Satyajit Gokhale, Aiswaryaa Venugopalan, Ruchi Agarwal, Nits Jeganathan, Khurram Khawaja, Vijay Mandadi, and Luke Huan contributed to this article. AWS Transform offers a .NET application modernization service that allows enterprise customers to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL Compatible Edition. This migration reduces licensing costs and enhances scalability while utilizing an open-source ecosystem. Schema conversion tools can assist in translating DDL to PostgreSQL-compatible definitions, but correctness must be verified by teams to ensure performance under real-world workloads. Traditional testing methods, such as using production data or manual checks, are not scalable and raise privacy concerns. Therefore, there is a need for realistic synthetic data generated at scale that adheres to the migrated schema's constraints. AWS Transform for SQL Server modernization employs an AI agent to generate constraint-respecting synthetic data at scale during the migration workflow. This process involves loading the PostgreSQL schema into a live database, constructing a dependency graph for data generation, and distributing tasks across multiple sub-agents. The agent validates each generated INSERT against the live database, ensuring compliance with constraints and providing error diagnostics for any violations. The synthetic data generation workflow consists of four stages: schema analysis, establishing generation order, distributing tasks across sub-agents, and validation with error recovery. The agent extracts metadata from the PostgreSQL schema, determines a valid table generation sequence, parallelizes data generation while maintaining referential integrity, and generates and validates data row-by-row against the live database. Each successful insertion enriches the context for subsequent generations, while errors are handled through structured diagnostics and iterative regeneration. The process ensures referential integrity and preserves the generation state, allowing for recovery from interruptions without data loss.