Building AI That Works: ESnet's Pragmatic Approach to AI-Driven Operational Excellence

2026-07-24

AI Summary

This work addresses inefficiencies in Network Operations Centers (NOCs)—including fragmented information retrieval, verbose ticketing, and loss of contextual continuity during handoffs—stemming from data silos. To mitigate these challenges, the authors propose ORBIT, an intelligent agent system integrated into the ServiceNow platform. ORBIT employs a modular, layered architecture that encapsulates task logic into versioned, testable “skills,” ensuring reliable and scalable operation within constrained behavioral boundaries. Its core components comprise a centralized reasoning engine, an MCP protocol interface to ESnet, a semantic search layer, an operational chat interface, and a LiteLLM model gateway. Evaluated on six initial tasks and rapidly adapted to two new ones, ORBIT significantly reduces operational steps, eliminates known error patterns, and sees broad adoption of its reusable components, thereby lowering cognitive load and accelerating incident response.

Abstract

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.

Problem

• operational pain points
• siloed data sources
• incident resolution
• context loss
• cognitive load

Innovation

• agentic AI
• modular architecture
• versioned skills
• semantic search
• operational integration