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Building data agents enterprise can actually trust
Your data agent is confident. Is it right?
Everyone is building agents. Far fewer are building data agents that can reliably reason over real enterprise data - with all its messiness, missing context, & scale - & give you a way to know when they're wrong before your stakeholders do.
The hardest part of a data agent isn't the LLM. It's everything underneath: the semantic layer, the ontology, the data federation logic, & the infrastructure to serve it reliably. Get that wrong & your agent returns confident wrong answers. And because it sounds authoritative, those wrong answers are more dangerous than no answers at all.
Join us for an evening with Jerry Xu, AI Infrastructure Architect at Oracle, who has spent his career building the systems that put models into production at Meta, Lyft, Box, & the company he founded, DataTron.
This is a working session for technical leaders, founders, & data/AI platform teams who are past the demo stage & now have to make data agents dependable.
What Jerry will cover:
eal enterprise schemas, & why ontologies, federation logic, & certified data context are where the hard engineering actually lives
Building the data agent stack - agent architecture, orchestration patterns, multi-agent coordination, & how to handle ambiguity when querying live, messy enterprise data
How do you know when to trust the answer? - why standard benchmarks miss the failure modes that matter in production, what a real grading pipeline looks like, & how to design for verifiability from day one
What breaks at scale - failure modes no one talks about until it's already happened, & why long-term reliability requires treating models as living systems
Who should come: Engineering & AI leaders, data platform & infrastructure teams, founders building data products, & operators responsible for making AI reliable in production.
Hosts include
Open Future Forum
HG insights
Agentic Fabriq
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