Modernizing complex legacy code with AI agents - Mistral

The article details Mistral AI's project to modernize a challenging legacy system: a 40,000-line Fortran 77 physics-intensive reservoir simulator used by a European energy operator. This older system presented significant hurdles due to its lack of a test suite and centralized documentation. Mistral's approach was guided by three core principles. First, a "parity harness" was developed to guarantee numerical agreement between the original Fortran code and the newly migrated C++ code. This involved instrumenting the Fortran codebase to capture state snapshots and using a C++ test framework to verify correctness at various stages, thereby providing concrete proof of successful migration through numerical parity. Second, the team prioritized documentation by using AI agents to understand and catalog the existing Fortran codebase before initiating the migration. This step was deemed critical, as it's impossible to effectively migrate code that no one can comprehend. Finally, the project emphasized structured workflows that incorporated human oversight. Early attempts with fully autonomous AI agents resulted in code that merely replicated Fortran syntax in C++ rather than truly modernizing it. Mistral found that a hybrid strategy—combining AI agents for coding, testing, and reviewing with human review gates—produced high-quality, maintainable C++ code. This balanced method facilitated architectural refactoring, transforming procedural Fortran 77 into object-oriented C++. The project successfully migrated core functionalities and provided crucial insights for future large-scale legacy system modernizations.