Abstract
The objective of this paper is to examine the construction and design of multi-agent autonomous workflow systems (MAWS) that utilize agentic AI systems, as well as their impact on the evolution of business systems and the efficiency of business processes and decision-making. Multi-agent systems (MAS) consist of multiple autonomous agents that perform tasks independently. The agentic AI frameworks used to design systems that allow autonomous agents to work together, move tasks around on the fly, and adjust to changes in the work environment. The paper concentrates on the architectural design, implementation strategies, and applications of systems cantered on MAS, emphasizing decision intelligence, scalability, and flexibility as paramount. This paper talks about the systems and how advanced machine learning and reinforcement learning are used in them. It also suggests a way to build the best autonomous workflow systems. The systems can make the economy more productive, and they add value to the complicated fields of business operations, health care, manufacturing, and logistics. The systems give users a lot of value by making them more effective and improving their decision-making systems.