Operating large-scale scientific facilities requires coordinating diverse subsystems, translating operator intent into precise hardware actions, and maintaining strict safety oversight. Language model-driven agents offer a natural interface for these tasks, but most existing approaches are not yet reliable or safe enough for production use. In this paper, we introduce Osprey, a framework for using agentic AI in large, safety-critical facility operations. Osprey is built around the needs of control rooms and addresses these challenges in four ways. First, it uses a plan-first orchestrator that generates complete execution plans, including all dependencies, for human review before any hardware is touched. Second, a coordination layer manages complex data flows, keeps data types consistent, and automatically downsamples large datasets when needed. Third, a classifier dynamically selects only the tools required for a given task, keeping prompts compact as facilities add capabilities. Fourth, connector abstractions and deployment patterns work across different control systems and are ready for day-to-day use. We demonstrate the framework through two case studies: a control-assistant tutorial showing semantic channel mapping and historical data integration, and a production deployment at the Advanced Light Source, where Osprey manages real-time operations across hundreds of thousands of control channels. These results establish Osprey as a production-ready framework for deploying agentic AI in complex, safety-critical environments.

翻译：运行大型科学设施需要协调多样化的子系统，将操作员意图转化为精确的硬件动作，并保持严格的安全监管。语言模型驱动的智能体为这些任务提供了自然的交互界面，但现有方法大多尚未达到生产环境所需的可靠性与安全性标准。本文介绍Osprey框架，该框架旨在将智能体人工智能应用于大规模、安全关键的设施运营场景。Osprey围绕控制室的实际需求构建，通过四个层面应对核心挑战：首先，采用计划优先的编排器，在接触任何硬件前生成包含完整依赖关系的执行计划供人工审核；其次，协调层负责管理复杂数据流，保持数据类型一致性，并在必要时自动对大规模数据集进行降采样；第三，动态分类器根据任务需求仅选择必要工具，确保在设施功能扩展时提示词保持紧凑；第四，连接器抽象与部署模式适配不同控制系统，满足日常使用需求。我们通过两个案例验证框架效能：控制助手教程展示了语义通道映射与历史数据集成功能，以及在先进光源装置的生产部署中，Osprey成功管理数十万个控制通道的实时操作。这些成果表明Osprey已成为在复杂安全关键环境中部署智能体人工智能的生产就绪型框架。