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SUMMARY:Towards Agentic Intelligence: Architectures for Multi-Agent AI Systems
DESCRIPTION:Building on the tremendous response to Dhanashree Lele’s ACM talk on Multi-Agent Architectures for Enterprise AI\, this 2-day\, research-caliber\, hands-on workshop is designed to advance the state of practice in Agentic AI system design\, evaluation\, and optimization. \nThis workshop will guide participants through theory-to-deployment workflows for constructing next-generation multi-agent frameworks\, benchmarking agentic behaviors\, and applying compute-efficient orchestration strategies. The curriculum draws heavily from recent breakthroughs presented at NeurIPS\, ICLR\, and KDD\, grounding hands-on engineering in rigorous scientific principles and reproducible experimentation. \nBy bridging academic research and production-grade engineering\, this workshop is ideal for applied researchers\, industry practitioners\, graduate students\, and technical leaders seeking to design reliable\, interpretable\, and high-performance LLM-based agentic systems. \nLearning Format and Structure\nThis intensive two-day workshop follows a progressive “build-as-you-learn” methodology. Each module introduces core research concepts followed by guided implementation in Jupyter/Google Colab\, enabling participants to translate theory directly into working systems. \n📅 Day 1 — Architecting Multi-Agent Systems\n4 hands-on labs focused on: \n\n\n\nBuilding multi-agentic systems/use-cases from first principles\nExploring agent tools\, MCP operability\, and orchestration strategies\nConverting agentic prototypes into robust\, production-ready cognitive pipelines\nImplementing coordination\, planning\, and tool-use protocols across agents\n\n\n\n📅 Day 2 — Deep Dive: Research Frontiers and Reproduction\n\nAnalytical walkthroughs of seminal and frontier papers in Agentic AI from NeurIPS\, ICLR\, and KDD\nStructured methodology for paper-to-prototype translation: reproducing cutting-edge research through practical labs\nDiscussions on evaluation benchmarks\, alignment frameworks\, and emergent behavior analysis\nRoadmapping techniques for embedding research-grade systems into real-world enterprise use cases\n\nKey Outcomes\nTheoretical Foundations:Understand the mathematical and algorithmic underpinnings of multi-agent LLM architectures\, orchestration\, and alignment. \nHands-On Mastery:Gain practical experience in building agentic systems from scratch\, configuring MCP operability\, and scaling prototypes into production-grade deployments. \nEvaluation & Governance:Learn to design and apply alignment and evaluation frameworks to ensure robustness\, interpretability\, and responsible deployment of multi-agent systems. \nPractical Assets:Walk away with fully functional notebooks\, baseline reference architectures\, curated reading lists\, and reproducible workflows to accelerate implementation in your own organization. \nRegistration:\nEventbrite tickets
URL:https://svec.org/event/towards-agentic-intelligence-architectures-for-multi-agent-ai-systems/
LOCATION:Valley Research Park\, 319 North Bernardo Ave\, Mountain View\, CA.\, Valley Research Park\, 319 North Bernardo Ave\, Mountain View\, CA\, United States
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