摘要：随着大型语言模型 (LLM) 进化为自主智能体，传统静态评估已无法捕捉多步决策过程。我们引入了 AgenticAI-Supervisor，一个由 API 和 UI 驱动的 RL Gym 环境，它把环境创建与可扩展执行分离。通过转向可验证的执行结果，平台生成高保真轨迹并实施多维奖励塑造。至关重要的是，我们的框架通过严格的内部状态验证和测试来缓解奖励破解。本文通过客户支持智能体案例研究，首次展示平台的核心能力，演示了一致的闭环反馈以进行模型优化。未来工作将聚焦于高级功能，如计算机使用、工具使用、自动“出题难倒”和边缘案例生成。

Abstract:As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making. We introduce AgenticAI-Supervisor, an API and UI-driven RL Gym environment that decouples environment creation from scalable execution. By moving to verifiable execution outcomes, the platform generates high-fidelity traces and applies multi-dimensional reward shaping. Critically, our framework mitigates reward hacking through rigorous internal state validation and testing. This work provides a first look at our platform's core capabilities through a Customer Support Agent case study demonstrating a consistent closed-loop feedback for model optimization. Future work will focus on advanced features such as Computer Use, Tool Use, automated "stumping", and edge-case generation.