A Scalable Intelligent Framework for Real-Time Agent Assistance in Contact Centers Using Large Language Models to Optimize Service Efficiency and Customer Experience

Abstract

The increasing complexity of customer interactions in modern contact centers necessitates intelligent systems capable of supporting agents in real time. Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and generation, offering unprecedented capabilities in contextual reasoning and knowledge retrieval. This study proposes a scalable intelligent framework that integrates LLMs into real-time agent assistance systems to reduce average handle time (AHT) and improve customer satisfaction. The framework incorporates retrieval-augmented generation, human-in-the-loop feedback mechanisms, and cognitive load optimization principles. Drawing on foundational works in natural language processing, human-computer interaction, and service marketing, the study develops a comprehensive architecture and evaluates its functional implications through analytical modeling. Findings suggest that LLM-driven agent assistance significantly enhances response accuracy, reduces cognitive burden on agents, and improves service consistency. However, challenges such as hallucination risks, latency constraints, and ethical considerations remain critical. The research contributes a novel synthesis of AI-driven service optimization and establishes a foundation for future empirical validation.