AWS introduced a new tool to build AI agents for WhatsApp ordering.
It helps restaurants manage orders across text and voice.
The AI keeps customer context across channels.
What happened
AWS published a solution to deploy a multimodal WhatsApp ordering assistant using Amazon Bedrock AgentCore and Amazon Nova 2. Customers can text, send voice notes, or call the same WhatsApp Business number, and an AI agent handles orders end to end.
Why it matters
This offers restaurants a single backend and shared memory across channels, so a customer who texts today and calls tomorrow is recognized as the same person. It reduces the need to build and run separate systems for each ordering channel.
What to watch
The deployment requires AWS CDK, specific AWS resources, and Meta WhatsApp setup. It uses Amazon Nova 2 Lite for text and Amazon Nova 2 Sonic for voice, with AgentCore Gateway exposing backend tools via MCP.
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This AWS guide targets quick-service restaurants seeking a unified ordering channel. By consolidating app, web, and phone into WhatsApp, businesses avoid fragmented customer histories. The architecture separates the WhatsApp layer, agent runtimes, and backend, making it easy to add or remove channels without changing core logic.
The use of AgentCore memory ensures cross-channel continuity, as the same customer ID links interactions. Amazon Nova 2 Lite handles text conversations, while Amazon Nova 2 Sonic enables real-time speech, including voice calls with minimal latency. The guide details infrastructure like API Gateway, SQS, and DynamoDB to support the flow.
For developers, this solution demonstrates integrating LLMs with existing services through MCP, potentially simplifying deployment. However, the setup requires AWS CDK, model access, and Meta Business configuration, making it more suitable for technical teams in larger restaurants or cloud service providers.
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