Mistral AI has published details of a project migrating 40,000 lines of legacy Fortran 77 code to C++ for a European energy operator, offering lessons on using AI agents for large-scale code modernisation.

In a blog post published on 9 September, Mistral said the codebase, a physics-intensive reservoir simulator with no test suite or centralised documentation, presented particular challenges because Fortran 77's structure, including global COMMON blocks and implicit variable typing, does not map cleanly onto modern object-oriented C++. The company said it first built a "parity harness" to numerically verify that migrated code matched the original's outputs, calling this the most convincing proof that a module had been correctly converted.

Mistral said giving agents full autonomy to translate code independently produced functional but poorly structured results, which it described as "Fortran retyped in C++ syntax", while a rigid multi-agent workflow of planner, coder, tester and reviewer agents improved code quality but stalled when agents hit bugs they could not resolve. The company said it settled on a middle ground: a human operator overseeing coder, tester and reviewer agents, migrating the system module by module with human review gates before each merge.

The first phase of the project covered 40,000 of the codebase's 300,000 total lines. Mistral said the three principles it would carry forward to future legacy migrations were building a parity harness before writing migration code, organising documentation before relying on agents, and using structured workflows with human review rather than full autonomy or manual coding.

Swiss Cheese Defences- Identity fraud goes industrial and off-the-shelf
Ofer distinguishes between two current attack patterns: highly sophisticated, professionally coordinated deepfake and injection attacks designed to beat detection outright, and a much larger volume of lower-effort, high-scale attempts that rely on bombarding systems rather than disguising themselves well. He argues the real story right now is industrialisation of scale rather than uniform improvement in quality, though both are accelerating in parallel. The conversation covers why agentic AI is opening a new front in identity fraud, particularly the unresolved problem of tying an AI agent’s identity back to the human who deployed it and the permissions it holds. Ofer is candid about the current state of agent defences, describing them as underdeveloped and easy to hijack or poison, comparing the current state of play to Swiss cheese. He also discusses cross-industry fraud detection, including how signals, rather than raw data, are now being shared across platforms including AU10TIX and Reality Defender to surface fraud rings invisible to any single organisation. The conversation also covers explainability as one of AI’s most underdeveloped capabilities, with Ofer arguing that flagging a session as fraudulent without a credible, defensible reason will increasingly run into regulatory and practical limits. Elsewhere, the discussion covers credential laundering and the manufactured construction of fake digital histories and footprints, and the shift toward digital ID wallets that Ofer believes will make physical document fraud increasingly rare. He closes on what he calls “agentic avatars,” AI systems capable of holding a full visual and verbal conversation on someone’s behalf, and why he expects identity verification to have to become genuinely immersive across every form of media as a result.
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