Data minimization is difficult in legacy systems because teams cannot identify which fields are actually used. A code scanning approach can identify unused data fields by tracing their paths through the codebase, creating an evidence-based shortlist for removal decisions rather than relying on room consensus.
Brownfield agentic engineering involves using AI agents safely in legacy codebases by making hidden constraints visible and ensuring changes are trustworthy. The approach uses zone-based risk mapping—green zones for well-tested code where agents can work autonomously, yellow zones requiring characterization tests first, and red zones (auth, billing, permissions) that need human oversight to avoid technical debt.