedin.com/in/ashubham](https://linkedin.com/in/ashubham) \n—\nValley Research Park is a coworking research campus of 104\,000 square feet hosting 60+ life science and technology companies. VRP has over 100 dry labs\, wet labs\, and high power labs sized from 125-15\,000 square feet. VRP manages all of the traditional office elements: break rooms\, conference rooms\, outdoor dining spaces\, and recreational spaces. \nAs a plug-and-play lab space\, once companies have secured their next milestone and are ready to expand\, VRP has 100+ labs ready to expand into.\nhttps://www.valleyresearchpark.com/
URL:https://svec.org/event/multi-agent-systems-at-scale-as-a-shared-platform-for-the-enterprises/
LOCATION:Valley Research Park\, 319 N Bernardo Ave\, Mountain View\, CA\, 94043\, United States
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