Food and drinks are two of my major hobbies, and I love mixing cocktails at home. But when I host friends, I always end up as the menu. People keep asking what I have and what I can make.

In this talk I will show how I built an AI bartender on AWS that lets guests interact with a drink menu using natural language. Instead of listing options or explaining what’s available, guests can ask for things like “something fruity” or “a gin drink”, and the AI Bartender respond with suggestions powered by Amazon Bedrock.

We will go deep into how I built the AI agent with Amazon Bedrock using Strands Agents, and AgentCore providing conversation memory, identity, and a gateway for tool access. I’ll show how streaming responses, with Amazon API Gateway, are implemented for a great chat experience. MCP tools play a important role providing the agent with data, like ingredients, and menu stored in Aurora DSQL. Finally how serverless and event-driven patterns are used to decouple AI processing from APIs.

This is a technical, demo-heavy session with code walkthroughs and architecture deep-dives. We will explore agentic AI with tool calling workflows, conversation memory, and event-driven AI integration. You’ll walk away with insights on how to build intelligent, context-aware AI assistants that work with real data on AWS.