Overview
In this course, you’ll explore the core principles and strategies for designing Agentic AI systems using AWS services. You’ll learn how Agentic AI differs from traditional conversational systems, and how to use tools like Amazon Q, Kiro, Amazon Bedrock Agents, and Amazon Bedrock AgentCore to build autonomous, goal-driven solutions that solve real-world problems.
Activities
This course includes presentations, hands-on lab, and group exercises.
Prerequisites
We recommend that attendees of this course have:
- Generative AI Essentials or equivalent work experience
- Basic AWS knowledge and software development experience
Target Audience
This course is intended for:
- Software developers new to Agentic AI seeking foundational knowledge
- Technical professionals exploring AI capabilities and interested in core components and applications of agentic AI
- Development teams evaluating Agentic AI solutions and needing to differentiate between agent types
- AWS Users expanding into Agentic AI, including current users of Amazon Q Developer, Amazon Q Business, and Amazon Bedrock Agents
Learning Objectives
In this course, you will learn to:
- Summarize the evolution of Agentic AI and define what makes something 'agentic'
- Identify core components of agentic systems
- Distinguish between workflow, autonomous, and hybrid agents
- Compare AWS service options for Agentic AI
- Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro
- Explain Amazon Bedrock AgentCore and Amazon Bedrock Agents fundamentals
- Identify basic implementation patterns for Agentic AI
- Describe observability and interoperability patterns for production agentic AI systems
Course Outline
Module 1: From LLMs to Agents
- Understanding Large Language Models (LLMs)
- Innovations powering agents
- Evolution timeline from LLMs to Agents
Module 2: Exploring Agentic AI
- Understanding Agentic AI
- Types of AI agents
- Agentic AI applications
Module 3: Understanding Agentic AI Workflows
- Workflow patterns
- Amazon Bedrock flows overview
Module 4: Introducing Autonomous Agents
- How Autonomous Agents work
- ReAct
- ReWoo
- Multi-agent collaboration
- AWS Agentic AI solutions
Module 5: Amazon Q and Agentic Development Tools
- Amazon Q Developer
- Amazon Q Business
- Amazon Q in AWS Services
- Kiro: AI-powered IDE with spec-driven development
Module 6: Agentic AI with Amazon
- Amazon Bedrock Agents
- Amazon Bedrock AgentCore
- Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge
- Bases and Amazon Bedrock Guardrails
Module 7: Building DIY Solutions
- DIY solutions
- Observability and Monitoring
- Agent Interoperability
Module 8: Course Wrap-up
- Next steps and additional resources
- Course summary
Exams and Assessments
There are currently no exams or certifications associated with this course.
Hands-On Learning
- Discussion based sessions
- Demos
- Hands-on labs
Frequently asked questions
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QA online courses, also commonly known as distance learning courses or elearning courses, take the form of interactive software designed for individual learning, but you will also have access to full support from our subject-matter experts for the duration of your course. When you book a QA online learning course you will receive immediate access to it through our e-learning platform and you can start to learn straight away, from any compatible device. Access to the online learning platform is valid for one year from the booking date.
All courses are built around case studies and presented in an engaging format, which includes storytelling elements, video, audio and humour. Every case study is supported by sample documents and a collection of Knowledge Nuggets that provide more in-depth detail on the wider processes.
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When will I receive my certificate?
Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.