Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
6 Hour(s) 34 Minute(s)
Language
English
Taught by
Patrik Szepesi
Rating
Course Overview
Create Multi-Agent Workflows, Deploy on AgentCore + Build Memory-Enabled Strands Agents + DynamoDB and Web Search Tools
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.
Course Content
- 11 section(s)
- 59 lecture(s)
- Section 1 Course Overview
- Section 2 Multi Agentic Capstone Project
- Section 3 Course Resources
- Section 4 Setting Up Our AWS Account
- Section 5 Architecture Design, Model Access and Quotas
- Section 6 Creating the Restaurant Agent in AWS Bedrock
- Section 7 Creating the Accommodation Agent in AWS Bedrock
- Section 8 Multi Agentic Collaboration Using the Supervisor Agent in AWS Bedrock
- Section 9 Deploying Our Multi Agentic Workflow and Cleaning Up Resources
- Section 10 Deploying Agents with AWS Bedrock AgentCore
- Section 11 Adding Short and Long Term Memory To Bedrock Agents via Bedrock AgentCore
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
Skills covered in this course
Reviews
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AAvinash Patil1
Good Content
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RReza Haghi
It is very good and informative for beginners
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MMike Russo
Other than the fact that AWS rapidly changes and some of the screens and steps were minutely different, the message was clear and I learned something valuable. I like that it was small enough to digest in a day, but robust enough. Even got to learn a little about Lambdas, API Gateway, S3, etc. I will say that I used Google Antigravity to automate dealing with the permissions via aws cli, otherwise I'd probably break my stuff.
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VVisanu Mongsaithong
Good detailed instructions. Need more help with permissions and access. Over all very good lessons. Thank you.
