Trustworthy Agentic AI Systems: A Hybrid Cloud Framework for Scalable Autonomous Decision-Making
Abstract
The rapid evolution of Artificial Intelligence (AI) has accelerated the adoption of agentic AI systems capable of autonomous reasoning, planning, decision-making, and task execution across dynamic environments. However, ensuring trustworthiness, scalability, security, and governance remains a significant challenge, particularly in distributed cloud ecosystems. This paper proposes a Hybrid Cloud Framework for Trustworthy Agentic AI Systems that integrates public and private cloud infrastructures to support secure, scalable, and reliable autonomous decision-making. The framework combines large language models (LLMs), retrieval-augmented generation (RAG), knowledge graphs, multi-agent orchestration, and explainable AI (XAI) mechanisms to enable transparent and context-aware decision processes. A comprehensive trust layer incorporating identity and access management, zero-trust security, policy enforcement, continuous monitoring, audit logging, and ethical compliance ensures responsible AI deployment. The proposed architecture dynamically allocates computational workloads across hybrid cloud environments to optimize performance, reduce latency, and enhance fault tolerance while preserving data privacy and regulatory compliance. Experimental evaluation demonstrates improvements in decision accuracy, resource utilization, scalability, and operational resilience compared to conventional cloud-based AI deployments. The proposed framework provides a practical foundation for developing next-generation trustworthy agentic AI applications in healthcare, finance, smart manufacturing, government services, and enterprise automation
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