Build a serverless conversational AI agent using Claude with LangGraph and managed MLflow on Amazon SageMaker AI

Customer service teams face a persistent challenge. Existing chat-based assistants frustrate users with rigid responses, while direct large language model (LLM) implementations lack the structure needed for reliable business operations. When customers need help with order inquiries, cancellations, or status updates, traditional approaches either fail to understand natural language or can’t maintain context across multistep conversations.

This post explores how to build an intelligent conversational agent using Amazon Bedrock, LangGraph, and managed MLflow on Amazon SageMaker AI.

Solution overview

The conversational AI agent presented in this post demonstrates a practical implementation for handling customer order inquiries, a common but often challenging use case for existing customer service automation solutions. We implement an intelligent order management agent that addresses these challenges by helping customers find information about their orders and take actions such as cancellations through natural conversation. The system uses a graph-based conversation flow with three key stages:

1. Entry ...