NetGent: Automating Application Workflows to Revolutionize Networking Research
NetGent, a new open-source tool designed to automate application workflows, promises to address critical gaps in networking research by enabling scalable, repeatable, and realistic data generation. According to a detailed blog post by Jaber Daneshamooz, the tool aims to overcome the limitations of fragile browser automation scripts and prohibitively slow agent-based systems for generating high-quality traffic datasets.
NetGent: Rethinking Workflow Automation
NetGent introduces a new architectural approach to workflow generation by separating the intent (what a workflow achieves) from its execution (how it happens). Unlike traditional tools tied to specific UI elements or layouts, NetGent’s natural-language-driven system produces abstract workflows that remain stable even when application interfaces evolve. By compiling these workflows into a deterministic, lightweight executor, NetGent ensures reliability and repeatability under varying network conditions.
The system also addresses scale. Traditional automation tools struggle to reproduce experiments across hundreds or thousands of sessions due to cost, maintenance, and instability. NetGent’s ability to regenerate workflows modularly—only updating parts impacted by application changes—creates a scalable solution usable across fields, including congestion control, Quality of Experience (QoE) analysis, and machine learning for network environments.
Market Context: Why This Matters for Research and Industry
Networking research and performance benchmarking depend on high-quality, representative data. Current methodologies involving scripted or manually curated workflows often break when applications update their UI, limiting reproducibility and scalability. The gap is significant as modern applications iterate weekly or even faster.
The broader telecom and data science industries rely on consistent workflows to study network traffic patterns, identify bottlenecks, and refine machine learning models. This need is compounded by the shift toward performance-sensitive applications like video streaming, AR/VR, and IoT systems, which demand precision testing under diverse conditions ranging from high-latency environments to burst-heavy networks.
NetGent’s novel architecture provides a critical step forward, targeting these pain points with its disaggregated workflow framework. By merging human intent and application-specific execution, it significantly boosts reproducibility while reducing maintenance overhead—two factors that often derail long-term research initiatives.
Future Outlook: Scaling the Data-Generation Substrate
NetGent signals a broader push in the industry to standardize and improve data generation for application and network experimentation. The tool is part of a suite of solutions under development, complemented by systems such as NetReplica, designed to programmatically model diverse network environments. Together, these tools aim to create a programmable, reusable substrate for networking and machine learning research.
For researchers and operators, this marks the beginning of a shift toward workflow libraries that can be shared, extended, and validated across testbeds and research groups. The commitment to open source—NetGent’s code and pre-built workflows are available on GitHub—further ensures accessibility and community-driven innovation.
As applications become increasingly complex and interconnected, tools like NetGent will play a vital role in maintaining rigorous testing standards amidst rapid technological change. The real question is whether more widespread adoption will follow, extending these benefits to commercial deployments or collaborations between academia and industry.
Read the full details of the system in the original blog post: NetGent: Agent-Based Automation of Network Application Workflows.
