This course contains the use of artificial intelligence. Master Amazon Bedrock AgentCore and build production-ready AI agents in just 4 hours. This comprehensive course takes you from zero to deploying intelligent, multi-agent systems that integrate with real enterprise tools—all using AWS’s newest agentic AI platform. Whether you’re an enterprise developer rushing to deploy AI agents in production, or a seasoned AWS engineer transitioning into agentic AI, this course gives you everything you need to build sophisticated AI systems that actually ship to production.
What You’ll Learn – Foundation & Core Services:
- AgentCore Quick Start – Deploy your first intelligent agent in 15 minutes using Amazon Bedrock
- AgentCore Runtime Integration – Master ANY agent framework: Strands, LangGraph, CrewAI, or custom Python implementations
- AgentCore Gateway – Connect agents to MCP servers, third-party APIs, and internal tools with secure credential management
- AgentCore Memory – Implement conversation history, long-term memory, and context-aware agents that remember user preferences
- AgentCore Identity – Handle OAuth flows, API key management, and IAM integration for secure, multi-user agent systems
- AgentCore Observability – Leverage the Amazon CloudWatch GenAI Observability Dashboard and integrate with 3rd Party tools via OpenTelemetry
- AgentCore Code Interpreter – Let agents write and execute Python code dynamically for data analysis and computation
- AgentCore Browser Tools – Enable agents to navigate websites, extract data, and interact with web applications autonomously
Hands-On Learning: 8+ Production Labs
This isn’t just lectures—you’ll build real applications through comprehensive, step-by-step labs:
- Lab 0: Setup your AWS Account, Amazon Kiro and use the AWS Free Tier
- Lab 1: AgentCore Runtime – Deploy your first Bedrock AgentCore Runtime agent with Strands SDK and Claude Sonnet, test locally, and understand the core architecture.
- Lab 2: Gateway – Connect your agent to real-world Weather, Flight and Exchange rate API sources using MCP and AgentCore Gateway with API Key authentication.
- Lab 3: Memory – Build a customer service agent with conversation history, user profile memory, and context-aware responses across sessions.
- Lab 4: Identity & OAuth – Implement secure 3LO OAuth agents with Google OAuth to create documents in your Google Drive, credential management, and per-user data isolation.
- Lab 5: Code Interpreter Tools – Create a data analysis agent that writes Python code, generates visualizations, and performs statistical analysis on user data.
- Lab 6: Browser Tools – Build a research agent that navigates websites, extracts information, and compiles reports automatically.
- Lab 7: Integrate everything into one Agent – Create one AgentCore Runtime Agent that connects to Gateway with all MCP Tools for weather, flight and exchange rate.
