AN AI - DRIVEN MULTI-AGENT COLLABORATIVE FRAMEWORK FOR AUTONOMOUS

Abstract

This complexity in research is increasing, and it is evident that traditional human involvement and single automated systems are failing to function effectively. Moreover, it is clear that despite the growth in the technological capabilities of AI with regards to the automation of tasks such as literature searches and results processing, the traditional method remains disjointed and requires great human involvement. The paradigm of the multi-agent collaboration model has the potential to allow for the automation of scientific research, including research results and papers, making the current manuscript exceptional and creative. This model includes precise autonomous agents with different parameters for research, such as literature searches, thesis writing, experimentation, and results analysis. The model of the multi-agent collaboration framework works in an environment that allows for effective dynamic decomposition. The ability for the multi-agent model to collaborate, problem-solve, and decide is the driving force behind the conduct of excellent scientific research with little or no human participation, making the scientific research process completely repeatable. Tests have shown that the multi-agent model has the capability to detect shortcomings in the scientific research process, conduct tests, analyze results, and provide appropriate documentation accordingly. This research helps to boost the intelligent discovery process of the scientific inquiry by illustrating the capability of the model to coordinate intelligent agents to solve complicated scientific research processes, thereby promoting the development of robust scientific AI research environments.