Kevin Vicent

NVIDIA just fixed text-to-SQL’s biggest problem! the release is called Auto Ontology, and it targets the reason an agent can write valid SQL against a company database and still return the wrong number. the database stores columns, and the agent has to guess what they mean. a column called extendedprice might hold revenue, and two systems in the same company can define revenue differently. Auto Ontology gives the agent an ontology, a schema that maps business concepts to the columns that hold them and defines how they relate, before any query is written. it drafts that schema from your database metadata instead of making you write it, and a human certifies it. the ontology holds three things: → terms: business concepts like customer, order, or revenue, with descriptions and synonyms, mapped to the exact columns that store them. → SQL attributes: reviewed expressions like a margin or a rolling total, reused so every measure is computed the same way each time. → relationships: how tables join, including bridge tables, inferred from keys and the data itself. to draft these, it profiles columns, samples values, extracts terms table by table, and mines past query history for SQL attributes. everything lands in Postgres, with embeddings in pgvector. at query time, the agent maps the question to certified terms, follows relationships to find the joins, and writes SQL. it checks filter values against the real data, repairs the query if a check fails, and returns the answer with its SQL and steps. when the data can't support a question, the agent says so. in NVIDIA's demo, a question about return on ad spend on a sales database with no ad data gets a clarifying reply offering revenue instead of an invented number. so the ontology tells the agent how to compute an answer and also which questions have one at all. as the architecture in the image shows, an ingestion service compiles the ontology from your source databases, and any agent harness reaches it through MCP, CLI, or REST API. it supports ten engines including Postgres, Snowflake, Databricks, and DuckDB, runs on Nemotron by default or any OpenAI-compatible endpoint, and is Apache 2.0. check it out on GitHub: github.com/NVIDIA/auto-ontol… the same principle of defining a schema before the model extracts anything also decides whether agent memory works. I wrote about that in the article quoted below.