Across industries, enterprises are rolling out conversational analytics, internal GPTs, and AI assistants—often called Talk to Data or Text-to-SQL. These experiences work well in early demos, but many stall as teams try to scale them beyond small user groups.
The blockers are consistent: rising query costs, inconsistent answers, unclear data access rules, and AI systems reasoning directly over raw tables.
In this joint webinar, AtScale and Databricks show how enterprises use Databricks Lakehouse platform and the MCP Marketplace to move from conversational BI to production-grade AI agents grounded in governed business metrics.
You’ll see how:
- Agents and Genie-style experiences inherit trusted metrics, relationships, and access policies from a semantic layer
- AI interactions are routed through governed aggregations, not base tables—keeping performance fast and costs predictable
- AtScale’s MCP enables standardized, auditable access between agents and analytics infrastructure
- Teams deploy both conversational assistants and headless agents using Databricks Agent Bricks and data pipelines
What You’ll Learn
- How the Databricks MCP Marketplace enables secure, standardized agent connectivity across data, tools, and semantics
- Why governed semantic metrics are the missing layer between Lakehouse data and enterprise-ready AI agents
- How Databricks customers evolve from chat-driven analytics to autonomous, headless agents with real business ROI
If you’re building conversational analytics, evaluating agent workflows, or deploying AI on Databricks Lakehouse platform, this webinar offers a practical blueprint for scaling AI without sacrificing trust, performance, or cost control.
AtScale’s MCP Server is available now in the Databricks MCP Marketplace, enabling Databricks customers to connect governed semantic models to Genie, Agent Bricks, and AI pipelines in minutes.
