Contact centers are central nodes in enterprise customer engagement, yet they persistently contend with high operational costs and inconsistent service quality. The emergence of large language models (LLMs) presents a transformative opportunity through real-time agent assist capabilities. This paper proposes a deployment framework integrating LLMs via retrieval-augmented generation (RAG) architectures into live contact center workflows to provide agents with contextual suggestions and dynamic knowledge retrieval. Drawing on deployment observations and comparative analysis across representative enterprise environments, this study demonstrates that LLM-based agent assist systems can reduce average handle time (AHT) by 18 to 27 percent and produce measurable improvements in customer satisfaction scores. The paper discusses architectural considerations, integration challenges, ethical safeguards, and a phased adoption roadmap for practitioners.
Keywords: Customer Satisfaction, RAG, agent assist, average handle time, contact center AI, large language models
