Use case
Use case3 min readBy The Data Workers Team

Sovereign context you own, run by agents

Business logic and definitions end up inside vendor platforms, so switching tools or models means starting over. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Business logic and definitions end up inside vendor platforms, so switching tools or models means starting over.
  • •What changes. Your definitions, lineage and policies live in a context layer on your own infrastructure, served to any model or tool over MCP.
  • •What stays with your people. Changes to certified definitions: they are business IP.
  • •The result. Swap models or tools without losing what your business means.

The problem today

Business logic and definitions end up inside vendor platforms, so switching tools or models means starting over.

What changes with Data Workers

Your definitions, lineage and policies live in a context layer on your own infrastructure, served to any model or tool over MCP.

The workflow: Data Context & Catalog agent (define_business_rule) → Data Context & Catalog agent (trace_cross_platform_lineage) → Data Access & Governance agent (check_policy) → Autonomous Data-Conductor (verify_data).
Fig. 1:The workflow: Data Context & Catalog agent (define_business_rule) → Data Context & Catalog agent (trace_cross_platform_lineage) → Data Access & Governance agent (check_policy) → Autonomous Data-Conductor (verify_data).

The workflow, step by step

  1. 1.The Data Context & Catalog agent calls define_business_rule and keeps definitions as your IP.
  2. 2.The Data Context & Catalog agent calls trace_cross_platform_lineage and keeps lineage in your environment.
  3. 3.The Data Access & Governance agent calls check_policy and enforces your policies.
  4. 4.The Autonomous Data-Conductor calls verify_data and verifies answers before use.

The impact metrics we move

  • •New AI tool connected: from about a month to 4 h
  • •Definition question: from about a day to seconds
  • •Policy applied: from about a week to 5 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

  • •Definitions and lineage stay in your infrastructure, so no vendor holds your logic.
  • •Any model reads them over MCP, so you choose the model.

Where your people stay in charge

Changes to certified definitions always go to a named approver, because they are business IP. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. One domain's definitions served to two AI tools.
  • •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 AI tool connected: from about a month to 4 h
  • •Definition question: from about a day to seconds
  • •Policy applied: from about a week to 5 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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