OpenEnv, developed by Meta and Hugging Face, is an open-source framework aimed at evaluating AI agents in real-world scenarios rather than controlled environments. Traditional AI assessments often overlook challenges like multi-step reasoning, real tool interactions, and partial information, leading to a gap between research results and practical applications. OpenEnv addresses this by providing a standardized interface for agents to interact with actual tools and workflows, focusing on reliable evaluations that mirror real-world conditions. A key component of OpenEnv is the Calendar Gym, a sophisticated environment designed to test tool-using agents through the complexities of calendar management. Unlike simple simulations, the Calendar Gym forces agents to navigate real constraints such as access control, user permissions, and multi-step workflows. The environment allows agents to perform various calendar operations and face challenges like handling failed actions and missing permissions. This realism makes calendars an effective benchmark for assessing agent capabilities. Insights from testing in the Calendar Gym highlight significant challenges faced by AI agents. Multi-step reasoning emerged as a major hurdle, with agents struggling to link actions across longer tasks. Performance also dropped sharply when tasks were presented in natural language rather than explicit identifiers. More than half of the failures were due to issues with malformed arguments or incorrect action ordering, indicating that effective agent performance relies heavily on execution quality and structured feedback. These findings extend beyond calendar management and point to broader limitations in AI agent deployment across various domains.