OpenEnv in Practice: Evaluating Tool-Using Agents in Real-World Environments

AI agents often fail in real-world systems despite success in controlled research settings. To bridge this gap, the OpenEnv framework provides a standardized way to evaluate agents against real environments rather than simulations. Turing contributed a production-grade "Calendar Gym" benchmark, which tests agents on complex calendar management tasks involving access control, temporal reasoning, and multi-agent coordination. Findings from the Calendar Gym show that agents struggle with multi-step reasoning and ambiguity, with performance dropping significantly on tasks described in natural language. This highlights that reliable agent behavior depends not just on tool selection but also on execution quality and structured environmental feedback.