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

Building on Snowflake simplified a lot. Your warehouse scales, your semantic views give the business consistent answers, and with CoWork and Cortex Agents your data is now something people can talk to.

But the work around the warehouse hasn't changed much. Data still arrives through Fivetran or Openflow from sources that change without warning. Transformations run in dbt. Schedules live in Airflow. The ML team has a Databricks workspace. Access requests queue up. A Data Metric Function fires at 6 a.m., and a person has to work out why, fix it, and tell everyone it's fixed.

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 Snowflake solved, and what it didn't

Snowflake solved the warehouse. Horizon governs what lives there, semantic views keep metrics consistent, and CoWork answers questions about them. What Snowflake doesn't do is operate the estate around it. Its agents act inside the account, and nothing in its stack owns fixing a problem once an alert fires. That isn't a flaw. Snowflake's agents exist to make the account the center of gravity, and they do that well.

Snowflake is your warehouse. Data Workers is the agentic data platform that runs your whole data estate, Snowflake included.

Or, in one sentence for your team: Ask CoWork about Snowflake data. Ask Data Workers about the whole estate, and hand it everything that has to be fixed, approved or paid for.

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 Snowflake

Snowflake leads where it should: answers on semantic views and AI inside SQL. We're even on metric definitions, because Data Workers reads your semantic views as the source of truth. On everything that needs fixing, approving or paying for across the estate, Data Workers leads, and on incident resolution Snowflake has no product at all.

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

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. Snowflake, 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 key column in a Postgres source changes type overnight.
• •The Airflow task that loads it fails after its retries, and nobody is paged.
• •At 6 a.m. a Data Metric Function flags the revenue table as stale.
• •By 9 a.m. finance is asking CoWork why revenue looks flat, and CoWork can explain the number but can't fix its cause.

Today that's a morning of Slack threads. Data Workers traces the alert back to the failed load, fixes and reruns it, confirms the revenue table is fresh again, and records what happened. Snowflake's 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 Snowflake autonomy playbook.

How it fits with Snowflake

• •Horizon and Snowflake RBAC stay the lock. Every change in Snowflake goes through the roles you grant. We never create a second permission system.
• •Business users ask wherever they already work. CoWork keeps answering inside Snowflake. Spellbook gives the same people one chat box across the whole estate, and Spellbook for Slack and Teams (coming soon) puts every Data Workers agent in the channels they already use.
• •Your semantic views stay the source of truth. We read them; we don't redefine them.

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:

• •Everything runs inside Snowflake, including dbt, with no other platforms in the critical path.
• •Your only need is Q&A on semantic views that never leave Snowflake, which CoWork 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 Snowflake teams start with one of three: access requests, credit spend, or turning Data Metric Function alerts into fixed incidents. 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. See pricing for how the pilot works.

If your architects want the detail, send them Data Workers on Snowflake. It covers what Snowflake 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.