AgenticNet: Utilizing AI Coding Agents To Create Hybrid Network Experiments

TL;DR

AgenticNet introduces AI coding agents to enable flexible hybrid network experiments combining simulation and emulation, improving development speed and accuracy in network research.

Contribution

This work presents a novel AI-powered tool, AgenticNet, for hybrid network experimentation, integrating simulation and emulation with support for rapid development.

Findings

C++ version outperforms Python in accuracy and performance

AgenticNet enables flexible hybrid network experiments

Supports rapid development with AI agents

Abstract

Traditional network experiments focus on validation through either simulation or emulation. Each approach has its own advantages and limitations. In this work, we present a new tool for next-generation network experiments created through Artificial Intelligence (AI) coding agents. This tool facilitates hybrid network experimentation through simulation and emulation capabilities. The simulator supports three main operation modes: pure simulation, pure emulation, and hybrid mode. AgenticNet provides a more flexible approach to creating experiments for cases that may require a combination of simulation and emulation. In addition, AgenticNet supports rapid development through AI agents. We test Python and C++ versions. The results show that C++ achieves higher accuracy and better performance than the Python version.