A Multi-Agent Framework for Natural Language-Driven Network Simulation

This paper presents a framework that combines large language models (LLMs) with a multi-agent system (MAS) to automate the translation of natural language instructions into executable network simulations. Designed to improve the usability of traditional simulators, the proposed system enables users, regardless of technical background, to interact with Mininet through intuitive language prompts. The MAS architecture assigns specific tasks to agents, including input parsing, topology analysis, code generation, execution, and result interpretation. A retrieval-augmented generation (RAG) module boosts contextual understanding by accessing authoritative documentation. The framework was tested on benchmark scenarios of increasing complexity, such as connectivity checks, routing optimization, and DDoS mitigation. Results show that LLMs are effective at interpreting intent and refining prompts, while code generation, especially for complex tasks, remains a challenge. Larger models performed better in accuracy and robustness, whereas smaller ones struggled with error handling and refinement. These findings highlight both the promise and current limitations of agentic AI in network simulation, positioning the system as a foundation for more intelligent and accessible tools in education, research, and infrastructure management.