On September 9, 2026, Mistral published a case study describing how its Applied AI team helped a European energy operator migrate 40,000 lines of Fortran 77 reservoir-simulation code to modern C++. The project covered the first sprint of a 300,000-line codebase and focused on a physics-intensive simulator that had no test suite and no centralized documentation.
The Fortran 77 code relied on COMMON blocks for global state, implicit typing by first letter, and six-character variable-name limits — constraints that make line-for-line translation impractical when the target is object-oriented C++ integrated with frameworks such as PETSc. Mistral's team found that syntax translation alone produced "Fortran retyped in C++ syntax" rather than modernized code, so they built a structured workflow combining AI agents with human review gates.
Confirmed
- Scope: 40,000 of 300,000 lines migrated in the first sprint; the codebase was self-contained and runnable.
- Parity harness: Before migration, the team instrumented the Fortran code to dump state snapshots (e.g., a RHOG value of 42.71834 in one run) and built a C++ test framework to verify numerical equality of final results and critical intermediate points flagged by reservoir engineers.
- Documentation pass: A custom parser generated a caller-callee tree; over 100 agents (via Vibe CLI and Mistral OCR) documented each node from leaf to root, pulling in scattered PDFs and inline comments, with a reviewer agent on a cron schedule approving PRs.
- Structured workflow: Independent modules (< ~10,000 Fortran lines each) were identified from the call tree. For each module: generate target C++ architecture → reservoir-engineer review → break into task queue → run plan/implement/test sub-workflow per task → human reviews PRs until merge.
- Team composition: Mistral's Applied AI team (full-stack engineers building domain-specific solutions on Mistral models and Enterprise platform).
Unknown
- Wall-clock duration of the first sprint and total project timeline.
- Compute cost or token usage for the agent runs (the case study notes agents ran for a week on the first autonomous attempt).
- Whether the migrated C++ code has been deployed to production or remains in a validation environment.
- Independent replication of the workflow on other legacy languages (COBOL, PL/I, older C) or on codebases that depend on external systems and lack a runnable baseline.
- Specific Mistral model versions used for the coder, planner, tester, and reviewer roles.
Our take
The parity-harness-first discipline — numerical checkpoints before any migration code — is the transferable lesson. Full autonomy produced architecturally backward code; even the structured multi-agent loop stalled on bugs without a human in the loop. For teams facing similar procedural-to-OO migrations, the bottleneck is not translation quality but architectural refactoring and verification.
