Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting

2025-07-01

AI Summary

The lack of standardized, reproducible, and open benchmarks hinders systematic evaluation and advancement of AI agents for network troubleshooting. Method: This paper introduces NetBench—the first open-source experimental platform framework tailored to this domain—integrating large language models (LLMs) with AI agent architectures to enable automated diagnostic reasoning, network state comprehension, and interactive fault isolation, thereby supporting low-barrier, reproducible, and comparable model evaluation. Contribution/Results: (1) A modular, extensible benchmarking platform prototype; (2) A unified evaluation protocol and a curated set of representative network failure scenarios; (3) Substantial reduction in operational overhead for developing and validating AI agents. Experimental results demonstrate NetBench’s feasibility and generalizability across diverse network issues, establishing foundational infrastructure for systematic research and practical deployment of generative AI in network operations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthesis and automating network diagnosis tasks, among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effort.

Problem

• Developing a playground for AI agent benchmarking
• Standardizing network troubleshooting evaluation methods
• Enabling low-effort AI agent experimentation

Innovation

• AI agents for network troubleshooting
• Standardized benchmarking platform
• Low operational effort evaluation