Building data agents enterprise can actually trust
Building Data Agents Enterprises Can Actually Trust
Private Executive AI Forum
Keynote
Jerry Xi
AI Platform Architect, Generative AI & ML Infrastructure, Oracle
Host
Murray Newlands
Founder, Open Future Forum
Panel
Jerry Xi — AI Platform Architect, Generative AI & ML Infrastructure, Oracle
Rahul Choudhry — Leader, Data Science & Machine Learning, First American Title
Everyone is building AI agents.
Far fewer are building data agents that can reliably reason over complex enterprise data—where information is fragmented, constantly changing, and often missing critical business context.
The challenge isn't choosing the right LLM. It's building everything underneath it.
Semantic layers. Knowledge graphs and ontologies. Data federation. Governance. Identity. Evaluation. Infrastructure. These are the components that determine whether an agent becomes a trusted business system or an expensive demo.
When these foundations are weak, agents produce answers that sound convincing but are wrong. In production, that is one of the most dangerous failure modes in enterprise AI.
Join Oracle AI Platform Architect Jerry Xi for an in-depth discussion on what it really takes to deploy trustworthy data agents at enterprise scale. Jerry has built AI infrastructure across Oracle, Meta, Lyft, Box, and as founder of DataTron, giving him firsthand experience designing the platforms that move machine learning from research into production.
This executive forum is designed for engineering leaders, AI architects, founders, and enterprise data teams who are moving beyond prototypes and need AI systems they can trust in production.
Discussion Topics
Why the Semantic Layer Matters
Why natural language to SQL is only part of the solution, and why semantic models, ontologies, metadata, and trusted business context become the real differentiators for enterprise AI.
Designing the Modern Data Agent Stack
Architectures for production-grade data agents, orchestration frameworks, retrieval strategies, multi-agent systems, and approaches for handling ambiguity across distributed enterprise data.
Measuring Trust and Reliability
How leading organizations evaluate AI systems, identify hallucinations, validate outputs, and build verification pipelines that create confidence before results reach executives.
Scaling Enterprise AI
Operational lessons from deploying AI at scale, including infrastructure design, lifecycle management, observability, governance, and keeping production AI systems reliable over time.
Who Should Attend
Chief AI Officers
Chief Data Officers
CTOs and VP Engineering
AI Platform and Infrastructure Leaders
Data Engineering Teams
Machine Learning Engineers
Enterprise Architects
Founders building AI and data platforms
Hosted By
Open Future Forum
HG Insights
Agentic Fabriq
About Jerry Xi
Jerry Xi is an AI Platform Architect specializing in generative AI infrastructure, large-scale machine learning systems, and production AI platforms. Throughout his career he has focused on one of the hardest problems in enterprise AI: transforming advanced models into secure, scalable, and reliable production systems.
Oracle (2024–Present)
AI Platform Architect – Generative AI & ML Infrastructure
Leads the architecture and scaling of large language model infrastructure supporting Oracle Cloud Infrastructure's Generative AI and Agent Services.
Meta (2020–2024)
AI Infrastructure
Built infrastructure supporting thousands of production machine learning models and developed AutoML platforms that streamlined model development and deployment across Meta.
DataTron (2016–2020)
Co-Founder & CEO
Founded DataTron, an MLOps platform focused on simplifying model lifecycle management, monitoring, governance, and production deployment. The company was backed by 500 Startups (Batch 18) and StartX.
Lyft (2015–2016)
Principal Engineer
Worked on real-time ETA prediction systems and large-scale machine learning infrastructure powering ride predictions.
Box (2014–2015)
Principal Software Engineer
Focused on distributed storage systems and large-scale data engineering.
Education
Shanghai Jiao Tong University
Jerry's experience sits at the intersection of distributed systems, AI infrastructure, MLOps, model lifecycle management, and production engineering. As enterprises move from experimentation to deploying autonomous AI agents, these capabilities become increasingly critical.
His perspective on scalable AI infrastructure, governance, and trustworthy enterprise systems makes this an ideal discussion for technical executives responsible for delivering AI that businesses can rely on.