Aether: Network Validation Using Agentic AI and Digital Twin

2026-04-20

AI Summary

Network change validation has long relied on manual processes, which are inefficient and error-prone, while existing approaches struggle to handle the complexity of continuous changes in real-world production environments. This work proposes the first end-to-end automated validation framework that integrates multi-agent generative AI with a unified network digital twin. The framework employs five specialized agents that collaboratively orchestrate the entire pipeline—from intent parsing to test validation—by tightly coupling modeling, simulation, and agent coordination. Evaluated on both synthetic scenarios and a real ISP network, the system achieves 100% error detection accuracy and 92–96% diagnostic coverage, completing validation in just 6–7 minutes, substantially outperforming conventional methods.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• network validation
• network change
• digital twin
• operational errors
• verification coverage

Innovation

• Agentic AI
• Network Digital Twin
• Network Change Validation
• Automated Verification
• Generative AI