Review of Autonomous and Collaborative Agentic AI and Multi-Agent Systems for Enterprise Applications

Abstract

Artificial Intelligence (AI) landscape is fast developing such that there are dynamic and autonomous representatives of AI which are referred to as AI agents. These agents, fueled by the evolution of generative AI and large language models (LLMs), can make their own decisions, perform tasks and make adjustments to rapidly changing environments. An even more advanced step is the instrumentation of various specialized AI agents into working multi-agent systems (MAS). The paper will discuss the disruptive effect of the introduction of AI agents and MAS on the automation and service of enterprises and different industries. We look into their possibilities, various uses, and the natural strengths and weaknesses, such as the essentiality of effective governance infrastructure and complicated conditions in human-AI partnership. Although promising new levels of efficiency and capability to solve problems previously inaccessible, ethical implications associated with the use of these agentic systems have to be carefully explored as well as the approaches to integration that should be able to guarantee their long-term value and be serving to empower humans. This paper is a survey paper regarding Agentic AI and multi-agent systems within the enterprise context. Examining 65 of thes contemporary sources (2024-2025), we record the paradigm shift of passive generative AI to autonomous agentic systems. The paper analyses the architectural structures, models of collaboration, industrial use and governance issues. The most significant ones are (1) multi-agent systems have a 40-60% efficiency gain of the processes, (2) special agent relation coordination protocols are becoming important infrastructure, and (3) it is found that human-agent collaboration needs new stewardship and motivational models. All these are ended in the paper with new directions of agent-to-agent communications and the specific agent settings.