This course is designed to help you go beyond basic AI demos and learn how modern AI agents are actually built inside the AWS ecosystem. You will work through real business-style scenarios while building RAG systems, AI agents, guardrails, Action Groups, Lambda integrations, and multi-agent workflows step by step.

What makes this course different than others?

Complete guide.
This is not a crash course that only shows a few demos inside the AWS console. This course is designed to take you from understanding Amazon Bedrock fundamentals to building fully functional agentic AI systems with RAG, guardrails, action groups, Lambda integrations, and multi-agent collaboration.

You learn the architecture behind modern AI systems and then build them step by step.

Built around a real business project.
The course revolves around UnicornsX, a fictional company with inventory systems, operational policies, employee training guides, and business workflows.

Throughout the course, you build AI agents that solve real business problems using real system architecture patterns.

Hands-on from start to finish.

You will:

• Build RAG agents

• Connect knowledge bases

• Configure S3 document storage

• Create guardrails

• Build Action Groups

• Integrate Lambda functions

• Design multi-agent workflows

• Test and debug agents

Built for both beginners and developers.

New to coding? You can still build powerful agents directly inside the Bedrock console using guided walkthroughs and downloadable resources.

Already a developer? You will go deeper with Lambda functions, Boto3 integrations, action routing, debugging, and infrastructure design.

What is Amazon Bedrock?

Amazon Web Services Bedrock is AWS’s platform for building generative AI applications using foundation models from providers such as Anthropic, Meta, Mistral AI, and Amazon Titan.

Bedrock gives developers access to:

• Foundation models

• AI agents

• Knowledge bases

• Guardrails

• Model orchestration

• Tool integrations

• Serverless AI workflows

All within the AWS ecosystem.

Why Amazon Bedrock?

Enterprise standard - Most large companies already run on AWS. Bedrock is quickly becoming where enterprises build production AI agents.

Model agnostic - Use Claude today, switch to another model tomorrow. Your architecture stays flexible.

All-in-one AI platform - models, orchestration, knowledge bases, agents, and guardrails all live inside the same ecosystem.

What is this course all about?

This course will take you from knowing little or nothing about Amazon Bedrock to confidently building AI agents, RAG systems, and multi-agent architectures on AWS.

By the end of this course, you will be able to:

• Navigate Amazon Bedrock confidently

• Select foundation models for different AI tasks

• Build RAG-powered AI agents

• Connect S3 buckets and knowledge bases

• Create and apply guardrails

• Build Action Groups with Lambda integrations

• Design agents that use external business logic

• Create multi-agent collaboration systems

• Test, debug, and improve AI workflows

Course Overview

• Introduction - Understand the course roadmap, how AI agents connect to external infrastructure, and what you will build throughout the course.

• Amazon Bedrock Foundations for Agent Builders - Learn the fundamentals of Amazon Bedrock, AWS permissions, foundation models, and how to connect external environments like Google Colab using Boto3.

• Meet Your Course Company: The Course Project - Explore the fictional UnicornsX company, its business workflows, inventory systems, and internal documentation used throughout the course.

• Build Your First Bedrock Agent with RAG - Design and build a RAG-powered AI agent using S3, Bedrock Knowledge Bases, and retrieval-based workflows.

• Guardrails in Your RAG Agent - Learn how to create and apply guardrails that help control unsafe, restricted, or unwanted AI responses.

• Inventory Agents Using Action Groups - Build AI agents that interact with external logic and calculations using Action Groups and AWS Lambda integrations.

• Multi-Agents Collaboration: Inventory + RAG - Create and test multi-agent systems where agents collaborate through orchestration and routing workflows.

• Conclusions - Wrap up the course by reviewing everything you built and exploring where to go next with Amazon Bedrock and AI agent development.