This paper proposes a multi-agent collaborative design solution for the interior design industry, leveraging AI technologies to enhance design efficiency, quality, and user-designer collaboration. The framework consists of four specialized agents: data analysis, requirement guidance, scheme generation, and design optimization. The data analysis and requirement guidance agents, developed and evaluated in real-world projects, demonstrate effectiveness in reducing designers' workload and improving requirement understanding. The scheme generation and design optimization agents, although conceptual, offer innovative approaches integrating parametric modeling, graph networks, style transfer, reinforcement learning, and VR interaction. Experimental results and user feedback validate the implemented agents' practical value and provide insights for future improvements. The proposed solution has the potential to transform the interior design workflow, promoting a collaborative, data-driven, and intelligent design ecosystem. Future research directions include expanding the design knowledge base, developing efficient machine learning algorithms, customizing multi-agent platforms for different design fields, fostering cross-domain collaboration, and exploring new design production models. This research paves the way for a more intelligent and collaborative future in interior design, where designers and AI agents work together seamlessly to create innovative and user-centric design solutions.