发表机构
Energy Science Network（能源科学网络）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ORBIT项目针对ESnet运营痛点，将智能人工智能集成到ServiceNow平台，采用模块化分层架构，以版本化、测试的“技能”管理工具链，完成多项任务，减少步骤、消除错误模式，获广泛应用。
AI 中文摘要
ORBIT项目旨在评估适用于即将到来的ESnet 7计划的智能人工智能，并解决网络运营中心（NOC）工作流程中持续存在的运营痛点。ESnet运营商面临孤立数据源检索缓慢、工单冗长且难以解析、轮班交接时上下文丢失等问题。ORBIT旨在实现常规自动化、跨源综合以及在运营商现有工具中直接提供可操作的见解。它是集成到ServiceNow（ESnet的主要事件管理平台）中的智能人工智能系统，采用模块化分层架构，包括集中推理中心、通过MCP访问ESnet数据源的工具、语义搜索层和面向操作员的聊天界面。通过将任务逻辑构建为有版本、经过测试的“技能”来管理人工智能工具链的复杂性和随机性。关键结果表明，ORBIT成功完成了所有六项初始任务，该架构还使NOC工程师能够快速开发另外两项任务。观察到通用基础设施组件被广泛采用，特别是聊天界面和LiteLLM模型网关。技能实验表明，这种方法可以减少任务完成步骤并消除观察到的错误模式。
英文摘要
The ORBIT (Operations Responses and Business Intelligence Toolkit) project was initiated to assess agentic AI for the upcoming ESnet 7 initiative and to address persistent operational pain points in the Network Operations Center (NOC) workflow. ESnet operators experience slow retrieval from siloed data sources, incidents described in lengthy and difficult-to-parse tickets, and context loss across shift handoffs. These challenges increase cognitive load and prolong incident resolution times. ORBIT therefore targets routine automation, cross-source synthesis, and actionable insights delivered directly within operators' existing tooling. ORBIT is an agentic AI system integrated into ServiceNow, ESnet's primary incident management platform. The design uses a modular, layered architecture comprising a centralized reasoning hub, tool access via MCPs for ESnet data sources, a semantic search layer, and an operator-facing chat interface. To manage the complexity and stochasticity of the AI toolchain, ORBIT follows industry best practices by structuring task logic as versioned, tested "skills" that guide the system in performing bounded responsibilities. This improves reliability and predictability compared to fully unconstrained agent behavior. Key results show that ORBIT successfully delivered all six initial tasks, and the architecture enabled rapid development of two additional tasks proposed by NOC engineers. We observed strong organic adoption of general-purpose infrastructure components, especially the chat interface and LiteLLM model gateway, including high request volumes from outside the project. Experiments with skills indicate that this approach can reduce task completion steps while eliminating observed error modes.
