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 cloud-edge enterprise infrastructures in general. The proposed framework relies on large language model (LLM)-driven agents capable of dynamic task decomposition, real-time decision-making, and self-correcting execution pipelines to manage heterogeneous workloads. Through the incorporation of multi-agent coordination protocols, context-aware scheduling algorithms, and feedback-driven optimization loops, the system facilitates seamless task delegation throughout edge nodes and cloud backend systems while managing latency, resource allocation, and compliance constraints. Experimental evaluations show up to percentage improvements in workflow completion rates, resource utilization, and fault tolerance over traditional static-command Rule-based orchestration approaches. Additionally, the framework features explainability modules and audit trails to promote transparency and accountability in autonomous operations. The results provide evidence that agentic AI architectures can serve as a scalable, resilient and intelligent control mechanism for next generation enterprise workflow management across hybrid cloud-edge settings. This has laid a foundation and is to our best of knowledge, the first systematic pioneers work that lays down a roadmap for production-grade autonomous orchestration deployed in analytics and enterprise domains.
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, AI intelligence, digital orchestration, process automation, and decision support layers. The framework enables intelligent decision-making, predictive analytics, resource optimization, and adaptive business operations through AI and machine learning. Performance evaluation using key indicators demonstrates significant improvements in operational efficiency, automation, customer satisfaction, predictive accuracy, and resource utilization compared to traditional enterprise systems. The proposed approach supports scalable, agile, and sustainable enterprise transformation, providing valuable insights for researchers and practitioners developing next-generation intelligent enterprises.
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 autonomous cloud infrastructure management.
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 natural language processing. Their integration into enterprise workflows improves efficiency, reduces execution time, minimizes errors, and optimizes resource utilization. The study highlights that agent-based models significantly outperform conventional automation methods, especially in complex and changing conditions. Although challenges like scalability and ethical concerns remain, autonomous AI agents have strong potential to transform workflows into self-optimizing systems across various industries.
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 paper proposes a novel multi-agent system (MAS) framework that enables autonomous optimization of data pipelines in AI workflows. Each agent is responsible for specific tasks such as data ingestion, transformation, scheduling, and resource management, and they collaborate through adaptive protocols to achieve global optimization objectives. We demonstrate the effectiveness of the proposed framework through extensive experiments, showing significant improvements in pipeline throughput, latency, and resource utilization compared to conventional methods. Our approach highlights the potential of MAS to bring intelligence, flexibility, and scalability to data pipeline management in AI systems.
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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