Building Multi-Agentic Ai Workflows On Aws Bedrock

Harness LLMs, Orchestrate Multi-Agent Workflows, and Build Your Own Production Ready Travel Agent Powered By AI

What you'll learn

Understand and Implement Multi-Agent Workflows

Deploy Multi-Agent Workflows with AWS- using Bedrock, Lambdas, API Gateway, S3 and many more

Leverage AWS Bedrock for LLMs

Implement Multi-Agent Collaboration

Deploy Production AI Systems – Set up a scalable AI architecture using AWS Lambda and API Gateway.

Create Action Groups in AWS Lambda – Build and manage action groups for AI decision-making in serverless environments.

Build AI-Powered Travel Agents – Design an intelligent travel assistant that can provide accommodation and restaurant recommendations.

Implement API Gateway for External Access – Expose your AI travel agent to the web using AWS API Gateway.

Optimize AI Requests with API Rate Limits – Learn how to manage API request limits and prevent excessive usage costs.

Implement Logging and Monitoring – Track AI model performance and monitor API usage with AWS CloudWatch.

Understand the Role of Supervisor Agents – Learn how supervisor agents manage and coordinate tasks efficiently.

Deploy an End-to-End AI System – Take your travel agent from concept to production in a real-world AWS environment.

Fine-Tune AWS Bedrock LLM Responses – Adjust system parameters to improve the accuracy and relevance of travel recommendations.

Design Scalable Serverless Applications – Learn best practices for scaling AI-driven serverless applications in AWS.

Requirements

Basic Python Programming

Description

Do you want to harness the power of multi-agentic workflows to create cutting-edge AI applications—and deploy them at scale? This course is your gateway to building a fully operational, production-ready travel planner on AWS Bedrock, where multiple agents collaborate to deliver personalized, real-time recommendations. You’ll see how Supervisor Agents coordinate the flow of tasks, while Collaborator and Helper Agents do the heavy lifting—making database lookups, handling API calls, and processing travel preferences on your behalf. By structuring your AI in this agent-centric way, you’ll develop a scalable, modular system that adapts smoothly to complex, real-world scenarios.We begin with the fundamentals of multi-agentic design—when to break tasks into specialized agents, how to handle inter-agent communication, and ensuring seamless collaboration for lightning-fast responses. Next, we’ll dive into AWS Bedrock’s Large Language Models (LLMs), showcasing how to customize prompt templates, override default parameters, and optimize your AI’s output for user queries. You’ll learn how to store key travel data in Amazon S3 and build a serverless application layer using AWS Lambda functions—Action Groups—to keep your AI workflow lightweight and cost-effective. Finally, we’ll demonstrate how to go production-ready by deploying via AWS API Gateway, providing a robust interface that can serve live requests from anywhere in the world with built-in scalability and security.By the end of this course, you’ll have a production-grade, multi-agentic application capable of automatically looking up database records, making API requests, and delivering dynamic travel recommendations. Whether you’re an aspiring AI developer or a seasoned engineer, you’ll gain the hands-on skills to orchestrate Supervisor, Collaborator, and Helper Agents for real-world, enterprise-scale solutions. Join us and start building the next generation of AI with AWS Bedrock—all in a fully production-ready environment!

Overview

Section 1: Introduction

Lecture 1 What We Are Building Part 1

Lecture 2 What We Are Building Part 2

Section 2: Course Resources

Lecture 3 Access The Course Resources Here

Section 3: Setting Up Our AWS Account

Lecture 4 Signing In to AWS

Lecture 5 Creating and IAM User in AWS

Lecture 6 Accessing Our AWS Environment with our IAM User

Lecture 7 What to Do If your AWS Account Gets Hacked

Section 4: Architecture Design, Model Access and Quotas

Lecture 8 Architecture Diagram of Our Multi Agentic Workflow

Lecture 9 LLM Model Access, API Rate Limits, Quotas, and AWS Regions

Section 5: Creating the Restaurant Agent in AWS Bedrock

Lecture 10 Introduction to AWS Bedrock Agents

Lecture 11 Creating the Restaurant Agent

Lecture 12 Creating our AWS S3 Bucket To Store Our Data

Lecture 13 Uploading Restaurant Data to AWS S3

Lecture 14 Creating an Action Group For Our Restaurant Agent

Lecture 15 Finishing Our Lambda Function for our Restaurant Agent

Lecture 16 Testing Our Restaurant Agent

Section 6: Creating the Accommodation Agent in AWS Bedrock

Lecture 17 Setting Up The Accommodation Agent

Lecture 18 Uploading Our Hotel and Airbnb Data to AWS S3

Lecture 19 Creating The Lambda Function Action Group For The Accommodation Agent

Lecture 20 Finishing The Accommodation Agent

Lecture 21 Testing The Accommodation Agent

Section 7: Multi Agentic Collaboration Using the Supervisor Agent in AWS Bedrock

Lecture 22 Creating and Testing The Supervisor agent

Lecture 23 Explaining Agent Collaborators

Lecture 24 Multi Agent UI Enhancement, Timing Agents

Section 8: Deploying Our Multi Agentic Workflow and Cleaning Up Resources

Lecture 25 Serverless Invocation of the Supervisor Agent using AWS Lambda

Lecture 26 Setting up AWS API Gateway to Deploy Our Worfklow Through the Internet

Lecture 27 Testing Our Endpoint Through The Internet with Postman

Lecture 28 Cleaning Up Resources

AI and Machine Learning Engineers who want to leverage Large Language Models (LLMs) and advanced agent orchestration techniques.,Cloud Architects and Engineers looking to design robust, serverless AI architectures on AWS with production-grade scalability.,Data Scientists interested in expanding their skill set to include multi-agent systems and real-time deployment in the cloud.,DevOps and MLOps Professionals eager to master end-to-end automation for AI applications, from data storage to API endpoints.,Software Developers keen on integrating conversational AI into their applications for dynamic, user-friendly experiences.,Tech Enthusiasts and Entrepreneurs who see the potential of AI-driven services and want a hands-on approach to rapid prototyping and scaling.