An Agentic AI Framework for Enterprise Workflow Automation in Cloud Environments

Abstract

Enterprise workflows are becoming increasingly complex, making traditional automation approaches less effective in environments that require dynamic decision-making and coordinated task execution. This work presents AFAEAC, an Agentic AI Framework for Autonomous Enterprise Workflow Automation in Cloud-Native Environments, designed for IT service management workflows. The framework combines intelligent agents, workflow orchestration, governance controls, and cloud-native infrastructure to support efficient service automation. Experimental evaluation using the BPI Challenge 2013 dataset showed strong performance, achieving 96.38% accuracy with an execution latency of 128 ms. The findings demonstrate improved service efficiency, faster response times, and better resource utilization compared with existing approaches.