MaNet: Towards Network Task Automation with Multi-Agent Intelligence

Large language agents have demonstrated remarkable versatility in personal computing scenarios and are increasingly expected to operate critical infrastructure. Network Task Automation (NTA), the focus of this paper, aims to compile a network operator's natural-language intent into an executable workflow over a proprietary capability catalog, which confronts a small on-premise model with heterogeneous decisions beyond the coverage of pretraining. Existing approaches typically rely on hand-crafted workflows or prompts over frozen models and thus struggle to incorporate execution feedback, while the lack of relevant public data also hinders further advancement. In response to these challenges, we present NetBench, the first NTA benchmark that pairs thousands of realistic operator intents with corresponding reference workflows over the network capability catalog. Beyond this dedicated dataset, we further introduce a multi-agent framework (MaNet) for NTA, which assigns the heterogeneous network decisions to specialized agent roles and jointly optimizes these roles via reinforcement learning from execution feedback. Empirically, MaNet exhibits consistent superiority on NetBench, as well as on the open-source benchmark TaskBench for general task automation. The analysis reveals that specialized agents improve steadily on complex intents where a shared policy plateaus, showcasing the effectiveness of multi-agent intelligence in NTA.