This research paper explores multi-LLM-agent systems (MLAS), a new paradigm in artificial intelligence where multiple large language models (LLMs) act as autonomous agents, collaborating to solve complex tasks. The authors discuss the technical aspects of MLAS, including architecture, communication protocols, and agent training methods, while also addressing key business considerations such as data privacy and monetization strategies. Different MLAS architectures are examined, along with potential security vulnerabilities and defenses. Finally, the paper presents case studies illustrating real-world applications and implications of MLAS.

https://arxiv.org/pdf/2411.14033