This paper introduces MaAS, a novel framework for automating the design of multi-agent systems built on Large Language Models (LLMs). Instead of seeking a single best system, MaAS optimizes an agentic supernet, a probabilistic distribution of possible architectures. This allows MaAS to dynamically sample query-dependent multi-agent systems, tailoring solutions and resource allocation based on the specific input. Experimental results demonstrate that MaAS achieves higher performance across various benchmarks compared to existing methods while being more resource-efficient in terms of training and inference costs. Furthermore, MaAS exhibits strong transferability across different datasets and LLMs and possesses inductive capabilities to handle new agentic operators.