Recent advancements in Large Language Models (LLMs) have led to significant capabilities in natural language tasks. The proposed Mixture-of-Agents (MoA) methodology leverages multiple LLMs to achieve state-of-the-art performance on various benchmarks. The MoA architecture shows promise in surpassing existing models like GPT-4 Omni. This approach opens doors for collective intelligence in AI systems. Further research could focus on optimizing the interaction between LLM agents in MoA for even greater performance.