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
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.
