AbstractMulti-Agent Systems (MAS) have emerged as a powerful framework for addressing complex, distributed computational challenges. Unlike single-agent architectures, MAS leverages multiple agents that collaborate, learn, and optimize decision-making in decentralized environments. This paper explores the core motivations for MAS, including overcoming limitations such as single-threaded processing, high computational costs, hallucinations in large language models (LLMs), constrained context w...