Towards Agentic Intelligence: Architectures for Multi-Agent AI Systems

Event Logistics

2 DAY EVENT! Saturday 11/15 AND Sunday 11/16 (details below)

Students Register for the class at this Eventbrite Link. Pricing, with discounts that stack
* 10% discount when signing up for both days with one purchase
* 20% early bird discount
* NEW: 50% student/unemployed discount. To get the discount code, email proof of full time enrollment or unemployment to . Use the email subject “ACM multi-agent PDS: discount”. There is a limited number.

* 50% BOGO discount. Buy one ticket and sign up two people, for a limited number of tickets. This 50% discount does not include the regular food or drink. This discount stacks with “early bird”, “student” or “2 days” discounts.

All pricing includes a $20 annual membership to the SFbayACM

We accept optional donations to our non-profit organization, which is tax deductible. SFbayACM’s Non-Profit Taxpayer ID: 31-0963922
In past years, we have given $3,500 to support STEM education, judging 1,000+ science fair projects at the Synopsis Science Fair, https://science-fair.org/

Day 1, Labs (details below)

• Saturday, Nov 15 8:30am to 5pm
• Sign up for a limited number of seats in person at
• Valley Research Park, 319 North Bernardo Ave, Mountain View, CA.
• Go to the right side of the building, 2nd door
• Coffee breaks and lunch is included in the price
• Zoom for a remote audience
• Both the local and remote audience will be supported by TA’s. The moderator will watch the Zoom chat for questions for the speaker, and ask them.
• Remote tickets have a built in discount, because food is not provided

Day 2, Lectures (outlined below)

• Sunday, Nov 16, 9am to 5pm, only on Zoom
• Zoom for a remote audience
• The remote audience will be supported by questions that can be directed to the speaker by the moderator
• Remote tickets have a built in discount, because food is not provided

Overview

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.
This 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.
By 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.

Learning Format and Structure

This 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.

???? Day 1 — Architecting Multi-Agent Systems

• 4 hands-on labs focused on:
• Building multi-agentic systems/use-cases from first principles
• Exploring agent tools, MCP operability, and orchestration strategies
• Converting agentic prototypes into robust, production-ready cognitive pipelines

???? Day 2 — Deep Dive: Research Frontiers and Reproduction

• Analytical walkthroughs of seminal and frontier papers in Agentic AI from NeurIPS, ICLR, and KDD
• Structured methodology for paper-to-prototype translation: reproducing cutting-edge research through practical labs
• Discussions on evaluation benchmarks, alignment frameworks, and emergent behavior analysis

Key Outcomes

Theoretical Foundations: Understand the mathematical and algorithmic underpinnings of multi-agent LLM architectures, orchestration, and alignment.
Hands-On Mastery: Gain practical experience in building agentic systems from scratch, configuring MCP operability, and scaling prototypes into production-grade deployments.
**Evaluation & Governance:**Learn to design and apply alignment and evaluation frameworks to ensure robustness, interpretability, and responsible deployment of multi-agent systems.
Practical Assets: Walk away with fully functional notebooks, baseline reference architectures, curated reading lists, and reproducible workflows to accelerate implementation in your own organization.
ACM Certificate for completing the class.

Prerequisites

• Intermediate to advanced Python programming is nice-to-have. However, if you are a manager, product manager, CTO or other that wants exposure to the technology, you can still get alot out of the class. You can run code in a Jupyter Notebook step by step.
• Familiarity with APIs and Jupyter/Colab environments is helpful.
• Experience with or interest in LLMs (OpenAI API will be used; open-source alternatives such as Claude, Mistral, and Llama 3 will also be discussed)

Who Should Attend

• AI/ML Engineers, Data Scientists & AI Practitioners designing or deploying LLM applications
• Applied Researchers & Postdocs exploring Agentic AI, neuro-symbolic systems, or autonomous orchestration
• Technical Leaders & Architects integrating multi-agent reasoning in production environments

Speaker Bio

Dhanashree is a Senior Machine Learning Engineer and AI Researcher with over a decade of experience designing and deploying advanced AI systems at scale. Her expertise spans architecting multi-agent solutions that integrate Large Language Models (LLMs), computer vision pipelines, and structured data to solve complex enterprise challenges across industries including retail, healthcare, and finance.
At Albertsons, Deloitte, and Fractal, Dhanashree has led the development of production-grade AI applications, focusing on optimization, model observability, and responsible AI practices. Her work includes designing scalable inference architectures for LLMs on modern GPU infrastructures, building hybrid pipelines that fuse vision and language models, and engineering systems that balance performance with ethical and regulatory considerations.
She actively collaborates with research institutions like the University of Illinois. Dhanashree actively engages with the research community and frequently speaks on bridging advanced AI research and production systems.
https://www.linkedin.com/in/dhanashreelele/

Dhanashree gave a prior ACM Talk – “Deploying & Scaling LLM in the Enterprise: Architecting Multi-agent AI Systems”

• Meetup Talk Description, Monday, Sept 22, 2025
• Video recording (2h 3m with many questions)

SPONSOR INFORMATION:
From vision to execution, Ccube partners with forward-thinking clients to co-build Apps, Data, and GenAI solutions across industries. Ccube has 10+ service lines, 30+ happy clients, 90% client retention, and saved clients ~50% costs on average.
Ccube has Silicon Valley roots, deep expertise, customer first approach and leverages lean teams for onsite in US and offshore delivery teams in India.
Watch for us also on
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As a way to “thank your sponsor”, Ccube invites you to share your contact info, and take a brief survey. A summary of the survey results will be shared at the event.