AWS Agentic AI Foundations

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Course 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.

Who Should Attend

  • 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

Course Objectives

    • 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 Bedrock

  • 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

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Class Dates & Times

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This is a 1-day class

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the availability or support of technology within the University of Maine System.