Amazon AgentCore: Scale Your Agentic AI to Production

Deploy Agentic AI in Production with Amazon Bedrock AgentCore, AWS, and the OpenAI Agents SDK

What you'll learn
- Deploy an agentic AI application into production using Amazon Bedrock AgentCore on AWS, managing serverless runtime, scaling, and reliability.
- Implement authentication and identity management for agents to ensure secure access, user verification, and proper credentials in real‑world scenarios.
- Add both short‑term and long‑term memory to agents to improve context awareness, persistence, and user experience over time.
- Integrate external tools, APIs, and third‑party data sources via Gateways, and leverage the OpenAI Agents SDK for orchestrating tool usage.
- Monitor, observe, and debug your agentic systems using AgentCore’s observability features, log metrics, and performance dashboards in production.
- Understand best practices and design patterns for deploying generative AI systems securely and efficiently with Amazon AgentCore and Bedrock.

Requirements
- Familiarity with Python programming
- Experience in developing with agentic AI frameworks
- Basic familiarity with AWS and the AWS console
- Hands-on activities require an OpenAI account, an AWS account, and a Python development environment

Description
Tired of building AI prototypes that never make it to production?You’re not alone. Many engineers can build impressiveagentic AIdemos—but hitting a wall when trying to scale those systems into production is common.

This course solves that problem.

You’ll learn how to useAmazon Bedrock AgentCore—part ofAWS’scutting-edgegenerative AIstack—to deploy real, secure, scalable agent systems. You’ll take a workingOpenAI Agents SDKproject and transform it into a production-grade service, using AgentCore’s built-in memory, identity, tools, and observability features.

By the end of this course, you won’t just understandagentic AI—you’ll have deployed one.

What You’ll Learn

How to useAmazon AgentCoreto host your AI agents serverlessly in production

Add memory to your agents (short-term and long-term)

Handle user identity and secure authentication in agent workflows

Integrate real tools, APIs, and third-party data using Bedrock’s Gateways

Monitor and debug agents using AgentCore’s observability features

Build a complete hands-on agentic AI project using theOpenAI Agents SDK

Why Amazon AgentCore?

Amazon Bedrock AgentCoreprovides a serverless runtime purpose-built foragentic AI. It handles scaling, security, and tool integrations so you don’t have to. With first-class support inAWS, it’s the fastest way to take your generative AI project from experiment to enterprise.

Who This Course Is For

AI engineers and developers who’ve built agent prototypes—but haven’t shipped them

ML practitioners ready to operationalize generative AI

Software engineers looking to upskill inAWS AItools and infrastructure

Builders who want hands-on, project-based experience with agent systems in production

If you’ve been exploringagentic AIor theOpenAI Agents SDK, this course will show you how to make it real—on a secure, scalable production stack.

About the Instructor

Hi, I’mFrank Kane. I spent 9 years atAmazon and IMDb, where I helped build and lead the AI systems behind some of the most-visited websites on the planet.

Since leaving Amazon, I’ve taughtover one million studentsaround the world how to succeed in machine learning and data science through Sundog Education.

This course brings my real-world engineering experience at Amazon together with today’s most powerful agentic AI tools—so you can stop prototyping and start deploying.

What You'll Walk Away With

By the end, you’ll have:

A working, full-featured agentic AI system deployed withAmazon AgentCore

The confidence to scale, monitor, and maintain your own production agents

Practical experience that applies directly to your work or portfolio

Please Note:

Following along hands-on with the project in this course requires an OpenAI developer account and an AWS account, as well as a Python development environment. Total costs should not exceed a few dollars, or you can just watch the videos without incurring any cloud costs.

Who this course is for:
- AI / ML engineers, software developers, or cloud architects who need to take Ai prototypes into production
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