Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Mastering Amazon Bedrock AgentCore: Bring your PoC to Prod!
Rating: 4.6 out of 5(234 ratings)
2,055 students

Mastering Amazon Bedrock AgentCore: Bring your PoC to Prod!

Learn how to build AI Agents on AWS and how to follow best practices to make your AI Agents ready for production.
Created byPuria Izady
Last updated 4/2026
English
English [Auto],Spanish [Auto],

What you'll learn

  • Understand the requirements in production for agentic AI workloads
  • Learn the fundamental components of Amazon Bedrock AgentCore
  • Deploy AI Agents for scale, performance, security and reliability
  • Hands-On Development of Agentic solution with Strands SDK, MCP, Rest APIs, OAuth and Memory

Course content

9 sections24 lectures5h 47m total length
  • What is Amazon Bedrock AgentCore?20:12

    Discover how Amazon Bedrock Agent Core maps POC pain points to seven components—runtime, gateway, memory, identity, tools, and observability—to enable scalable, secure production deployments.

  • Lab 0: Setup AWS Account Free-Tier, AWS Builders ID and Kiro2:34

    Set up your AWS account and AWS Builder ID to access the free tier and credits, then install Amazon Key Row and log into Hero for free coding help.

  • Lab 1: Clone repository and setup IAM Users in AWS Account4:01

    Clone the Mastering Amazon Bedrock Agent Core repo from GitHub and set up a programmatic AWS IAM user with access keys for Cairo and local workflows.

  • Lab 2: Setup Python libraries with UV and connect to AWS boto34:35

    Set up a Python environment with UV in Cairo, install dependencies, and configure the AWS SDK (boto3) to connect to AWS and run code for live flight and weather data.

  • Lab 3: Introduction to Amazon Kiro6:27

    Explore Cairo, an AI coding companion that supports quick chat and spec-driven planning, enabling MCP server setup for AWS docs and strands agent docs and Google Drive and Calendar workflows.

Requirements

  • Python
  • GenAI Basics
  • AWS Basics

Description

This course contains the use of artificial intelligence.


Master Amazon Bedrock AgentCore and build production-ready AI agents in just 6 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

  • AgentCore Evaluation – Measure, validate, and benchmark your agents with production-grade evaluation workflows

  • AgentCore Policy – Apply fine-grained guardrails to control what your agents can and can't do in production

Hands-On Learning: 10+ 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.

Lab 8: AgentCore Observability – Instrument your agents with CloudWatch GenAI Dashboard and OpenTelemetry for full production visibility

Lab 9: AgentCore Evaluation – Run evaluations on your agents to measure quality, catch regressions, and validate behavior before shipping

Lab 10: AgentCore Policy – Deploy fine-grained policies that control agent behavior in production environments

Who this course is for:

  • Cloud Architects
  • AI Beginners
  • Software Developers
  • Product Managers
  • Software Architects
  • Cloud Engineers