The field of artificial intelligence is witnessing a significant shift towards the development of more advanced and dynamic systems, with a focus on multi-agent collaboration and large language models. Recent research has explored the potential of these systems in various applications, including phishing email detection, code optimization, and decision-making tasks. A key direction in this field is the use of multi-agent frameworks, where multiple agents with specialized expertise work together to achieve a common goal. This approach has shown promising results in improving the accuracy and efficiency of various tasks, such as phishing email detection and code optimization. Another important trend is the integration of large language models with reinforcement learning, which has enabled the development of more advanced decision-making systems. These systems have the potential to revolutionize industries such as energy and materials science, where complex decision-making tasks are common. Noteworthy papers in this area include MultiPhishGuard, which presents a dynamic LLM-based multi-agent detection system for phishing email detection, and EvoGit, which introduces a decentralized multi-agent framework for collaborative software development. Additionally, papers such as HASHIRU and MACS have demonstrated the potential of multi-agent systems in hybrid intelligent resource utilization and optimization of crystal structures, respectively.