# NetConfArena: An Executable Benchmark for LLM Agents in Closed-Loop Network Configuration

arXiv: https://arxiv.org/abs/2608.23179 · HTML: https://mlsanity.com/paper/2608.23179/

## Key facts

- Cited by 1 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 15 of its 38 references that are in the map.
- First posted on arXiv 24 Aug 2026.

## 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, 15 of 15)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://mlsanity.com/paper/2404.03648/) (2024)
- [WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration](https://mlsanity.com/paper/2408.15978/) (2024)
- [PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC](https://mlsanity.com/paper/2502.14282/) (2025)
- [What do LLMs need to Synthesize Correct Router Configurations?](https://mlsanity.com/paper/2307.04945/) (2023)
- [Enhancing Network Management Using Code Generated by Large Language Models](https://mlsanity.com/paper/2308.06261/) (2023)
- [Large Language Models for Networking: Workflow, Advances and Challenges](https://mlsanity.com/paper/2404.12901/) (2024)
- [NetArena: Dynamic Benchmarks for AI Agents in Network Automation](https://mlsanity.com/paper/2506.03231/) (2025)
- [GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration](https://mlsanity.com/paper/2407.08249/) (2024)
- [Network-wide Configuration Synthesis](https://mlsanity.com/paper/1611.02537/) (2016)
- [CAIP: Detecting Router Misconfigurations with Context-Aware Iterative Prompting of LLMs](https://mlsanity.com/paper/2411.14283/) (2024)

## Built on by (most cited first, 1 of 1)

- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poorly understood. An essential prerequisite is to assess such agents in a realistic but risk-free environment. Existing benchmarks, however, fall short: they often treat configuration as static command generation or rely on overly simplified settings. Such evaluations understate the core challenges of network configuration, where correctness requires reasoning about protocol complexity and topology dependence. We present NetConfArena, an executable benchmark for evaluating LLM agents in closed-loop network configuration. NetConfArena places agents in emulated multi-device networks, provides a standardized and compact action interface for task execution, and evaluates the resulting network behavior with hidden task-specific executable test cases. The benchmark relies on an LLM-assisted, emulation-grounded pipeline, which converts human-oriented network materials into reusable parameterized task templates. We evaluate representative LLM agents on 480 task instances instantiated from 96 protocol-focused task templates, yielding 3840 execution trajectories, and show that failures are not limited to command errors. The failures also reveal gaps in task-specification adherence and robust planning and execution. These findings suggest two future directions: using validated trajectories as supervision signals to improve foundation models, and designing harness mechanisms that make agent execution more reliable and accountable.