Agentic Engineering On the Go — Page 2

AI-generated mobile code fails security standards at fixed rates that models alone cannot fix.

Treating performance budgets as CI gates prevents silent regressions.

AI-generated code is fast, but reviews can't keep up with the volume.

Closing the gap between what automated tests verify and what users actually need from mobile apps.

Explicit product standards prevent AI from passing tests while breaking the actual product.

AI-generated code demands a new kind of QA leader.

AI agents expose gaps between intent and implementation that human code review cannot catch alone.

AI-accelerated development exposes quality standards that exist only in tribal knowledge.

Coverage metrics hide whether AI-generated code actually meets requirements.

QA's future lies in defining what success looks like, not catching every bug.

Automated checks miss real barriers that only assistive-tech users encounter.