An enterprise software provider needed to modernize hundreds of legacy application screens — built over 20+ years in C#, ASP, and VBScript — to a modern C# .NET Core and ReactJS architecture. A fully manual migration would take multiple years. We built an agentic AI workflow system that performs the conversion with a high level of automation.
The client's enterprise system contains hundreds of application screens built across two decades using legacy technologies in an MVC architecture. Manual conversion of each screen to modern architecture is labor-intensive and error-prone. The business cannot halt operations during migration, and the engineering team cannot absorb the conversion workload on top of ongoing product development.
An agentic AI workflow system that converts legacy application screens to modern architecture. Rather than a single monolithic tool, the system is built as a set of coordinated AI agents — each responsible for a specific stage of the conversion process. The agents analyze the structure and logic of each legacy screen, reason about its equivalent in the modern stack, generate the corresponding code, and validate the result. The engagement runs on four parallel tracks:
1. Screen type characterization — categorizing legacy screens into reusable patterns and types. Each screen type has its own structural signature — form layouts, data bindings, navigation patterns, business logic placement. The agents learn these signatures so they can apply the right conversion strategy per category, rather than treating every screen as a one-off.
2. Agentic workflow development — building and iterating on the multi-step AI workflows that perform the actual code conversion. Each workflow chains together analysis, code generation, and validation agents. As the system encounters new screen patterns, the workflows are refined — the agents improve their accuracy over successive conversion runs.
3. Assisted conversion and validation — running conversions with human oversight at every stage. The agents produce conversion output, but a human reviewer validates correctness before anything is committed. This human-in-the-loop approach catches edge cases that the agents have not yet encountered and feeds corrections back into the workflow.
4. Knowledge transfer — comprehensive documentation, training, and enablement so the client's own engineering team can run and manage the agentic workflows independently. The explicit design goal is self-sufficiency: the client should not depend on us to operate the system long-term.
- Multi-agent system: analysis, code generation, and validation agents working in sequence
- Screen type classification for pattern-based conversion optimization
- Agents improve accuracy over successive runs as they encounter new patterns
- Human-in-the-loop validation at every conversion stage
- Full knowledge transfer program — client operates the system independently
- Converts from C#/ASP/VBScript (MVC) to C# .NET Core + ReactJS
The system demonstrated a high level of automation for representative screen types — the agents successfully analyzed legacy screens, generated equivalent modern code, and validated the output with minimal human correction. The engagement is designed from the ground up for client self-sufficiency: the agentic workflows, documentation, and training are all structured so the client's own engineering team runs conversions independently. The system is not a black box — it is a toolset the client owns and operates.