# NetGent: Agent-Based Automation of Network Application Workflows

arXiv: https://arxiv.org/abs/2509.00625 · HTML: https://mlsanity.com/paper/2509.00625/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 30 Aug 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 5 of 5)

- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://mlsanity.com/paper/2310.11441/) (2023)
- [Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks](https://mlsanity.com/paper/2503.09572/) (2025)
- [In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems](https://mlsanity.com/paper/2306.08853/) (2023)
- [Decoding the Divide: Analyzing Disparities in Broadband Plans Offered by Major US ISPs](https://mlsanity.com/paper/2302.14216/) (2023)
- [NetReplica: A Programmable Substrate for Bottleneck-Centric Network Data Generation](https://mlsanity.com/paper/2507.13476/) (2025)

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

- [Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research](https://mlsanity.com/paper/2607.26352/) (2026)

## Abstract (arXiv)

We present NetGent, an AI-agent framework for automating complex application workflows to generate realistic network traffic datasets. Developing generalizable ML models for networking requires data collection from network environments with traffic that results from a diverse set of real-world web applications. However, using existing browser automation tools that are diverse, repeatable, realistic, and efficient remains fragile and costly. NetGent addresses this challenge by allowing users to specify workflows as natural-language rules that define state-dependent actions. These abstract specifications are compiled into nondeterministic finite automata (NFAs), which a state synthesis component translates into reusable, executable code. This design enables deterministic replay, reduces redundant LLM calls through state caching, and adapts quickly when application interfaces change. In experiments, NetGent automated more than 50+ workflows spanning video-on-demand streaming, live video streaming, video conferencing, social media, and web scraping, producing realistic traffic traces while remaining robust to UI variability. By combining the flexibility of language-based agents with the reliability of compiled execution, NetGent provides a scalable foundation for generating the diverse, repeatable datasets needed to advance ML in networking.