TITLE:
SAGENT: An Intelligent System for the Management of Complex Workflows
AUTHORS:
Hong Wu, Vijay K. Madisetti
KEYWORDS:
AI Agent, Automated Decision Making, Autonomous Systems, Financial Technology, Graph-Based Learning, Insurance Technology, Multimodal Large Language Models (LLMs), Multi-Agent Systems (MAS), Operational Efficiency, Operational Intelligence, Process Optimization, Unstructured Data, Workflow Automation
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.18 No.12,
December
23,
2025
ABSTRACT: In today’s fast-paced, data-driven environments, many industries face challenges in handling time-consuming, complex human-machine tasks, such as chargebacks, dispute resolution, and insurance claim verifications. These tasks often involve multiple human-machine interactions and require the processing of unstructured multimedia data and nonstandardized man-machine interfaces, leading to inefficiencies, security risks, and delays. The absence of an integrated, autonomous system capable of managing such complex workflows hinders operational effectiveness. This paper presents SAGENT (System for Autonomous Graph-Enhanced Multimodal LLM AgeNTs), an innovative framework designed to automate and optimize these tasks using advanced multimodal language models (LLMs) combined with graph-based techniques. By leveraging the synergy of graph-enhanced data structures and AI-driven decision-making processes, SAGENT offers a solution to reduce human intervention and errors, accelerate resolution times, and improve overall task efficiency. We demonstrate the potential of this system in streamlining operations, enhancing accuracy, and maximizing ROI for industries reliant on the processing of complex, unstructured data.