ML teams that lack sufficient real-world evaluation data for a newly developed LLM feature or a niche domain need to bootstrap a high-quality eval set using synthetic generation — but naive LLM-generated eval data suffers from distribution narrowness, contamination with training data artifacts, and adversarial insufficiency. This agent designs and produces a rigorous synthetic eval dataset.