Build a Text-to-SQL AI Agent on Databricks — Live, with Cost Guardrails
Deploy db-agent on a real Databricks workspace in 45 minutes — with the data/LLM boundary explicit and the complete cost-guardrail layer installed live. Statement allowlists, query cost checks, retry ceilings, context budgets. Every token-spending decision narrated.
July 8 · 12:00 PM EDT
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Microsoft Teams link sent instantly. Replay available if you can't attend live.
What we'll build, live
Chandan Kumar
Founder, beCloudReady · Creator of open-source db-agent (AAAI-25 workshop) · 4,000+ A100/H100 GPU operations background
Why this session, why now
Bundled LLM-over-data tools — Databricks Genie and its equivalents — are convenient and opaque by design. You don't choose the model. You can't route simple questions to a cheaper one. You can't set token ceilings or retry limits. And the AI consumption arrives entangled with platform compute, so when spend climbs, you can't isolate which decision caused it.
The teams that stay in control are the ones that kept the boundary between their data infrastructure and their LLM infrastructure. This session shows you exactly what that looks like in production — built live, in 45 minutes.
Deeper context: The Token Playbook — why dev teams burn AI budgets in a week, and the two disciplines that fix it.
Who this session is for
Data engineers & analytics engineers
You run a Databricks or Snowflake lakehouse and business users want to ask it questions in plain English — but you've seen Genie's cost curve and want a version you can govern, route, and budget.
Platform & security leads
You need an LLM-over-data deployment that respects Unity Catalog, keeps the audit trail explicit at the boundary, and doesn't let an agent run arbitrary SQL against production.
Engineering managers & data leaders
Your AI-on-data line item is opaque and growing. You want LLM spend isolated from platform compute, with token ceilings enforced in architecture — not in a memo.
Engineers building on db-agent
You're evaluating or already using the open-source db-agent and want to see the production deployment patterns and cost guardrails the maintainer uses in real engagements.
For data teams
Want the full version on your own Databricks workspace?
The same build, done by your engineers on your Databricks workspace over two days — Unity Catalog scoping, the complete guardrail layer, deployed and yours to keep. This free session is the honest preview.
Talk about the private workshopCan't make it live?
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