Building Multi-Agentic Workflows on AWS Bedrock + AgentCore

Name: Building Multi-Agentic Workflows on AWS Bedrock + AgentCore
Rating: 4.5 (238 reviews)

Create Multi-Agent Workflows, Deploy on AgentCore + Build Memory-Enabled Strands Agents + DynamoDB and Web Search Tools

Created byPatrik Szepesi

Last updated 3/2026

English

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
Add long-term memory with semantic and preference strategies to your Agents
Leverage AWS Bedrock for LLMs
Deploy agents to production on AgentCore Runtime
Create agents that remember users across sessions
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.
Understand short-term vs long-term agent memory
Understand the Pricing for Bedrock AgentCore Runtime
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.
Understanding the Pricing for Bedrock Agentcore Long and Short Term Memory
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.
Build web search agents with Strands and DuckDuckGo
Implement short-term memory for conversation tracking
Use lifecycle hooks to load and save agent memory
Build a personal assistant with Claude Haiku and live web search
AWS Strands Framework

Course content

12 sections • 71 lectures • 9h 32m total length

What the Course Covers6:15
Important Course Merger Update0:21

Introduction to AWS Bedrock Agents1:04
Creating the Restaurant Agent18:25
Configure the restaurant agent in AWS Bedrock, enable multi-agent collaboration for the supervisor agent, and set up a List restaurants lambda with city and fine dine parameters.
Creating our AWS S3 Bucket To Store Our Data4:10
Uploading Restaurant Data to AWS S30:47
Upload the restaurant CSV to an AWS S3 bucket, confirming the file is in the bucket and ready for the restaurant agent's Lambda function in the next video.
Creating an Action Group For Our Restaurant Agent13:51
Finishing Our Lambda Function for our Restaurant Agent14:18
Testing Our Restaurant Agent15:36

Creating and Testing The Supervisor agent9:38
Create and test a supervisor agent that orchestrates multi-agent collaboration with restaurant and accommodation collaborators on AWS Bedrock, enabling history sharing, precise routing, and final responses.
Explaining Agent Collaborators4:52
Multi Agent UI Enhancement, Timing Agents3:01
Observe how the enhanced UI in Bedrock supervisor demonstrates multi-agent collaboration with a trace timeline, revealing when the supervisor, restaurant, and accommodation agents run and for how long.

Serverless Invocation of the Supervisor Agent using AWS Lambda12:44
Learn to build a serverless workflow that invokes the supervisor agent via AWS Lambda and API Gateway, using Bedrock's runtime to pass input text and handle streaming responses.
Setting up AWS API Gateway to Deploy Our Worfklow Through the Internet3:38
Set up an HTTP API in AWS API Gateway (Oregon) to expose Bedrock agents workflow via a Lambda integration, enabling a get agent response route for backend calls or Postman.
Testing Our Endpoint Through The Internet with Postman5:58
Test an endpoint through Postman by routing via API gateway to a lambda that invokes the supervisor agent. Maintain history with a session ID and send text in the body.
Cleaning Up Resources2:40

What is AWS Bedrock Agent Core11:04
Accessing AgentCore via Sagemaker AI Setup1:35
Finishing SagemakerAI Setup3:13
AWS Bedrock Agent Core Architecture Diagram0:31
Creating our Test Agent15:48
Testing Our Agent5:40
Configuring the AgentCore Runtime9:31
Deploying the Agent to AgentCore8:59
Tracing the Agent Logs in CloudWatch for Observability16:31
Don't forget to shutdown the SagemakerAI Server0:33
Session Management for Agents8:21
Understanding AgentCore Sessions6:06
Lifecycle Management for AgentCore Sessions4:21
Cost Calculations for AgentCore Runtime3:17
Understanding Cost0:06

Requirements

Basic Python Programming

Description

Want to build AI applications where multiple agents collaborate, remember users, and run in production? This course takes you from multi-agent fundamentals to deploying intelligent, memory-enabled agents on AWS Bedrock and AgentCore.

You'll build a fully operational travel planner where Supervisor Agents coordinate tasks while Collaborator and Helper Agents handle database lookups, API calls, and travel preferences on your behalf. You'll also build a personal assistant agent with live web search powered by DuckDuckGo — capable of fetching real-time information and responding with up-to-date answers.

What You'll Learn:

Multi-Agent Design — When to break tasks into specialized agents, how to handle inter-agent communication, and how to ensure seamless collaboration
AWS Bedrock LLMs — Customize prompt templates, override parameters, and optimize AI output using foundation models
Serverless Deployment — Store data in S3, build with Lambda Action Groups, and deploy via API Gateway for live, scalable requests
AgentCore Runtime — What Amazon Bedrock AgentCore is and how to deploy and run agents at scale on purpose-built infrastructure
Web Search Agents — Build agents using the Strands framework with Claude Haiku that search the web in real time via DuckDuckGo
Short-Term Memory — Track conversation context within a session using AgentCore's get_last_k_turns
Long-Term Memory — Configure extraction strategies that automatically capture Semantic facts, User Preferences, and Session Summaries — so your agents remember users across sessions

By the End of This Course, You Will Be Able To:

Orchestrate Supervisor, Collaborator, and Helper Agents for real-world scenarios
Deploy agents on AgentCore Runtime with production-grade infrastructure
Build agents that search the web and respond with live information
Give agents short-term and long-term memory that persists across sessions
Deliver dynamic, personalized recommendations powered by multi-agent AI

Whether you're an aspiring AI developer or a seasoned engineer — this course gives you the hands-on skills to build agents that don't just respond, but remember, personalize, and improve over time. Join us and start building the next generation of AI with AWS Bedrock and AgentCore.

Who this course is for:

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.

Building Multi-Agentic Workflows on AWS Bedrock + AgentCore

What you'll learn

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Course content

Course Overview2 lectures • 7min

Multi Agentic Capstone Project2 lectures • 11min

Course Resources1 lecture • 1min

Setting Up Our AWS Account4 lectures • 15min

Architecture Design, Model Access and Quotas2 lectures • 15min

Creating the Restaurant Agent in AWS Bedrock7 lectures • 1hr 8min

Creating the Accommodation Agent in AWS Bedrock5 lectures • 49min

Multi Agentic Collaboration Using the Supervisor Agent in AWS Bedrock3 lectures • 18min

Deploying Our Multi Agentic Workflow and Cleaning Up Resources4 lectures • 25min

Deploying Agents with AWS Bedrock AgentCore15 lectures • 1hr 36min

Requirements

Description

Who this course is for: