Modernizing complex legacy code with AI agents.

By Carlo Antonio Patti & Rasul Alakbarli

Legacy code modernization is challenging, especially when migrating complex Fortran 77 systems to modern C++. Mistral successfully migrated 40,000 lines of a physics-intensive reservoir simulator by building a parity harness for numerical verification, documenting the codebase with AI agents, and using structured workflows with human oversight to ensure high-quality, maintainable code. Key lessons include prioritizing numerical agreement, organizing documentation before migration, and balancing agent autonomy with human review.

Legacy scientific codebases accumulate over decades, and when original authors leave, the knowledge embedded in the code becomes hard to recover. Moreover, using languages with no active developer ecosystem means missing out on the opportunity to build on top of others’ work. Mistral helped a European energy operator migrate 40,000 lines of Fortran 77 to C++, a physics-intensive reservoir simulator with no test suite and no centralized documentation.

Moving beyond code translation in legacy code modernization.

Translating syntax from one language to another is a largely solved task. Asking any recent model to translate a snippet from a non-completely-obscure language to another, will likely converge to an acceptable outcome in few iterations. However, migrating a full system from a procedural language to object-oriented C++ creates the need for architectural refactors that make the task non-trivial.

Fortran 77 was standardized in 1977, as the name may suggest, and code written in it reflects those constraints directly: no modules, no namespaces, no structured types. State lives in COMMON blocks—global memory shared across the entire program. Variables are implicitly typed by their first letter, so a misspelled name silently creates a new variable instead of raising a compiler error.