Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 1250-1257· 0 citations· 15 references
Abstract
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.
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
This study presents an Intelligent Workflow Orchestration (IWO) Framework for containerized cloud environments that integrates Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, and autonomous decision-making. Traditional orchestration methods often struggle to manage dynamic workloads efficiently due to their reliance on static scheduling and fixed resource allocation. The proposed framework addresses these limitations through AI-based workload forecasting, adaptive scheduling, intelligent autoscaling, resource-aware orchestration, container migration, and automated fault recovery. The architecture consists of monitoring, analytics, orchestration intelligence, and execution management layers that enable real-time workflow optimization. Machine learning models predict workload demands, while reinforcement learning supports optimal resource allocation decisions. Experimental results demonstrate improvements in workflow completion time, resource utilization, service availability, scalability, and operational efficiency compared to conventional orchestration approaches. The framework also enhances system resilience through automated fault detection and recovery, 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
Kiso is situated at the intersection of scientific workflow management and complex, agent-based computing, highlighting its potential to accelerate research on adaptive, self-organizing cyber-physical systems—an emerging frontier in complex systems science.
R. Mayani, K. Vahi, M. Rynge et al.· Frontiers in Complex Systems· 1 citation
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.
This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems, and reveals that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings.
A. Reza· International Journal of Mac...· 0 citations
LAMaS is a latency-aware orchestration framework for learning-based multi-agent systems that achieves the best latency among evaluated learning-based MAS baselines, reducing end-to-end latency by over 50% while maintaining competitive or better accuracy.