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Training
AWS-AAIF: Agentic AI Foundations
Course Information
Course Name
AWS-AAIF: Agentic AI Foundations
Duration
1 Day
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
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.
Overview
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.
Audience Profile
Audience Profile
This course is intended for:
- Software developers new to Agentic AI seeking foundational knowledge and practical implementation skills
- 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
Prerequisities
Prerequisites
We recommend that attendees of this course have:
- Generative AI Essentials or equivalent work experience
- Basic AWS knowledge and software development experience
At Course Completion
At Course Completion
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: goals, memory, tools, and environment
- Distinguish between workflow, autonomous, and hybrid agents
- Compare AWS service options for Agentic AI (Specialized, Managed, and DIY approaches)
- Describe capabilities and use cases of Amazon Q Developer, Amazon Q Business, and Kiro
- Explain Amazon AgentCore and Amazon Bedrock Agents core functionalities
- Identify basic implementation patterns for Agentic AI
- Describe observability and interoperability patterns for production agentic AI systems
Course Outline
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
- Demo: Amazon Bedrock Flows
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
- Demo: Amazon Q
Module 6: Agentic AI with Amazon Bedrock
- Amazon Bedrock Agents
- Amazon Bedrock AgentCore
- Demo: Amazon Bedrock Agents
- 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
