•    Assessing the technologies defining AI Infrastructure in 2026
•    Monoliths vs Modular: rethinking AI Architecture with real-world trade-offs
•    Explore CPU vs GPU shifts and the re-architecture of cloud contracts
•    The rise of AI-defined networking, storage, and resource orchestration
•    How hyperscalers are countering neoclouds 
•    How energy constraints and sustainability now directly influence workload placement
•    What enterprises must build today to prepare for self-optimizing, agent-driven infrastructure in the next 24 months

Deepak Sachdeva

Chief Information Officer
United States Air Force

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•    Assessing how to evaluate readiness for AI workloads
•    Understanding the importance of a mindset shift beyond the technology
•    Designing data flow topologies
•    Embed real-time intelligence across applications
•    Ensuring resilience and observability throughout the stack
•    Optimizing distributed compute and integrating emerging AI accelerators. 
•    Practical lessons learned from managing model lifecycle sprawl

Rameshwar Balanagu

Chief Enterprise Architect
UNFI

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•    The essential protocols and standards required for agent-to-agent communication and interoperability
•    How open ecosystems accelerate innovation and reduce fragmentation in AI systems and what it means for the future of agentic AI
•    The governance, security, and compliance frameworks needed for enterprise-grade multi-agent orchestration
•    Our vision for a trusted, interoperable, multi-cloud agent ecosystem that empowers organizations to scale intelligently

Ranjan Sinha Ph.D.

IBM Fellow and CTO Watsonx, Enterprise AI and Data
IBM

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•    Determining organizational readiness: infrastructure, data, and orchestration capabilities required to support multi-agent systems at scale
•    Where today’s agentic tooling falls short and what must evolve for true autonomy
•    Governance, risk, and human-in-the-loop considerations for safe autonomous collaboration
•    Communication, coordination, and conflict resolution strategies among agents 
•    Key design trade-offs: centralized vs. decentralized control 
•    Practical lessons from real-world implementations
•    Tips for charting a roadmap from single agents to fully coordinated, enterprise-grade agent ecosystems

David Lloyd

Chief AI Officer
Dayforce

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Mathew Paruthickal

Global Head, Data Architecture, Utilization, & AI Engineering
Opella US (a Sanofi company)

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Jyoti Chawla

CTO & Chief Architect CX Americas
Cisco

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1.ROUNDTABLE: Innovate Boldly, Govern Carefully: Navigating Vendor Risk in Agentic AI 

Led by Neil Aronson, Global Head and Senior Director Strategic Sourcing & Vendor Risk Management, Coinbase  

2. ROUNDTABLE: Evolving Team Structures for the Age of Agentic AI 
Led by Wendy Zhang, Head of Applied & Agentic AI and Executive Director, Genentech

3. ROUNDTABLE: Trust at Scale: The Human Architecture Behind Enterprise Agentic AI

Led by Gopal Renganathan, Senior Director, Data & Analytics, Compass International Holdings

This roundtable explores the full lifecycle of human oversight in agentic AI systems from initial training and evaluation to real-time monitoring, correction, and retraining to measurement of human judgement. We’ll assess oversight models by risk profile, organizational readiness of roles, tools and thresholds for human intervention, as well as impact of human oversight on enterprise confidence, compliance and time to value. Join other practitioners discussing regulatory and audit expectations, and alignment of enterprise governance with standards and risk frameworks. Walk away with practical insights on how to quantify human error risk and design of oversight workflows to minimize it.

4. ROUNDTABLE: Straightening Out Data Foundations to Unlock AI’s Full Potential 

Other Topics may include:

1.    Mastering Multi-Agent Orchestration: Scaling Agentic AI Across the Enterprise

2.    Recalibrating Cloud Strategy for the AI Workload Era

3.    Discussing the Good, Bad, and the Ugly: Assessing the Latest Tools to Build Agentic AI Capabilities 

Neil Aronson

Global Head and Senior Director Strategic Sourcing & Vendor Risk
Coinbase

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Wendy Zhang

Head of Applied & Agentic AI and Executive Director
Genentech

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Gopal Renganathan

Senior Director, Data & Analytics
Compass International Holdings

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• Building risk management for AI agents 

• Establishing infrastructure to support responsible work from AI agents 

• Implementing testing and monitoring practices 

• Employing AI agents in contexts with human oversight

David Lloyd

Chief AI Officer
Dayforce

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In the age of AI-optimized infrastructure, hybrid clusters, heterogeneous accelerators, autonomous scaling layers, and agent-driven operations, traditional observability is no longer sufficient. This workshop brings together technology leaders to discuss how observability capabilities must evolve to keep pace with increasingly dynamic, self-optimizing systems. Participants will explore emerging requirements such as real-time insight into model and agent behavior, telemetry for GPU and accelerator utilization, cross-layer correlation between data pipelines and inference workloads, and governance-ready auditability. The conversation will focus on what new visibility, instrumentation, and automation are required to maintain reliability, efficiency, and trust in AI-first infrastructure, what metrics to track and how organizations can prepare their observability strategies now to stay ahead of rapidly shifting demands.In the age of AI-optimized infrastructure, hybrid clusters, heterogeneous accelerators, autonomous scaling layers, and agent-driven operations, traditional observability is no longer sufficient. This workshop brings together technology leaders to discuss how observability capabilities must evolve to keep pace with increasingly dynamic, self-optimizing systems. Participants will explore emerging requirements such as real-time insight into model and agent behavior, telemetry for GPU and accelerator utilization, cross-layer correlation between data pipelines and inference workloads, and governance-ready auditability. The conversation will focus on what new visibility, instrumentation, and automation are required to maintain reliability, efficiency, and trust in AI-first infrastructure, what metrics to track and how organizations can prepare their observability strategies now to stay ahead of rapidly shifting demands.