
Create a sample data set from all data lines, store it in a vector store with OpenAI, and run a multi-agent setup (data, guardrail, calculator) in preparation for production deployment.
Install bedrock agent core and starter toolkit, then configure the data_agent_core.py entry point and requirements. Run the local service on port 8080 and test a post to /invocations.
Enable observability via CloudWatch transaction search and structured logs per the Bedrock Agent Core guide. Deploy the agent with Agent Core launch and requirements.txt packaging to a serverless endpoint.
Run and test the AgentCore app using the Starter Toolkit, configuring the OpenAI API key as an environment variable to deploy in AWS and verify results via CloudWatch logs.
Explore agent core observability in CloudWatch, reviewing invocations, latency, token counts, and errors. Drill into sessions and traces to optimize performance with Bedrock Agent Core metrics and runtime insights.
Explore how agent cores enable large language models to use memory, tools, and planning to act autonomously, and how to deploy production-ready multi-agent patterns.
Secure production access with inbound and outbound OAuth using Cognito and Agent Core, including a Cognito user pool, bearer tokens, and a requires access token decorator for external calls.
Set up inbound OAuth authentication for agent core with Amazon Cognito, automating user pool, app client, test user, and bearer token retrieval in 60 minutes.
Explore how agentic AI systems manage short-term and long-term memory at scale, using Agent Core memory to store memory records and integrate with the OpenAI agents SDK for scalable memory.
Integrate the code interpreter and browser tools from Agent Core into your agent, wrapping them for the OpenAI agent SDK and showcasing a Python execution workflow.
Demonstrates importing an Amazon Bedrock agent into Agent Core, converting it to strands or lang graph code, then testing, deploying, and extending it, using S3 vectors for knowledge retrieval.
Use the agent core gateway to wrap external tools and rest api endpoints as mcp-like endpoints, handling authentication and semantic tool selection for scalable ai agents.
Evaluate agent performance with metrics like faithfulness, helpfulness, and correctness using prompt templates and custom evaluators across session, trace, or span levels.
Tired of building AI prototypes that never make it to production?
You’re not alone. Many engineers can build impressive agentic AI demos—but hitting a wall when trying to scale those systems into production is common.
This course solves that problem.
You’ll learn how to use Amazon Bedrock AgentCore—part of AWS’s cutting-edge generative AI stack—to deploy real, secure, scalable agent systems. You’ll take a working OpenAI Agents SDK project 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 understand agentic AI—you’ll have deployed one.
What You’ll Learn
How to use Amazon AgentCore to 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 trust in your agent with Policies and Evaluators
Build a complete hands-on agentic AI project using the OpenAI Agents SDK
Why Amazon AgentCore?
Amazon Bedrock AgentCore provides a serverless runtime purpose-built for agentic AI. It handles scaling, security, and tool integrations so you don’t have to. With first-class support in AWS, 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 in AWS AI tools and infrastructure
Builders who want hands-on, project-based experience with agent systems in production
If you’ve been exploring agentic AI or the OpenAI Agents SDK, this course will show you how to make it real—on a secure, scalable production stack.
About the Instructor
Hi, I’m Frank Kane. I spent 9 years at Amazon 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 taught over one million students around 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 with Amazon 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.