Poster: LLM Multi-Agent Collaboration for Network Deployment and Management

This paper presents the MAPLE framework, which harnesses large language models (LLMs) to facilitate multi-agent collaboration for fully automated deployment and management of large-scale networks. Within MAPLE, a supervisor agent interprets natural language instructions from users, orchestrates specialized agents to execute tasks, and validates outcomes through integration with a network simulation platform. Experimental findings show that MAPLE outperforms single-agent approaches in terms of success rates for topology deployment and service configuration. Moreover, experiments reveal that by adaptively employing LLMs with varying capabilities according to task requirements and inter-agent dependencies, the framework effectively balances task success rates with cost efficiency.