Enterprises want their operational data on a modern platform so they can run AI applications and agents on live data. Most of that data still sits in Oracle, SQL Server, and DB2, with decades of applications built around it.

Moving the data itself is well understood, and mature tooling exists for it. The years go into the code wired to the data: stored procedures, hand written SQL, ORM layers (the code that maps application objects to database tables), and business logic that nobody has touched since the original team left.

The rewrite lands on the same engineers who own the product roadmap. So
enterprises modernize one application at a time, or the program stalls with the data ready and the application still running on the old database.

Autonomous AI agents take on the rewrite itself. An agent reads the codebase, plans the work, rewrites the data access layer, runs the tests, and opens pull requests. One engineer can run dozens of these agents in parallel and spend their own time reviewing the work and making the design calls.

Devin for MongoDB Modernizations pairs that agent with deterministic data tooling, so the code and the data move as one program. Devin rewrites the application, and MongoDB's Application Modernization Platform (AMP) designs the document model, moves the data into Atlas, and validates it.

Start with one application and a copy of its data. The results from that first application give the team real numbers to plan the rest of the estate.