AI in Quality Engineering: Boundaries After We Tried It
What emerged after experimenting with AI in Quality Engineering—where it helped, where it quietly caused harm, and the boundaries required to preserve confidence and ownership.
What emerged after experimenting with AI in Quality Engineering—where it helped, where it quietly caused harm, and the boundaries required to preserve confidence and ownership.
An examination of why APIs decay over time—not because of technology, but because of contract drift, unclear ownership, and shortcuts that quietly break trust as systems scale.
Examination of why quality gates are routinely bypassed, how progressive validation and ownership change behavior, and why decision-aligned gates scale better than binary rules.
A take on k6, performance testing maturity, and the signals engineering teams send when they treat load testing as traffic generation instead of system design feedback.