Use case
Use case3 min readBy The Data Workers Team

Grants management data, run by agents

Applications, awards and recipient reports arrive in different formats, and monitoring is a manual review. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. Applications, awards and recipient reports arrive in different formats, and monitoring is a manual review.
  • •What changes. Agents land and map recipient reports, check them against awards and flag exceptions.
  • •What stays with your people. Award and compliance decisions: they are legal determinations.
  • •The result. Grant monitoring catches issues while they can still be fixed.

The problem today

Applications, awards and recipient reports arrive in different formats, and monitoring is a manual review.

What changes with Data Workers

Agents land and map recipient reports, check them against awards and flag exceptions.

The workflow: Data Ingestion agent (get_connector_status) → Schema Evolution agent (generate_migration) → Quality Monitoring agent (run_quality_check) → Autonomous Data-Conductor (request_governance_review).
Fig. 1:The workflow: Data Ingestion agent (get_connector_status) → Schema Evolution agent (generate_migration) → Quality Monitoring agent (run_quality_check) → Autonomous Data-Conductor (request_governance_review).

The workflow, step by step

  1. 1.The Data Ingestion agent calls get_connector_status and lands recipient reports.
  2. 2.The Schema Evolution agent calls generate_migration and maps formats.
  3. 3.The Quality Monitoring agent calls run_quality_check and checks against awards.
  4. 4.The Autonomous Data-Conductor calls request_governance_review and routes exceptions.

The impact metrics we move

  • •Recipient report checked: from about a week to under 1 h
  • •Award reconciliation: from about 2 weeks to 2 h
  • •Exception routed: from about 2 days to minutes
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

  • •Recipient reports are checked on arrival, so issues surface early.
  • •Awards reconcile to spending, so monitoring is continuous.

Where your people stay in charge

Award and compliance decisions always go to a named approver, because they are legal determinations. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. One grant program's reports.
  • •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:
  • •Recipient report checked: from about a week to under 1 h
  • •Award reconciliation: from about 2 weeks to 2 h
  • •Exception routed: from about 2 days to minutes
  • •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 government hub · The full team playbook

Related Posts

Use case

Account intelligence data, run by agents

Account data is split across CRM, billing, product usage and enrichment vendors that identify accounts differently. Here is how Data Workers runs it instead.

Use case

Accruals and prepaids data, run by agents

Accrual schedules live in spreadsheets, and missed or stale accruals surface in review. Here is how Data Workers runs it instead.

Use case

Agent governance and audit, run by agents

AI agents act on data with no consistent approval, record or rollback. Here is how Data Workers runs it instead.

Use case

Agentic controllership, run by agents

Controllers spend the month chasing reconciliations, approvals and evidence across ERP, billing and spreadsheets. Here is how Data Workers runs it instead.