Implementing Agentic AI Enabled Multi-Cloud Enterprise Platforms for Intelligent Business Process Automation
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805035Keywords:
Agentic AI, Multi-Cloud Architecture, Intelligent Business Process Automation, Multi-Agent Systems, Enterprise Governance, Large Language Models, Retrieval-Augmented Generation, Human-in-the-Loop.Abstract
Modern enterprise environments are undergoing a paradigm shift from rigid, rule-based process automation to dynamic, cognitive orchestration driven by Agentic Artificial Intelligence (AI). Traditional Robotic Process Automation (RPA) and legacy cloud architectures struggle with unstructured data, unexpected workflow exceptions, and multi-cloud fragmentation. This paper introduces a comprehensive framework for implementing Agentic AI across distributed, multi-cloud enterprise ecosystems to achieve end-to-end Intelligent Business Process Automation (IBPA). Utilizing multi-agent orchestration topologies, autonomous tool invocation, dynamic task decomposition, and real-time retrieval-augmented context engineering, the proposed architecture abstracts underlying multi-cloud complexity—spanning AWS, Microsoft Azure, Google Cloud Platform, and private edge nodes. We evaluate the implementation using a prototype deployed across hybrid enterprise environments handling complex financial, procurement, and IT operations workflows. The results demonstrate an 81% reduction in manual process intervention, a 4.5× acceleration in end-to-end workflow completion speeds, and an exceptional 99.2% operational resilience rate against cross-cloud system failures. Furthermore, the study presents an in-depth analysis of governance mechanisms, zero-trust cryptographic boundary enforcement, and Human-in-the-Loop (HITL) exception handling required to operate non-deterministic autonomous agents safely within regulated global enterprises.
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