Industrial automation in the energy sector requires AI systems that can operate autonomously regardless of network availability, a requirement that cloud-centric architectures cannot meet. This paper evaluates the application of Device-First Continuum AI (DFC-AI) to critical energy sector operations. DFC-AI, a specialized architecture within the Hybrid Edge Cloud paradigm, implements intelligent agents using a microservices architecture that originates at end devices and extends across the computational continuum. Through comprehensive simulations of energy sector scenarios including drone inspections, sensor networks, and worker safety systems, we demonstrate that DFC-AI maintains full operational capability during network outages while cloud and gateway-based systems experience complete or partial failure. Our analysis reveals that zero-configuration GPU discovery and heterogeneous device clustering are particularly well-suited for energy sector deployments, where specialized nodes can handle intensive AI workloads for entire fleets of inspection drones or sensor networks. The evaluation shows that DFC-AI achieves significant latency reduction and energy savings compared to cloud architectures. Additionally, we find that gateway based edge solutions can paradoxically cost more than cloud solutions for certain energy sector workloads due to infrastructure overhead, while DFC-AI can consistently provide cost savings by leveraging enterprise-owned devices. These findings, validated through rigorous statistical analysis, establish that DFC-AI addresses the unique challenges of energy sector operations, ensuring intelligent agents remain available and functional in remote oil fields, offshore platforms, and other challenging environments characteristic of the industry.

翻译：能源领域的工业自动化需要能够独立于网络可用性而自主运行的人工智能系统，这一需求是云中心架构无法满足的。本文评估了设备优先连续体人工智能（DFC-AI）在能源领域关键业务中的应用。DFC-AI是混合边缘云范式中的一种专用架构，它采用微服务架构实现智能体，该架构始于终端设备并延伸至整个计算连续体。通过对能源领域场景（包括无人机巡检、传感器网络和工人安全系统）的全面模拟，我们证明DFC-AI在网络中断期间能保持完整的运行能力，而基于云和网关的系统则出现完全或部分故障。我们的分析表明，零配置GPU发现和异构设备集群特别适用于能源领域的部署，其中专用节点能够为整个巡检无人机或传感器网络群处理密集的人工智能工作负载。评估显示，与云架构相比，DFC-AI实现了显著的延迟降低和能耗节约。此外，我们发现对于某些能源领域工作负载，基于网关的边缘解决方案可能因基础设施开销而成本高于云解决方案，而DFC-AI通过利用企业自有设备能够持续提供成本节约。这些经过严格统计分析验证的研究结果证实，DFC-AI能够应对能源领域业务特有的挑战，确保智能体在偏远油田、海上平台以及该行业特有的其他恶劣环境中保持可用和正常运行。