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.

翻译：传统网络实验主要通过仿真或模拟两种方式进行验证。每种方法各有其优势与局限性。本研究提出一种基于人工智能编码代理的新型下一代网络实验工具。该工具通过仿真与模拟功能支持混合网络实验，其模拟器支持三种主要运行模式：纯仿真模式、纯模拟模式及混合模式。AgenticNet为需要结合仿真与模拟的实验场景提供了更灵活的方案，同时通过AI代理实现了快速开发。我们分别测试了Python和C++版本，结果表明C++版本在准确性和性能方面均优于Python版本。