Deploy a multimodal WhatsApp ordering assistant with Amazon Bedrock AgentCore
AWS Machine Learning Blog · 人工智能
This post shows how to deploy a multimodal WhatsApp ordering assistant built with Amazon Bedrock AgentCore and Amazon Nova 2 . Many quick-service restaurants spread ordering across an app, a website, a phone line, and the counter. Each of those is a separate system to build and run. Each one also fragments the customer’s history, making the same person look like a stranger on every channel. Customers already live in their messaging apps. WhatsApp reaches more than two billion people. A customer who can text, send a voice note, or place a call from the same conversation does not need to install anything or sign in. A single WhatsApp Business number hosts the assistant. A customer can text the restaurant, send a voice note, or place a voice call. An AI agent takes the order end to end, from greeting to confirmation. All three channels share one backend and one cross-channel memory. A customer who texts today and calls tomorrow is recognized as the same person. The solution uses the Meta WhatsApp Business Platform as the customer front door. Amazon Bedrock AgentCore hosts the agents. Amazon Nova 2 Lite handles text through the Amazon Bedrock Converse API , and Amazon Nova 2 Sonic handles real-time speech on voice notes and calls. The agents reach the restaurant backend through the Model Context Protocol (MCP) . You deploy the whole system with the AWS Cloud Development Kit (AWS CDK) . The channel and the ordering logic stay separate, so the backend doesn’t change when you add or remove a channel. Solution overview The design keeps three things apart: (1) the WhatsApp layer handles the conversation, (2) three agent runtimes run the conversations for their channels, and (3) the backend holds the menu, carts, orders, and locations. Inbound traffic arrives on a single HTTPS webhook, is acknowledged with a 200 immediately, and then processed asynchronously so no request blocks the response. This separation keeps each layer independently deployable and straightforward to rea