Getting a CX agent live is only the first step. The harder work starts once real customers are using it. Our new guide looks at how teams at Lyft, Vodafone, and LATAM Airlines run CX agents in production.

It covers how they evaluate responses, monitor failures, and use production conversations to improve the system over time. You’ll learn how to: Keep prompt quality from slowing development Build observability into the agent from the start Catch failure modes before they affect more customers Turn customer conversations into useful signals for support, product, and operations Choose architectures that hold up in real CX workflows. MCP is an open standard protocol.

It connects AI models to external tools and data sources. These can be APIs, databases, or apps like Gmail, Slack, or Git Hub. So instead of you writing the integration or doing the integration for each of these applications separately, MCP basically gives you a standard way to connect to these systems.

In RAG, the model pulls the fresh information when a query comes. And this is why the model does not have to make stuff up on its own, but instead it fetches the fresh information from external data sources, like docs, PDFs, and databases, to give the most up-to-date answer to the prompt. An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions.

And then making sure everything is working fine, instead of a chatbot, which is really a request-response.