You Built on Databricks. Now Make It Agentic & Autonomous with Data Workers

You made a big bet on Databricks, and it was probably the right one. Your lakehouse is faster, better governed and more capable than what came before it, and with Genie people can ask it questions in plain English.

But look at how the work actually gets done. Pipelines still run through dbt. Some schedules still live in Airflow. A Snowflake account still feeds finance. Source systems change without warning, and dashboards live in Tableau or Power BI. When something breaks, or someone needs access, or the bill jumps, a platform engineer opens a ticket, checks four tools, pings three people in Slack and fixes it by hand.

Your data platform has a control-plane problem, and the control plane is your team. People are the connective tissue between excellent tools that don't talk to each other, carrying context from one to the next by hand. The expensive part isn't knowing what needs doing. It's the doing: the building, the changing, the checking, the fixing.

What Databricks solved, and what it didn't

Databricks solved the lakehouse. Unity Catalog governs what lives there, and Genie answers questions about it in Slack and Teams. Databricks' agents can increasingly reason across your wider estate. What they don't do is operate it: the fixes, approvals and changes that happen in dbt, Airflow, Snowflake and your BI layer stay with your team. That isn't a flaw. Databricks' agents exist to make Databricks the center of gravity, and they do that well.

Databricks is your lakehouse. Data Workers is the agentic data platform that runs your whole data estate, Databricks included.

Or, in one sentence for your team: Use Genie for questions about Databricks data. Use Data Workers for everything that has to change outside the workspace and still be true.

What goes on autopilot

This is the part that changes your week. Each of these is a queue your platform team runs by hand today.

None of this requires replacing anything. Data Workers works inside the tools you already run, reasons across all of them, and leaves an auditable record of every change it makes. Your data stays in your infrastructure.

What changes for your organization

Here's what that looks like on a normal week.

• •Your platform team stops being the human control plane. The queues that used to wait for a platform engineer (access, schema changes, incidents, cleanup, catalog upkeep) move without them. Engineers spend their time on the work only they can do.
• •Incidents stop becoming meetings. A broken number is traced to its cause in whatever system it started in, fixed there and checked downstream, and the record is there when someone asks what happened.
• •Spend is managed continuously, not quarterly. Waste is found and cleaned up as it appears, across every platform you pay for, not only the one you standardized on.
• •Audits get easier. Every change has a receipt: who or what made it, why, what it touched, and how to undo it. Evidence builds up as a side effect of the work.
• •Big programs get smaller. Migrations that normally need a dedicated team become a series of approvals.
• •Everyone works from one shared understanding of your data. Definitions, owners and lineage across every platform live in one governed place, and nothing becomes official until a person approves it.

Side by side with Databricks

Databricks leads where it should: self-serve answers and tools for building your own agents. We're even on context, because Data Workers reads everything Unity Catalog and Genie know. Everywhere the work crosses the workspace edge, from definitions that agree across platforms to incidents, migrations and spend, Data Workers leads.

The reasoning behind every score, and the full technical comparison, is in Data Workers on Databricks.

Why teams choose Data Workers

• •It's an agentic data platform, not another tool. Most AI in data today is a copilot that helps one person do one task faster. Data Workers does the work: 20+ specialist agents that detect, fix, verify and remember across your whole estate.
• •It works where your team already works. People ask, delegate and approve from the coding agent they already use, whether that's Claude Code, Codex or Cursor. Spellbook is where you look: every asset, every change and every receipt.
• •It works with everything you already run. Databricks, your other platforms, dbt, Airflow and your BI tools stay exactly where they are. Nothing is migrated to get started.
• •It's governed from day one. Every change is scoped before it runs, approved at the level you set, recorded with a receipt and reversible in one click. No agent can approve its own work.
• •It gets better every time. Every fix is written back to one shared understanding of your data, so the next incident is caught earlier and fixed faster.
• •It's priced for ambition, not usage. Unlimited seats, no usage meter, and no markup on model spend. So more autonomy doesn't mean a bigger bill.
• •You're never locked in. The core is open source under Apache 2.0, and there's no notice period or exit fee.

One incident, start to finish

• •A source system renames a field overnight.
• •The dbt model that reads it keeps running, but writes blanks.
• •A Databricks job copies the result into the table your churn model trains on.
• •By 9 a.m. the CFO's revenue dashboard is down, and Genie can explain the drop but can't fix its cause.

Today that's a morning of Slack threads. Data Workers catches the rename before the dbt run, proposes the fix to the dbt model, confirms the Databricks table and the dashboard are right again, and records what happened. Databricks' tools explain the symptom. Data Workers closes the ticket.

You choose how far and how fast

Our thesis is that data teams will climb from people working alongside a coding agent, to people governing a team of agents, to a largely self-running agentic enterprise. You don't have to jump. You choose the altitude, one area at a time.

Every area starts with agents watching and explaining. Then they propose changes for your team to approve. Then they make reversible changes on their own and leave a receipt. Only areas that have earned it run fully on their own, and any area can be dialled back at any time. No agent can approve its own work.

The detailed, stage-by-stage version is in our Databricks autonomy playbook.

How it fits with Databricks

• •Unity Catalog stays the lock. Every change on Databricks goes through UC grants. We never create a second permission system.
• •Genie stays the front door for questions. Business users keep asking Genie. Data Workers runs the back office behind it.
• •Your agents can use ours. Teams building on Agent Bricks can call Data Workers' agents as tools.

Nothing is migrated, and no data is copied out of your platforms. Data Workers stores metadata and scrubbed facts about your data, not your tables.

When you don't need Data Workers

Be honest with yourself about these:

• •Your entire estate is one Databricks account, with dbt and orchestration running inside it.
• •Your main need is business Q&A, which Genie handles well.
• •Your platform team isn't the bottleneck.

If all three are true, keep your budget. If any of them isn't, the rest of this guide is about you.

Where to start

Pick one area where your team is the bottleneck. Most Databricks teams start with one of three: access requests, cost cleanup, or the move off the Hive metastore into Unity Catalog. Each is high-volume, rule-bound and easy to measure.

Start with a pilot. A forward-deployed engineer connects your estate and runs the first areas alongside your team, so you see results on your own data before you commit. Book a demo, join the pilot program or see pricing.

If your architects want the detail, send them Data Workers on Databricks. It covers what Databricks does, what Data Workers adds, and the four products that do the work: Data Context Wizard, the Data-Agents Swarm, the Autonomous Data-Conductor and Spellbook. The thinking behind them is in our thesis.