BenchGen

AI agents that shine in a demo often stumble once they meet real APIs, long workflows and messy business rules. BenchGen tackles that gap by building sandboxed copies of the systems an agent will actually operate, so teams can watch it succeed or fail safely before anything reaches production. The twist is that every evaluation also produces training data, which closes the loop between testing and improvement.

• Builds simulated environments that mirror real business operations, such as a full bank
• Records every action and reward so failures can be replayed and studied
• Converts benchmark runs into trajectories usable for reinforcement learning
• Supports enterprise data connections and cloud or air-gapped deployment
• Publishes live rankings showing how frontier models score on its benchmarks

BenchGen is best for enterprise AI teams, research groups and regulated organizations that need evidence an agent will hold up in multi-step, high-stakes workflows. A practical tip: browse its public benchmark rankings before a sales call to see how different base models perform on tasks similar to yours, which helps frame what you want to evaluate.