AWS Agentic AI Lab Series

Amazon Web Services via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

14 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Learn Agentic AI on AWS by building, deploying, and securing agentic AI applications on AWS through 10 comprehensive hands-on labs. Through these labs, learners gain practical skills in agent development, multi-agent orchestration, workflow automation, and production-ready implementation using Amazon Bedrock, Amazon Bedrock AgentCore, DevOps Agent, Kiro, Strands SDK and Amazon Quick.

Syllabus

Welcome to the Course

Welcome to this comprehensive hands-on learning journey into AWS Agentic AI services. This program is designed to give you practical experience with cutting-edge AI technologies through 12 carefully structured labs. Whether you're new to AI or looking to expand your cloud computing skills, these labs will provide you with the foundational knowledge and hands-on practice needed to work with AWS's Agentic AI services.

Create an HR Agent with Amazon Quick

This lab provides experience working with Amazon Quick to create spaces and custom chat agents.

Automate HR Onboarding with Amazon Quick Flows and Research

In this lab, you use Amazon Quick to automate an HR employee onboarding workflow. You create Quick spaces populated with HR policy documents, conduct AI-guided research on remote work best practices using Amazon Quick Research, build automated flows from natural language conversations, integrate Quick with an external HR API using an OpenAPI action connector, and design a multi-step onboarding flow that creates employee records, sends welcome emails, and generates IT setup tickets.

Structured Approach to AI coding with Spec-Driven Development on Kiro

This lab provides hands-on experience using both Amazon Q Developer and Amazon Kiro in a single IDE experience.

Security posture assessment and remediation using the Kiro CLI

This lab provides practice improving the security posture of a DevOps deployment using the Kiro CLI.

Enhance and Scale Agents with Amazon Bedrock AgentCore

This lab shows a typical journey faced by agent developers when moving from agent prototype, built using Strands SDK, to production agents using Amazon Bedrock AgentCore.

Deploying Intelligent Agents with Amazon Bedrock AgentCore Runtime

In this lab, you build AI agents that understand natural language, use custom tools, and coordinate with other agents to solve complex financial problems using Amazon Bedrock and Strands SDK. By the end, you will have deployed a production-ready multi-agent system using Amazon Bedrock AgentCore.

Creating an AWS DevOps AI Agent with the Strands Agents SDK

Learners will work to create an AWS management assistant with Strands Agents SDK. The core Bedrock model will enable general reasoning, while Strands tools will allow analyzing local files, running python scripts, searching the web, and taking actions on AWS. At the end all the agents they've created will be used as tools in a multi-agent architecture.

Setting Up and Investigating an Incident with AWS DevOps Agent

In this lab, you create an AWS DevOps Agent Space, start an investigation from an active CloudWatch alarm, and use DevOps Agent to identify the root cause of a DynamoDB throttling issue and apply a mitigation plan.

Build Multi-Agent Collaboration using Amazon Bedrock

This lab focuses on implementing multi-agent collaboration for AnyCompany's customer service request system using Amazon Bedrock and AWS services.

Design and Implement Autonomous Agents with Amazon Bedrock APIs

This lab guides learners through designing and implementing autonomous agents using Amazon Bedrock. Learners will create agents capable of independent decision-making, task execution, and problem-solving while maintaining alignment with business objectives. The lab emphasizes autonomous behavior configuration and complex task handling. This lab will focus on building the components using Bedrock APIs through a Jupyter notebook.

Secure Your Agentic Applications Using Amazon Bedrock Guardrails

This lab teaches learners how to implement security guardrails for agentic AI applications using AWS services. The lab focuses on practical implementation of security controls, monitoring, and governance for AI agents interacting with AWS services. Learners will configure guardrails to ensure secure, controlled, and auditable AI agent behaviors. This lab will focus on building the components using Amazon Bedrock APIs through an Amazon SageMaker Jupyter notebook.

Resolving Application Failures and Automating Investigations with AWS DevOps Agent

In this lab, you use AWS DevOps Agent to investigate and resolve three application failures across serverless, compute, and database services. You fix an S3 bucket policy error, review EC2 scaling and RDS connection recommendations, and configure webhook automation so CloudWatch alarms automatically trigger DevOps Agent investigations.

Course Wrap Up

In this lab series, you've gained hands-on experience working with AWS Agentic AI services. Next, take the feedback survey to let us know your understanding of the topics covered and your suggestions for this course.