The semantics, governance, and boundaries that define trustworthy AI
Your lakehouse centralizes storage, governance, and compute, but running agentic AI takes more.
When an autonomous agent reads a term like “top sales rep” or “year to date,” it acts on whatever definition it finds first. Get that definition wrong at machine speed, and you’re just confidently working with wrong answers at scale.
In this digital dialogue, research fellow Donald Farmer sits down with data and analytics leaders to map the foundation your lakehouse needs for accountable, production-ready AI.
You’ll discover:
Where organizations overestimate their AI readiness, and the semantic gap that trips agents up
How governed self-service widens who can shape business logic while IT keeps the full picture
Why clear boundaries let an agent act freely and stay auditable
How to scope what an agent should do vs. what it should never touch, before production
If agentic AI is a priority for your organization, the architecture underneath matters more than ever.
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