Can AI-generated code be defended in a regulated production environment? And can you put a credible number on a modernization program before the first code change?

This session presents a framework that is not AI-assisted but AI-native: AI agents analyze, document and modernize legacy systems, while senior engineers approve every defined gate - built for environments where every change must be provable. In a show case, a full system analysis was processed in about two hours instead of weeks, producing a modernization scope as a budgeting and planning basis before the first code change.

We walk through the four phases:

• Ingest: full capture of code, docs and dependencies. RAG-only, the model never gets direct filesystem access.
• Analyze: structure, risk and modernization analysis as an auditable report - the basis for scope and budget decisions.
• Implement: AI-accelerated refactoring, test and doc generation. No merge without approval by senior engineers.
• Maintain: continuous modernization in operation, with an audit trail for regulators and internal audit.

You leave with the phase model, the governance criteria to evaluate any AI coding framework against, and the traceability mechanism that makes AI-generated code defensible in regulated production: every change approved by qualified engineers and fully traceable.