Learn from the Minds Shaping Agentic AI

Josh Reini

Developer Advocate | Snowflake

Karen Zhou

Member of Technical Staff | Anthropic

Matan-Paul Shetrit

Director of Product , a leader and pioneer in enterprise AI | Writer

Sydney Runkle

Software Engineer | LangChain

Leonardo De Marchi

VP of Labs | Thomson Reuters

Andrea Kropp

Applied AI Engineer | LandingAI

Manoj Saxena

Founder & CEO | Trustwise

Claire Longo

AI Researcher | Comet

Kyle Stratis

Founder Stratis | Data Labs

Cameron Royce Turner

Founder and CEO | TRUIFY.AI

Zain Hasan, PhD

Staff AI/ML Engineer - DevRel | Together AI

Sara Zanzottera

Senior Developer | BNP Paribas

Harpreet Sahota

Hacker-in-Residence | Voxel51

Sanyam Bhutani

Partner Engineer Generative AI Engineer at Meta

João (Joe) Moura

CEO at CrewAI

Sheamus McGovern

Founder of ODSC, Head of AI and VP - Cortical Ventures

Holt Skinner

Developer Advocate at Google Cloud

Ivan Lee

CEO / Founderat Datasaur

Sinan Ozdemir

AI Author and Educator

Dr. Ali Arsanjani

Director, Applied AI, Google Cloud

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‍We are accepting hands-on workshop submissions for a limited time. Alternatively, If you are interested in sponsorship please contact partners@odsc.com‍

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Preliminary Schedule Overview

Week 0 - Introductory Workshops

Theme: Build Your First Agent – Tools, Prompts, and Use Cases

Week 1 - Foundation

Theme: Understanding AI Agents and Their Business Impact

Week 2 - Implementation

Theme: Advanced Patterns and Industry Solutions

Shane Murray | Field CTO & Head of AI Strategy | Monte Carlo
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Abstract

‍Nearly every data organization is under pressure to deploy Agentic AI initiatives, but where do they start? What do data teams actually need to know before they begin?The engineering team at Monte Carlo, a data + AI observability company, not only works with hundreds of enterprise customers to help them build reliable AI products every day, but the team has also developed their own Agentic AI applications within the product.

After a year of deploying GenAI in production, including the world's first-ever Observability Agents, the team has learned 10 key lessons that can serve as guidance for others currently in the process of or planning for their Agentic AI investment. Join Shane Murray, Field CTO & Head of AI at Monte Carlo, as he shares the takeaways his team has learned in the Agentic AI development process.

Lessons include:
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- 1: Separate AI research from product research
- 2: Employ observability into model output
- 3: Validate results with data or human interactions
- 4: Divide and aggregate small agentic tasks
- 5: Split tasks horizontally to reduce runtime
- 6: Some tasks will have to wait for better models
- 7: AI solutions can be valuable for fast development
- 8: Establish cooperative workflow between product engineers and data scientists
- 9: Use LLM clone models to keep data secure
- 10: Leverage structured output response formats

Attendees will walk away with a deeper understanding of best practices for Agentic AI product research, validations, task splitting, workflows, security, and perhaps the most difficult, knowing when to be patient.

Session Outline:

Lesson 1: Set the right foundations for the early stages of Agentic AI development. This means establishing the right research workflows and monitoring output quality from the jump. Decide how your team will validate responses.

Lesson 2: Learn how to split Agentic tasks correctly. This means dividing and aggregating small tasks, as well as splitting tasks horizontally to effectively reduce runtime.

Lesson 3. Establish a cooperative workflow between internal teams, from product engineers to data scientists, for continued development and optimization. Learn how to maintain data security with LLM clone models, as well as how to leverage structured output response formats.

I'd focus on lessons around building the Troubleshooting Agent here at Monte Carlo, including:

- The problem definition (and why this problem was suited for AI) - Getting to production / value
- How it's built – a review of the multi-agent architecture built with Claude, LangGraph
- What it does – a demo or overview of the user interaction
- How we evaluate model quality
- Tips for how to get started building agents

Attendees will walk away with a deeper understanding of best practices for product research, validations, task splitting, workflows, security, and perhaps the most difficult, knowing when to be patient when it comes to effective Agentic AI development.

Background Knowledge:

Basic understanding of data engineering, AI development, and data + AI infrastructure.

Week 3 - Scale

Theme: Production Deployment and Business Transformation

SecureYour Spot Now
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Your Agentic Future Starts Here

3-WEEK HANDS-ON VIRTUAL SUMMIT

JAN 21 - FEB 5, 2026

30+

Expert Instructors

50+

Workshops & Demos

1k+

Builders, Engineers, & Innovators

$2,500

AI Platform Credits

Build Future-Ready Agentic AI Skills

Speakers & Instructors