This hands-on session demonstrates how Model Context Protocol can revolutionize customer support systems by creating genuinely humanized interactions. Participants will transform a standard support chatbot into an empathetic, context-aware assistant that understands and addresses complex customer needs.

Starting with a typical support scenario—customers facing multiple, interconnected issues—attendees will experience the limitations of traditional RAG-based systems that treat each query as isolated and fail to capture emotional context. Through guided implementation exercises, participants will:

Build a system that recognizes customer frustration levels and adapts response tone accordingly
Implement memory mechanisms that maintain conversation history across multiple interactions
Develop agentic capabilities that proactively retrieve relevant information before customers need to ask
Create intelligent handoff protocols that seamlessly transition between automated and human support

Working with real customer conversation datasets, participants will implement and test their enhanced systems, measuring improvements in customer satisfaction scores, resolution time, and first-contact resolution rates. The workshop includes practical code patterns for detecting sentiment, managing conversation state, and dynamically adjusting retrieval strategies based on interaction history.