CrossnoKaye 如何用 Amazon Bedrock AgentCore 落地物理 AI
原标题：How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore
AI 摘要
CrossnoKaye 将其软件定义自动化平台 ATLAS 与智能体系统 AURA 构建在 AWS 上，利用 Amazon Bedrock AgentCore 的代码解释器在隔离微虚拟机沙箱中运行 Python 计算，为物理 AI 的数值答案提供真实数学支撑。ATLAS 已覆盖 100 多个工业设施和 13,000 台受控设备，每天处理 500 亿条新读数，运行超 1,000 个闭环优化应用，据称可提升产能 11–17%、降低设施总能耗平均 21%。该方案通过边缘集群与云端协同，在不替换现有 PLC/DCS 硬件的前提下实现闭环控制。
正文节选
How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore Industrial operations present AI challenges that don’t show up in typical enterprise software. In most facilities, plant data and control logic are spread across separate systems from different vendors: programmable logic controllers (PLCs) and distributed control systems (DCS) run the equipment logic, historians record time-series tags under site-specific naming schemes, supervisory control and data acquisition (SCADA) systems dr