Build Production-Grade Multi-Agent AI on AWS in 60 Minutes

What you'll learn

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Model the business value and ROI of AI agent projects using interactive Streamlit dashboards to justify investment and track performance
Integrate production-grade AWS data sources like DynamoDB and Aurora MySQL to serve as differentiators for specialized AI agents
Build RAG-enabled applications using Amazon Bedrock Knowledge Bases and OpenSearch Serverless for accurate, policy-compliant AI responses
Develop a Multi-Agent Collaboration (MAC) system with the Strands SDK, featuring a supervisor agent orchestrating specialized sub-agents
Requirements

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Basic proficiency in Python programming, as you will use the Strands SDK and Streamlit framework to build the agentic loop
A fundamental understanding of AWS cloud services (such as S3, DynamoDB, and IAM) and basic Generative AI concepts like Large Language Models
Access to an AWS environment to provision Bedrock Knowledge Bases, OpenSearch collections, and database clusters
Description

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Are you ready to move beyond basic chatbots and build enterprise-ready, autonomous AI systems? This course provides a comprehensive blueprint for developingproduction-grade multi-agent Generative AI applications on AWS. Using a real-world "AnyCompany Coffee Shop" case study, you will learn how to design an AI Barista that doesn't just chat, but actually executes business logic.
We begin withStrategy and Ideation, where you will useStreamlit to model the financial impact and ROI of your AI agents, ensuring your projects deliver clear business value. From there, we dive into a robustData Strategy, teaching you how to leverage your existing data-includingAmazon DynamoDB for orders andAmazon Aurora for store operations-as a competitive differentiator.
The heart of the course focuses on the technical implementation ofMulti-Agent Collaboration (MAC) architecture. You will learn to use theStrands SDK to build aSupervisor Agent that orchestrates specialized sub-agents for Orders, Menu, Payments, Stores, and Promos. You will also masterRetrieval-Augmented Generation (RAG) by building Knowledge Bases usingAmazon Bedrock andAmazon OpenSearch Serverless to ensure your agents provide accurate, policy-compliant responses.
By the end of this course, you will have a deep understanding of theAgentic Loop, Harness and Loop Engineering-how models use tools and prompts to autonomously plan, reason, and take actions to solve complex tasks. This is the definitive guide for developers looking to master the next frontier of AWS cloud architecture and Generative AI.
Who this course is for

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This course is designed for AI Engineers, Cloud Architects, and Software Developers who have moved beyond simple prompts and want to build autonomous systems that can plan, reason, and use tools to solve complex tasks . It is also highly valuable for Technical Product Managers and Business Analysts who need to quantify the financial impact of AI agents through ROI modeling . If you are an IT professional tasked with connecting enterprise data-like order history in DynamoDB or store operations in Aurora-to Generative AI to improve customer experience or employee productivity, this course provides the production-grade blueprint you need
Homepage

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