STELLAR: A Structured, Trustworthy, and Explainable LLM-Led Architecture for Reliable Customer Support - Scatolin, Matheus Ferracciú; Pedrini, Helio (Journal of the Brazilian Computer Society, 2026)
While Large Language Models (LLMs) offer transformative potential for automating customer support, significant hurdles remain concerning their reliability, explainability, and consistent performance in complex, sensitive interactions. This paper introduces STELLAR (Structured, Trustworthy, and Explainable LLM-Led Architecture for Reliable Customer Support), a novel architectural blueprint designed to address these issues. STELLAR utilizes a Directed Acyclic Graph (DAG) structure composed of nine specialized modules and eleven predefined workflows to orchestrate support interactions in a structured and predictable manner. This design promotes enhanced traceability, reliability, and control compared to less constrained systems. The architecture integrates components for few-shot classification, Retrieval-Augmented Generation (RAG), urgency-aware human escalation, compliance verification, user interaction validation, and knowledge base refinement through a semi-automated loop. This modular design deliberately balances LLM-driven innovation with operational requirements such as human-in-the-loop integration and ethical safeguards through embedded checks. We evaluated the core modules of STELLAR in key tasks - classification, retrieval, and compliance - demonstrating strong performance and reliability. Together, these features position STELLAR as a robust and transparent foundation for the next generation of intelligent, reliable customer support systems.