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Intugle AI powers governed enterprise agents with Databricks

With Unity Catalog, Lakehouse Federation, Databricks SQL Serverless and Lakeflow Jobs, Intugle built a governed execution layer for enterprise AI across clouds and data platforms. The platform is built on a multi-agent architecture, with specialized agents responsible for metadata exploration, semantic relationship discovery, transformation generation, predictive modeling and text-to-code (SQL & Python) query generation. Using Databricks SQL Serverless improved query execution times by 50–60% while reducing startup times from several minutes with traditional compute clusters to just 3–4 seconds.

Agent or tool
Databricks
Topic
Customer Support
Reported on
Case study (Databricks)
Added
2026-11-11

Why it matters

Support is repetitive and time-sensitive. Agents can draft answers, look things up and handle simple requests, so people can focus on the tricky ones. It is one of 554 sightings filed under Customer Support on Agent Sightings (number 424 by interest), and one of 37 involving Databricks.

How you could try it

Here is a reusable prompt, written by us in our own words, that captures the task. Paste it into Databricks or any similar agent and adjust the details to your situation.

I want an agent to handle routine customer questions end to end, and pass anything unclear to a person. The tool in the source case was Databricks.

Saved

Tip: Start small: run it once on a low-stakes example, read what the agent did, and only then let it run on real work.

Source

Read the original first-hand account on Case study (Databricks): https://databricks.com/customers/intugle-ai/unity-catalog. Our summary is a paraphrase; details are in the source.

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