Use case
Use case3 min readBy The Data Workers Team

Model-agnostic deployment, run by agents

Logic built for one model or vendor has to be rebuilt when the company switches. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Logic built for one model or vendor has to be rebuilt when the company switches.
  • •What changes. Agents run in your infrastructure with your model key, and definitions live in your context layer, so models can change without rework.
  • •What stays with your people. Model and vendor choices: the platform team decides.
  • •The result. Switch models without rebuilding anything.

The problem today

Logic built for one model or vendor has to be rebuilt when the company switches.

What changes with Data Workers

Agents run in your infrastructure with your model key, and definitions live in your context layer, so models can change without rework.

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

The workflow, step by step

  1. 1.The Data Context & Catalog agent calls define_business_rule and keeps definitions model-independent.
  2. 2.The Data Access & Governance agent calls check_policy and enforces the same policies for any model.
  3. 3.The Autonomous Data-Conductor calls verify_data and verifies answers after a model change.
  4. 4.The Data Access & Governance agent calls generate_audit_report and records which model acted.

The impact metrics we move

  • •Model switched: from about 3 months to 8 h
  • •Answers verified after switch: from about a week to ~1 h
  • •Policy applied to a new model: 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 are model-independent, so no rework on a switch.
  • •Runs on your model key, so costs and data stay yours.

Where your people stay in charge

Model and vendor choices always go to a named approver, because the platform team decides. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Run one workflow on two models.
  • •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:
  • •Model switched: from about 3 months to 8 h
  • •Answers verified after switch: from about a week to ~1 h
  • •Policy applied to a new model: 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 AI and automation page · The full team playbook

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