A pattern for giving AI agents reliable access to data warehouses by using a curated semantic layer (Databricks Genie) instead of raw text-to-SQL, improving accuracy and governance. The agent calls Genie's Conversation API as a tool, receiving both natural-language responses and exact SQL.
One of the harder problems I keep running into when building agents is letting them answer questions over a structured data warehouse. Naive text-to-SQL against raw tables is fragile: the model guesses join keys, invents column meanings, and gives confident wrong numbers. The pattern that's worked better for me is to put a curated semantic layer between the agent and the tables, and let the agent call that as a tool instead of writing SQL from scratch. The version of this I've been using is Databricks Genie. The idea is you build a "Genie Space" scoped to a specific set of tables and curate it with example SQL queries, reusable SQL expressions for business definitions, and certified/trusted functions for things like a standard tax or revenue calc. That curation is the whole point: when the agent asks something, Genie matches the question against the verified examples and definitions rather than free-styling, so it stays governed by Unity Catalog permissions and is a lot more consistent than pointing a model at raw schemas. Mechanically, your agent just calls the Genie Conversation API as a tool. You POST a natural-language question to /api/2.0/genie/spaces/{space_id}/start-conversation, then poll the message endpoint until status is COMPLETED. The response comes back as attachments containing the natural-language text, the actual SQL Genie generated, and an attachment_id you use to fetch the result set from the query-result endpoint. So your agent gets both the rows it can reason over and the exact SQL, which is great for transparency and letting a user verify the query. It's stateful too, so the agent can ask follow-ups in the same conversation. There's also an "Agent mode" (formerly Research Agent) for more complex questions, where it builds a research plan, runs multiple queries, and returns a report with citations instead of a single answer. Curious how others are solving this. Are you giving agents a curated semantic layer like this, exposing read-only SQL with guardrails, or doing something else entirely to keep structured-data answers trustworthy?
Discusses the evolution from text-to-SQL to autonomous data agents, comparing custom-built agents using LangGraph with managed platforms like Snowflake Cortex Analyst, Databricks Genie, and PowerBI Copilot.
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This paper presents a semantic-layer-mediated NL2SQL agent that decouples intent from physical execution by reasoning over a curated semantic model, achieving 94.15% execution accuracy on the Spider2-snow benchmark.
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