Use case
Use case3 min readBy The Data Workers Team

Internal data products and data apps, run by agents

Every team asks for its own dataset, dashboard or app, and each one becomes something the platform team maintains forever. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Every team asks for its own dataset, dashboard or app, and each one becomes something the platform team maintains forever.
  • •What changes. Agents build internal data products as missions, register them with owners and SLAs, and keep them tested and documented.
  • •What stays with your people. Products shared outside the team: others depend on them.
  • •The result. New data products ship in days and maintain themselves.

The problem today

Every team asks for its own dataset, dashboard or app, and each one becomes something the platform team maintains forever.

What changes with Data Workers

Agents build internal data products as missions, register them with owners and SLAs, and keep them tested and documented.

The workflow: Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (create_quality_tests_for_pipeline) → Data Context & Catalog agent (generate_documentation) → Quality Monitoring agent (set_sla).
Fig. 1:The workflow: Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (create_quality_tests_for_pipeline) → Data Context & Catalog agent (generate_documentation) → Quality Monitoring agent (set_sla).

The workflow, step by step

  1. 1.The Pipeline Building agent calls generate_pipeline and builds the data product.
  2. 2.The Quality Monitoring agent calls create_quality_tests_for_pipeline and adds tests.
  3. 3.The Data Context & Catalog agent calls generate_documentation and registers and documents it.
  4. 4.The Quality Monitoring agent calls set_sla and sets freshness.

The impact metrics we move

  • •New data product: from about a month to 2 days
  • •Product registered: from about a week to ~1 h
  • •Freshness issue fixed: from about 8 hours to 15-20 min
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •Every product gets tests, an owner and an SLA, so nothing ships unowned.
  • •Upkeep runs on its own, so the platform team stops being a help desk.

Where your people stay in charge

Products shared outside the team always go to a named approver, because others depend on them. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Two internal data products built as missions.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •New data product: from about a month to 2 days
  • •Product registered: from about a week to ~1 h
  • •Freshness issue fixed: from about 8 hours to 15-20 min
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The data and IT page · The full team playbook

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