Migration Planning
Agentic Legacy Migration: Recover knowledge, modernise with precision
In many legacy systems, business logic is difficult to access. It sits in old code, data flows, and the knowledge of a few system experts. AI can make business logic and dependencies visible again. A presumed full migration then becomes a decision about what stays, what gets replaced, and what truly needs to be rebuilt.
Understand the business logic before choosing technology
Many enterprises depend on critical systems that are only partly understood. This applies to mainframes, old Java and .NET applications, proprietary platforms, accumulated database logic, and poorly documented integrations. Every change begins with technical archaeology.
COBOL-to-Java or mainframe-to-cloud already selects a technical treatment. The enterprise may not yet know which business process it must preserve, which old behaviour can disappear, or which part would be better replaced by standard software. Without that knowledge, it translates the estate before deciding what belongs in the future.
Agents recover business logic and dependencies
Microsoft's Legacy Modernization Agents show, through a COBOL example, how AI can inspect code, follow dependencies, and document candidate business logic. An AI wiki such as DeepWiki-Open then makes the results searchable, links explanations to code, and lets teams ask questions about the system.
The resulting knowledge base connects business logic and dependencies to the code that implements them. Before changing anything, teams can find affected processes and interfaces, derive requirements from observed behaviour, and train new staff without relying solely on oral history.[1][2][3][4]
One system, several migration paths
The established R frameworks from AWS and Microsoft assign each application a path such as retain, replatform, replace, rebuild, or retire. In accumulated systems, that decision often needs a finer boundary. Individual business processes such as ordering, billing, or archiving can take different paths together with their code, data, and interfaces.
One example is a mainframe that has not yet been replaced. Retain then means continuing a well-understood part for a limited period while teams remove dependencies and prepare its successor. Agentic Legacy Migration can reduce the scope of the rebuild. It does not replace a binding exit plan for a platform that is no longer viable in the long term.[5][6]
System knowledge becomes migration packages
One business process with its code, data, and interfaces
Continue for a limited period
Move the technology
Adopt standard software
Rebuild deliberately
Shut down and archive
Where to migrate? Bet on the web!
Target stacks tend to provoke arguments of faith. Long-term planning poses a more practical question: how many languages, databases, and operating models should the enterprise fund for years? One approach is to make TypeScript and PostgreSQL the default for new business applications. Both cover common needs and have large ecosystems. This reduces the number of technologies teams must master and the enterprise must secure and operate.
TypeScript can be used to develop web, mobile, and desktop applications as well as server-side logic. PostgreSQL is primarily a database. Its extension ecosystem also makes it a platform for search, queues, event streams, vectors, and jobs in many applications. This often removes separate message brokers or vector databases. Specialised services only make sense when requirements exceed these capabilities, which is unusual for most business applications.[7][8][9][10]
Agentic Legacy Migration clarifies scope, functions, and risk
For each migration package, Agentic Legacy Migration records the chosen path: continue temporarily, move to another platform, replace with standard software, rebuild, or retire. It identifies the affected components and data, the functions to preserve, and the unresolved risks.
Because each migration package documents its functions, dependencies, and risks, the team can define concrete acceptance criteria and stopping points. This makes the scope, cost, and risk of modernisation predictable earlier.