NetGent: Agent-Based Automation of Network Application Workflows

2025-08-30

AI Summary

Existing browser automation tools suffer from insufficient environmental fidelity, poor reproducibility, weak robustness, and high operational costs when generating diverse, high-fidelity network traffic. To address these limitations, we propose a natural language–driven executable workflow compilation framework. It automatically compiles user-specified, state-dependent web application behaviors—expressed in natural language—into nondeterministic finite automata (NFAs). By integrating state caching and LLM invocation optimization, the framework ensures execution determinism while enhancing adaptability to dynamic UI variations. The approach enables rapid workflow reconstruction and precise trajectory replay, substantially reducing manual intervention and computational overhead. Evaluated across 50+ real-world scenarios—including video-on-demand, live streaming, online conferencing, social media interaction, and web crawling—the framework generates high-fidelity traffic traces, demonstrating superior performance in diversity, robustness, and efficiency.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Automating diverse network application workflows for traffic generation
• Overcoming fragility and cost of existing browser automation tools
• Generating realistic, repeatable datasets for ML model development

Innovation

• Natural-language workflow specifications
• NFA compilation for deterministic replay
• State caching reduces LLM calls