Title: Redefining CX with Agentic AI: Minerva CQ Case Study Show Chinese title

Title: 用代理AI重新定义CX：Minerva CQ案例研究

Abstract: Despite advances in AI for contact centers, customer experience (CX) continues to suffer from high average handling time (AHT), low first-call resolution, and poor customer satisfaction (CSAT). A key driver is the cognitive load on agents, who must navigate fragmented systems, troubleshoot manually, and frequently place customers on hold. Existing AI-powered agent-assist tools are often reactive driven by static rules, simple prompting, or retrieval-augmented generation (RAG) without deeper contextual reasoning. We introduce Agentic AI goal-driven, autonomous, tool-using systems that proactively support agents in real time. Unlike conventional approaches, Agentic AI identifies customer intent, triggers modular workflows, maintains evolving context, and adapts dynamically to conversation state. This paper presents a case study of Minerva CQ, a real-time Agent Assist product deployed in voice-based customer support. Minerva CQ integrates real-time transcription, intent and sentiment detection, entity recognition, contextual retrieval, dynamic customer profiling, and partial conversational summaries enabling proactive workflows and continuous context-building. Deployed in live production, Minerva CQ acts as an AI co-pilot, delivering measurable improvements in agent efficiency and customer experience across multiple deployments. Show Chinese abstract

Abstract: 尽管人工智能在客服中心方面取得了进展，客户体验（CX）仍然受到平均处理时间（AHT）高、首次呼叫解决率低和客户满意度（CSAT）差的问题困扰。 一个关键原因是代理人员的认知负担，他们必须在碎片化的系统中导航、手动排除故障，并经常让客户等待。 现有的人工智能驱动的代理辅助工具通常是被动的，由静态规则、简单提示或检索增强生成（RAG）驱动，缺乏更深层次的上下文推理。 我们引入了基于目标的代理人工智能，这是一种自主的、使用工具的系统，能够在实时中主动支持代理人员。 与传统方法不同， 代理人工智能能够识别客户意图，触发模块化工作流，保持不断演变的上下文，并根据对话状态动态适应。 本文介绍了一个案例研究，即Minerva CQ，这是一个在基于语音的客户服务中部署的实时代理辅助产品。 Minerva CQ集成了实时转录、意图和情感检测、实体识别、上下文检索、动态客户档案和部分对话摘要，实现了主动工作流和持续的上下文构建。 在实际生产环境中部署，Minerva CQ作为人工智能副驾驶，实现了多个部署中的代理效率和客户体验的显著提升。