How to measure productivity in the age of agentic testing

In this AI transformation roundtable, Nate Custer, Principal Technologist at TTC Global, shares how to measure productivity on an agentic quality engineering team. Drawing on a mainframe regression story, he explains why saving time only matters if you use it to close coverage gaps. He also argues that QA teams should track how often they have to fix AI-generated code, and why that measurement might mean more than speed or headcount.

Key highlights:

• Automation cut a three-month regression cycle down to one week, enabling faster feature delivery.
• AI productivity gains are often offset by the bugs AI introduces due to lack of system context.
• The right metric isn’t speed — it’s how often you have to go back and fix something in production.

Watch the webinar: https://bit.ly/4i8s5DI