用智能代理系统提升客服效率，降低等待时间
Redefining CX with Agentic AI: Minerva CQ Case Study
- 构建自主决策的AI助手，主动识别客户意图并触发流程
- 部署后平均处理时长下降，首次解决率和满意度显著提升
- 适合需要实时优化客服体验的企业与技术团队
尽管人工智能在客服中心应用不断进步，客户体验仍面临平均处理时间高、首次解决率低、客户满意度差等问题。核心原因在于坐席需应对信息碎片化、手动排查故障及频繁让客户等待。现有AI辅助工具多为被动响应，依赖静态规则、简单提示或检索增强生成，缺乏深层上下文推理。本文提出一种目标驱动、自主运作、可调用工具的智能代理系统，能实时主动支持坐席。与传统方法不同，该系统可识别客户意图、触发模块化工作流、持续维护对话上下文并动态适应状态变化。本研究以真实语音客服场景中部署的Minerva CQ为例，集成实时转录、意图与情绪识别、实体抽取、上下文检索、动态客户画像及部分对话摘要功能，实现主动式工作流与连续上下文构建。上线生产后，Minerva CQ作为AI副驾，显著提升坐席效率与客户体验，在多个实际部署中均取得可衡量改进。
原文摘要 · Abstract (English)
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.
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