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

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.

@article{wang2025_2507.01997,
  title={ Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting },
  author={ Zhihao Wang and Alessandro Cornacchia and Franco Galante and Carlo Centofanti and Alessio Sacco and Dingde Jiang },
  journal={arXiv preprint arXiv:2507.01997},
  year={ 2025 }
}