The Context Gap: Why Accurate AI Needs a Semantic Layer

Building an agent is easy. Trusting one with your P&L is not.

LLMs are designed to converse, not to calculate. Without a governed source of truth, AI produces confident guesses—not reliable insights. That’s the context gap.

Closing it requires more than data access. It requires a business brain. And that’s where Tableau Semantics comes in: by providing a governed, unified understanding of your data, it ensures your agents reason in the language of your business—providing accurate, consistent, and actionable insights.

Join us for a tactical session that cuts through the AI hype to explore the architecture behind reliable, enterprise-grade agentic analytics. We’ll be joined by the team at Engine, who will share how they transformed service operations with agentic analytics. See how they built “Eva,” an employee-facing agent powered by Tableau Semantics, delivering trustworthy insights directly into the flow of work.

What we’ll cover:

• Engineering the "Business Brain": Why Tableau Semantics is the essential "translation layer" between raw data and reliable AI.
• The Blueprint for Accuracy: How to coach your agent using semantic models, eliminating guesswork and driving more accurate conversational analytics that evolve alongside your business logic.
• Real-World Results with Engine: A deep dive into how Engine built "Eva" to automate routine tasks and surface trustworthy insights directly within Slack and Salesforce.