Snowglobe Cuts Agent Eval Cycles 20×

Snowglobe’s pitch targets one of the most painful bottlenecks in agent development: reliable eval data arrives only after users encounter failures. Simulation can dramatically widen test coverage, but its value depends on realistic personas, grounded tool behavior, and validation against production distributions.

• –Generates diverse, multi-turn scenarios before deployment
• –Supports testing tool calls, edge cases, and stateful workflows without touching production data
• –Turns synthetic conversations into datasets for evaluation, prompt iteration, and fine-tuning
• –Helps teams test model changes earlier instead of waiting weeks for trace accumulation
• –Synthetic realism remains the key risk; poorly modeled users can create false confidence