Mistral Reveals AI Agent Workflow for Migrating 40,000 Lines of Fortran 77 to C++

Mistral has published a case study on migrating 40,000 lines of Fortran 77 code to C++ for a European energy operator. The project targeted a physics-intensive reservoir simulator, dealing with a complex legacy environment that lacked a test suite and central documentation.

Migration Complexity and Preparation

Fortran 77 differs fundamentally in architecture from modern C++, lacking modules, namespaces, and structured types, and storing state in global memory via COMMON blocks. This required a complete system refactoring rather than simple syntax translation, making the task highly difficult. Before migration, Mistral built a parity harness to verify numerical equivalence. This tool dumped state snapshots from the Fortran code and generated reference checkpoints using a C++ testing framework, serving as a critical tool to prove the correctness of the code generated by the agents.

Evolution of Agent Utilization Strategy

Initial attempts with fully autonomous agents merely retyped Fortran code into C++ syntax, failing to achieve true modernization. A second attempt, which divided roles among planner, coder, and tester agents, improved quality but encountered issues where agents stalled on complex sources. Ultimately, a hybrid approach was adopted where humans operate the workflow and agents work on a module-by-module basis. This method balanced autonomy and control by including human review gates.

Key Lessons and Results

In the first sprint, 40,000 lines out of a total of 300,000 were successfully migrated. Mistral emphasized three points for large-scale code modernization. First, a parity harness must be built before writing code to ensure numerical equivalence. Second, scattered documentation should be integrated into the code to improve readability before deploying agents. Third, structured workflows with human review gates are more effective than full autonomy for large-scale tasks.