拆包Agent AI：用AI Agent重写CX规则。

Unpackingagentic AI: Rewritingthe rules of CX withAI agents. Table of contents Introduction: A new era of customer experience.03I.From CX 1.0 to AI agents.04 II.The generative AI acceleration.05III.What’s still missing? Autonomy.06IV.What is agentic AI?07V.The power of AI agents.08VI.What an AI agent looks like in practice.09VII.Multi-agent systems in CX.10VIII.Before and after AI agents.11IX.KPI impact across the board.12X.Getting started with AI agents.13XI.The new definition of performance.14 Introduction: A new eraof customer experience. Traditional measures like AHT, CSAT, and FCR stillmatter, but the way we achieve and improve themis shifting. Enter agentic AI—a powerful newcapability that’s pushing us to completely rethinkwhat performance looks like in the contact center.We’re standing on the edge of a massive productivity breakthrough, driven by a new kind of AI. Not bots,not scripted tools—but intelligent, autonomoussystems that can adapt in real time, take initiative,and learn from outcomes. These AI agents aren’tjust supporting teams—they’re becoming partof them. And they’re rewriting the rules. I. From CX 1.0 to AI agents. In the first generation of AI in CX—what we mightcall CX 1.0—we saw real gains. Scripted chatbotsand workflow automation delivered faster responsetimes, more personalization, and better data visibility. maintaining these systems required complex modeltraining, specialized infrastructure, and dedicateddata science teams. That made it difficult to scaleand even harder to adapt.Now, with generative and agentic AI, we’ve entered a new phase—one that’s more scalable, moreadaptive, and more accessible.And the performance upsideis dramatically higher. II. The generative AI acceleration. Generative AI has exploded into the enterprise.In under a year, 38% of organizations moved topilot or production. CEOs are mandating investment,and CIOs are being asked to boost generative AIspending by 75% over the next two years. To customer-facing teams.Forty-two percent of companies are prioritizing customer service, success, and engagement. Why?Because that’s where the fastest productivity gainsare—and where AI can have the most immediatebusiness impact. of companies are prioritizing customer42% III. What’s still missing?Autonomy. clearly, personalize experiences, and even mimichuman tone. But it doesn’t act on its own. It can’tmake decisions, manage workflows, or respondto real-time context. IV. What is agentic AI? Agentic AI is designed not just to respond, but to reason,act, and improve over time. Here’s how it works: It builds a plan of action, It carries out that plan— It continuously improves Customers interact The AI understands the intent, either responding directly ororchestrating across systems.through techniques likereinforcement learning. naturally—via voiceor text. dynamically and in real time. evaluates options, and decideson the best path forward. adapting, and getting better with every interaction. V. The power of AI agents. When you combine the language strengths of generative AI withthe autonomy of agentic AI, you get something powerful: a fullyconversation-driven, outcome-oriented customer experience. AI agents don’t just understand—they act.They don’t just assist—they collaborate.And they bring that capability to bothcustomer-facing workflows andbehind-the-scenes agent support. volume. Instead of waiting, you can chatwith ourAI assistant right now. to the appointment. I have re-scheduledthe appointment. VI. What an AI agent looks like in practice. An AI agent brings everything together: •Natural conversation•Contextual reasoning•Smart decision-making•Workflow execution•Real-time learning And it’s goal-driven—you describe what you wantto happen, and the AI figures out how to get there. VII. Multi-agent systems in CX. Today’s most advanced implementations use multipleAI agents—each with a specific role. A primary agentorchestrates the interaction, while sub-agents handlethings like customer conversations, knowledgeretrieval, or agent coaching. prompts. Admins and supervisors don’t need toconfigure flows or write logic—they just describeoutcomes. The agents handle the rest.This model is scalable, modular, and far more agile than traditional rules-based systems. VIII. Before and after AI agents. faster design time andmeasurable gains across coreperformance indicators.90%Organizations report up to Before After •Manual configuration•Rigid, time-consuming design processes•Limited adaptability•KPI impact capped by operationaloverhead •Dynamic, autonomous execution•Faster deployment and iteration•Intelligent decision-making•Improved AHT, FCR, and CSAT—plus new KPIslike AI resolution rate and containment IX. KPI impact across the board. AI agents are already improving outcomes across five key areas: Faster answers, higher productivity. incremental improvement—it’s a performance reset. More coverage, better compliance. X. Getting star