AgenticNet: Utilizing AI Coding Agents To Create Hybrid Network Experiments
This provides a more flexible approach for network researchers needing combined simulation and emulation, but it is incremental as it builds on existing methods.
The authors tackled the problem of network experiment validation by introducing AgenticNet, a tool that uses AI coding agents to enable hybrid simulation and emulation, with results showing the C++ version achieves higher accuracy and better performance than Python.
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.