Aether: Network Validation Using Agentic AI and Digital Twin

TL;DR

Aether automates network change validation by integrating agentic AI with a digital twin, enhancing accuracy and speed while reducing manual effort in network operations.

Contribution

It introduces a novel agentic AI architecture combined with a network digital twin to automate and improve network change validation workflows.

Findings

Achieved 100% error detection in synthetic scenarios.

Provided 92-96% diagnostic coverage on real ISP incidents.

Reduced validation time to 6-7 minutes, faster than traditional methods.

Abstract

Network change validation remains a critical yet predominantly manual, time-consuming, and error-prone process in modern network operations. While formal network verification has made substantial progress in proving correctness properties, it is typically applied in offline, pre-deployment settings and faces challenges in accommodating continuous changes and validating live production behavior. Current operational approaches typically involve scattered testing tools, resulting in partial coverage and errors that surface only after deployment. In this paper, we present Aether, a novel approach that integrates Generative Agentic AI with a multi-functional Network Digital Twin to automate and streamline network change validation workflows. It features an agentic architecture with five specialized Network Operations AI agents that collaboratively handle the change validation lifecycle from intent analysis to network verification and testing. Aether agents use a unified Network Digital Twin integrating modeling, simulation, and emulation to maintain a consistent, up-to-date network view for verification and testing. By orchestrating agent collaboration atop this digital twin, Aether enables automated, rapid network change validation while reducing manual effort, minimizing errors, and improving operational agility and cost-effectiveness. We evaluate Aether over synthetic network change scenarios covering main classes of network changes and on past incidents from a major ISP operational network, demonstrating promising results in error detection (100%), diagnostic coverage (92-96%), and speed (6-7 minutes) over traditional methods.