Multi-Agent AI Orchestration Using MCP and Semantic Kernel for Autonomous Enterprise Systems

Abstract

This study investigated the effectiveness of multi-agent AI orchestration using the Model Context Protocol (MCP) and Microsoft’s Semantic Kernel for autonomous enterprise systems. Rigid processes, a lack of flexibility, and inadequate fault tolerance have frequently been the limitations of traditional enterprise automation frameworks. In order to overcome these obstacles, a prototype orchestration framework was created and assessed using a design science research technique. A Semantic Kernel-driven orchestration engine, an MCP-based communication backbone, and a multi-agent layer made up the experimental setup. According to simulation results, the suggested framework performed noticeably better in terms of efficiency, adaptability, scalability, and robustness than rule-based multi-agent systems and workflow automation. In particular, it improved fault recovery, increased job completion rates, sped up reaction times, and continued to operate well even when faced with heavy workloads. These results demonstrated how MCP and Semantic Kernel may be combined to build enterprise ecosystems that are more resilient, intelligent, and autonomous.