Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 874-880· 0 citations· 19 references
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.
Cloud-edge computing environments are evolving rapidly, requiring orchestration mechanisms that may automatically construct and manage complex multi-step workflows with little human intervention. We introduce a framework for the agentic AI and how it should be able to orchestrate an autonomous end-to-end workload of cl...
Shiza Arshad, Anusha Joodala, A. Agade et al.· 2026 International Conferenc...· 0 citations
Artificial Intelligence (AI), cloud computing, IoT, big data analytics, and automation are driving the evolution of digital transformation toward integrated and intelligent enterprise ecosystems. This research proposes an AI Orchestrated Digital Ecosystem (AIODE) framework that combines data acquisition, integration, A...
Rajesh Sharma· International Journal of Art...· 0 citations
This study presents an Intelligent Workflow Orchestration (IWO) Framework for containerized cloud environments that integrates Artificial Intelligence, Machine Learning, predictive analytics, and autonomous decision-making, providing a scalable and adaptive solution for next-generation cloud-native applications and aut...
Farhan Malik, Zara Ahmed· International Journal of App...· 0 citations
Autonomous AI agents represent a major advancement in workflow optimization by enabling intelligent, adaptive, and self-learning automation. Unlike traditional rule-based systems, these agents can handle dynamic environments, uncertainty, and complex decision-making through techniques such as reinforcement learning and...
Chen Wei, Liu Fang· International Journal of Art...· 0 citations
The optimization of data pipelines is critical for enhancing the performance and efficiency of AI workflows, which often involve complex, heterogeneous, and dynamic data processing stages. Traditional approaches to pipeline optimization struggle to adapt autonomously to evolving workloads and system conditions. This pa...
José María Troya, R. L. D. Mántaras· International Journal of Dat...· 0 citations
UMA, a Unified Multi-Agent Framework for enterprise AI systems, is introduced, designed to support the complete lifecycle of agentic systems, including deployment, orchestration, execution, monitoring, and return-on-investment (ROI) realization.
Umamaheswara Rao Kukkala· International Journal of Inn...· 0 citations
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