A practitioner's guide to deploying a text-to-SQL agent in a real-world business environment. The talk covers the critical lessons learned in moving from concept to production, focusing on the importance of the communication channel (Slack), the necessity of a semantic layer over benchmark scores, and a pragmatic approach to system architecture, testing, and evaluation.
David Jayatillake, VP of AI at Cube.dev, discusses the critical role of a headless, open-source semantic layer in the modern data stack. He argues against proprietary, BI-tool-specific semantic layers that create vendor lock-in and advocates for a decoupled approach. The conversation explores how AI agents can automate the entire data pipeline—from ingestion and transformation to generating and querying the semantic layer—and compares the functionalities of semantic layers and feature stores, highlighting the crucial difference of temporality.
Nick Schrock, CTO of Dagster, introduces Compass, a Slack-native tool for collaborative, exploratory data analysis, and discusses the rising importance of 'context engineering' as the new data pipeline in the AI era.
Yoni Leitersdorf, CEO of Solid, explains that directly applying Large Language Models (LLMs) to databases for text-to-SQL fails due to a lack of business context. He introduces the concept of a semantic layer as a critical "Rosetta Stone" that translates raw data into a meaningful format AI can understand, enabling reliable and accurate data interaction.
Simba Khadder explains that the primary bottleneck for LLM agents is not intelligence, but access to structured data. He introduces EnrichMCP, an open-source framework that creates a semantic layer over data models, enabling agents to discover, reason about, and query data sources like SQL databases effectively, moving beyond the limitations of RAG and direct API conversions.