Use case
Use case3 min readBy The Data Workers Team

Enterprise AI context, run by agents

Every copilot and agent reads different tables and definitions, so answers disagree with reports. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Every copilot and agent reads different tables and definitions, so answers disagree with reports.
  • •What changes. Data Context Wizard serves governed definitions, lineage and policy over MCP to every AI tool in the company.
  • •What stays with your people. Changes to certified definitions: every AI tool depends on them.
  • •The result. Every AI tool answers from the same governed context.

The problem today

Every copilot and agent reads different tables and definitions, so answers disagree with reports.

What changes with Data Workers

Data Context Wizard serves governed definitions, lineage and policy over MCP to every AI tool in the company.

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 governs definitions.
  2. 2.The Data Context & Catalog agent calls trace_cross_platform_lineage and attaches provenance.
  3. 3.The Data Access & Governance agent calls check_policy and enforces access.
  4. 4.The Autonomous Data-Conductor calls verify_data and verifies answers.

The impact metrics we move

  • •New AI tool connected: from about a month to 4 h
  • •Answer with provenance: from about a day to seconds
  • •Conflicting definition reconciled: from about a month to 8 h
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 update as agents work, so context never goes stale.
  • •Every answer carries provenance, so people can check it.

Where your people stay in charge

Changes to certified definitions always go to a named approver, because every AI tool depends on them. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Governed context for 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
  • •Answer with provenance: from about a day to seconds
  • •Conflicting definition reconciled: from about a month to 8 h
  • •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 AI and automation page · The full team playbook

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