Convert embedded database queries and verify a subscription-platform migration

What the source reports

An AWS account coauthored by a New York Times engineer describes migrating PLATO from Oracle to Aurora PostgreSQL. Schema-conversion tools handled database objects; the Mactores agent converted embedded SQL in application code and iterated against test failures. NYT engineers owned the migration design, infrastructure, quality assurance, rollback plan and final production sign-off. Before cutover, the team mirrored production traffic and ran shadow queries behind feature flags while Oracle remained the system of record.

Reported technology: AWS and Mactores; Aedeon Agent with Claude on Amazon Bedrock. Scope and outcomes are source-reported.

What the enterprise environment would need

The following is a proposed project design for an enterprise doing similar work. Published details are marked; the remaining systems, data and scenarios would be agreed for your project.

Systems and evidence

Proposed: provide isolated source and target databases, a small fictional subscription application and versioned renewal, cancellation and refund records.

People and agents

Proposed: an agent suggests query conversions and test cases. Engineers independently check behavior, investigate mismatches and retain cutover authority.

State and permissions

Proposed: preserve original queries, converted revisions and replayable transaction fixtures. Keep live billing and production deployment outside the practice environment.

A proposed simulation

An illustrative practice design derived from this work, not an executed assessment.

Proposed: convert a fictional subscription workflow to a new database and prepare a traceable behavior-comparison report for engineering review.

Review gate: An independent engineer can reproduce matching outcomes and explain every exception before separately approving a cutover rehearsal.

Challenge the result: A converted refund query passes syntax checks but changes decimal rounding. The review must detect the discrepancy and withhold acceptance.

What needs checking

• Proposed: compare source and target results with independently prepared expected outcomes, including decimals, dates, nulls and transaction failures.
• Proposed: detect duplicate charges, missing refunds and differences that a syntax-only check would miss.
• Proposed: rehearse a failed cutover in isolation and confirm that recovery preserves transaction identity and ordering.

Review environment fidelity, human capability and agent performance separately. The enterprise’s accountable owners decide acceptance of the shared workflow and any operating use.

Shape this around your enterprise

Bring a similar workflow and the people responsible for it. Together, define the records and systems to use, the result your team will build or configure, and how its owners will check it. Nuvepro scopes the simulation, environment and preparation. Agree any existing-agent evaluation, implementation assistance and production responsibilities separately.

Sources and limitations

Curation review: 27 October 2026. This is the date this source example was reviewed, separate from source publication dates.

• The account is published by AWS and coauthored by a customer engineer; it is not an independent outcome audit.
• The reported schedule compares completion with an initial estimate, not a controlled measure of the contribution of AI. No schedule reduction is adopted here as an acceptance target.
• The proposed environment and checks are illustrative designs, not an executed Nuvepro migration or a replica of NYT systems.

1. The New York Times cut Oracle migration time from 18 to 5 monthsSource publication: 2026-10-26