Agentic Hyperautomation: A Distributed Architecture for Scalable AI-Driven Workflows

Hyperautomation aims to digitize end-to-end business processes, but most of the current solutions and platforms still rely on pre-defined workflows to carry out complex tasks. However, business process automation can greatly benefit from recent technological innovations at the intersection of Large Language Models (LLMs) and Multi-Agent systems (MAS). In this paper, we present an Agentic Artificial Intelligence (AI) framework where a central LLM-driven orchestrator dynamically plans and delegates tasks to specialized LLM agents, which in turn exploit tools to act on business platforms and other external systems. A case study based on document management illustrates how the approach is able to deal with complex requests involving source file parsing and report generation, leveraging an approach based on Retrieval Augmented Generation (RAG) to enable knowledge sharing among specialized agents. Finally, the proposed framework introduces a practical blueprint for scalable, explainable Agentic AI in enterprise hyperautomation environments.