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

arXiv: https://arxiv.org/abs/2507.01997 · HTML: https://mlsanity.com/paper/2507.01997/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 7 of 7)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models](https://mlsanity.com/paper/2310.16340/) (2023)
- [Building AI Agents for Autonomous Clouds: Challenges and Design Principles](https://mlsanity.com/paper/2407.12165/) (2024)
- [MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments](https://mlsanity.com/paper/1606.05995/) (2016)
- [Strada-LLM: Graph LLM for traffic prediction](https://mlsanity.com/paper/2410.20856/) (2024)
- [LLM-Sketch: Enhancing Network Sketches with LLM](https://mlsanity.com/paper/2502.07495/) (2025)
- [Direct Telemetry Access](https://mlsanity.com/paper/2202.02270/) (2022)

## Built on by (most cited first, 2 of 2)

- [SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting](https://mlsanity.com/paper/2605.04530/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

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.