The hard part of building AI agents is knowing whether your modifications improved or degraded their ability to perform their tasks. The only way to know is to measure their success against real-world examples. Enter gym scenarios: model replicas of common UX patterns and computer-use actions that validate modifications to QA Wolf AI.

QA Wolf Engineering Lead Yurij Mikhalevich and host Caleb Masters break down how this system spots failures fast and allows for rapid iteration and improvement.

In this webinar, you will learn:

• Why standard agent metrics fall short in real-world testing.
  
• How weighted gym scenarios make evaluations more accurate and relevant.
  
• Where QA Wolf’s framework continuously adjusts agent behavior for real-world conditions.

Figure out how smarter evaluation keeps AI useful by watching the video or reading our recap.