Recent advances in generative models enabled the creation of high-quality synthetic text and images, raising concerns about provenance and misuse. We propose MW-MAS (Multimodal Watermarking Multi-Agent System), a unified framework that orchestrates watermarking across text and image modalities via three agents: the Text Watermark Agent, Image Watermark Agent, and Orchestration Agent. The Orchestration Agent adaptively selects optimal agent combinations based on sample characteristics. Evaluated on the WIT dataset, MW-MAS achieves up to 2 \(\times \) faster runtime than dual-agent baselines while maintaining high fidelity and robust bit-level watermark retrieval, offering a flexible and practical solution for multimodal content watermarking. Code is available at https://github.com/lynnchoi0126/MW-MAS .