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This is a recruitment post for retail store operations experts to participate in 1v1 interviews, aiming to create business scenarios for evaluating AI agent capabilities.
AgentMercury shows that training AI agents in simulated business environments generated from plain descriptions can transfer effectively to evaluation benchmarks, even if the training worlds are unrelated. The system improved performance through fine-tuning on construction traces.
AgentMercury introduces a scalable framework for synthesizing verifiable environments from business scenarios, enabling reinforcement learning agents to improve performance on enterprise and out-of-domain benchmarks.
A new research paper introduces AgentMercury, a scalable framework for synthesizing executable environments from business scenarios, which improves agent training performance on various benchmarks.