Your Decisions Run on Palantir. Now Make Your Data Operations Autonomous with Data Workers

Palantir changed how your company makes decisions. The Ontology turned tables into plants, orders and customers, and AIP lets people and agents act on them with real governance.

But the Ontology is only as good as the data underneath it, and most of that data doesn't live in Foundry. It lives in Snowflake, Databricks or BigQuery, is shaped by dbt and scheduled by Airflow, and reaches Foundry through virtual tables. When a Workshop app shows supply that doesn't exist, the cause is almost never in Foundry, and a data engineer has to go find it.

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 Palantir solved, and what it didn't

Palantir solved the decision layer. The Ontology, Actions and AIP are the best tools there are for running operations on business objects. What Palantir doesn't do is operate the warehouses, dbt projects and pipelines that feed it. Its agents build and act inside Foundry. That isn't a flaw. Foundry exists to be the operating system for decisions, and it does that well.

Palantir is where your decisions run. Data Workers is the agentic data platform that runs the data underneath them.

Or, in one sentence for your team: Use Palantir to run the business on the Ontology. Use Data Workers to run the data platform underneath it.

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 Palantir

Palantir leads where it should: operational apps and AI apps on business objects. We're even on business context, because Data Workers reads the Ontology directly. On running the warehouses, dbt and Airflow underneath, on cost, and on openness and price, Data Workers leads.

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

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. Palantir, 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

• •An ERP field changes its unit of measure overnight.
• •The dbt model that builds inventory positions keeps running, but mixes units.
• •Foundry reads the model through a virtual table, and a Workshop app shows supply that doesn't exist.
• •A planner is one click away from reallocating stock based on it.

Today that's a morning of Slack threads. Data Workers catches the change, fixes the dbt model, confirms the virtual table is right again before anyone acts on it, and records what happened. Palantir'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 Palantir autonomy playbook.

How it fits with Palantir

• •Foundry stays where the business runs. Nothing about the Ontology, Actions or AIP changes.
• •We read the Ontology directly through Palantir's Ontology MCP, and Palantir's agents can call ours.
• •Each warehouse's own permissions stay the lock for the platform underneath.

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 data platform lives entirely inside Foundry, with no warehouse underneath.
• •Your data engineering load is small.
• •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 teams with Foundry on a warehouse start with one of three: incidents that reach the Ontology, warehouse spend, or a planned warehouse migration. Each protects what the Ontology depends on.

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 Palantir. It covers what Palantir 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.