Next-Generation Cloud Computing Architectures with Autonomous AI Agents for Enterprise Service Optimization
DOI:
https://doi.org/10.15662/IJEETR.2026.0802484Keywords:
Cloud computing, Autonomous AI agents, Enterprise service optimization, Artificial intelligence, Multi-cloud architecture, Hybrid cloud, Edge computing, Intelligent automation, Resource allocation, Machine learning, Service orchestration, Predictive analytics, Cloud security, Digital transformation, Enterprise architectureAbstract
The rapid evolution of cloud computing has transformed enterprise information technology by enabling scalable, flexible, and cost-efficient service delivery. However, increasing complexity in cloud environments has created challenges in resource management, security, service orchestration, and operational efficiency. The integration of autonomous Artificial Intelligence (AI) agents into next-generation cloud computing architectures offers a promising solution by enabling intelligent automation, adaptive decision-making, and real-time optimization of enterprise services. Autonomous AI agents utilize machine learning, reinforcement learning, knowledge representation, and predictive analytics to monitor cloud resources, detect anomalies, allocate workloads dynamically, and optimize system performance with minimal human intervention. This study explores the role of autonomous AI agents in enhancing enterprise cloud infrastructures through intelligent resource allocation, automated service orchestration, predictive maintenance, cybersecurity enhancement, and energy-efficient computing. The paper further examines current cloud architectural models, including hybrid, multi-cloud, edge-cloud, and serverless environments, emphasizing their compatibility with autonomous AI-driven management frameworks. A review of existing literature highlights emerging trends, technological advancements, and research gaps concerning AI-enabled cloud optimization. The proposed research methodology adopts a qualitative secondary research approach supported by systematic literature analysis. The findings contribute to understanding how intelligent cloud ecosystems improve enterprise productivity, resilience, operational efficiency, scalability, and decision-making while addressing future challenges associated with governance, trust, security, and ethical AI deployment
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