I think there's a lot of methodological expertise that goes into collecting good eval data. For example, in many cases you need human labelers with the right expertise, well designed tasks, well defined constructs, and you need to hit interrater agreement targets and troubleshoot when you don't. Good label data is a prerequisite to the stuff that can probably be automated by the AI agent (improving the system to optimize a metric measured against ground truth labels). Data scientists and research scientists are more likely to have this skillset. And it takes time to pick up and learn the nuances.