Building an Agentic Analytics Foundation on the Lakehouse
Databricks frames the lakehouse as the required foundation for accurate, secure agentic analytics
“Agents don't know that "revenue" in your organization means "net income," or that "active customer" has a very specific definition.”
Databricks Solutions Architect Justin Kolb Peck argues that AI agents applied to enterprise data without a proper semantic foundation produce faster but incorrect answers, identifying accuracy, access, and scaling as the three core failure modes. Their proposed solution layers the Genie ontology for business-concept semantics, MCP-based connectivity (explicitly citing Claude Code), and Lakehouse Computing for cost-efficient scale. The framing positions Databricks infrastructure as a prerequisite for reliable agentic analytics rather than an optional add-on.