Agentic AI Foundations
Field | Description / Template |
|---|---|
Purpose | To introduce the fundamental principles of autonomous AI, teaching learners how to transition from static chatbots to goal-driven systems using AWS managed services and specialized tools. |
Audience | Developers and technical teams who are new to agentic architecture but familiar with general generative AI concepts. |
Role | Software Developers, AI Researchers, Technical Leads, and AWS Solution Architects. |
Domain | Agentic AI / AI Orchestration / Cloud Automation. |
Skill Level | Fundamental |
Style | Conceptual and strategic, focusing on the evolution of AI agents, architectural patterns, and the selection of appropriate AWS service models (Specialized vs. Managed vs. DIY). |
Duration | 1 Day |
Related Technologies | Amazon Bedrock Agents, Amazon Bedrock AgentCore, Amazon Q (Developer & Business), Kiro. |
Course Description
Agentic AI Foundations is designed to provide a solid baseline for building autonomous systems on AWS. In this course, you will explore the core principles that separate Agentic AI from traditional conversational systems. You will learn how to design goal-driven solutions that can interact with their environment and use tools to solve real-world problems. By exploring the spectrum of AWS offerings—from specialized tools like Amazon Q to the managed capabilities of Amazon Bedrock Agents—you will gain the strategic insight needed to select the right approach for your organization's AI journey.
Who is this course for
This course is intended for:
Software Developers seeking a foundational understanding of agentic "memory, goals, and tools."
Technical Professionals exploring the practical applications and core components of autonomous systems.
Development Teams evaluating different agent types (workflow, autonomous, or hybrid) for upcoming projects.
AWS Users already utilizing Amazon Q or Bedrock who want to expand their expertise into agentic orchestration.
Course Objectives
Evolution & Definition: Summarize the evolution of AI and define the specific characteristics that make a system "agentic."
Core Components: Identify and explain the four pillars of agents: Goals, Memory, Tools, and Environment.
Agent Taxonomy: Distinguish between workflow-based, fully autonomous, and hybrid agent models.
Service Comparison: Compare AWS service options including Specialized, Managed, and "Do-It-Yourself" (DIY) approaches.
Tool Mastery: Describe the capabilities of Amazon Q Developer, Amazon Q Business, Kiro, and the functionalities of Amazon Bedrock Agents.
Patterns for Production: Identify basic implementation patterns and describe observability and interoperability requirements for production-grade systems.
Prerequisites
Required: Completion of Generative AI Essentials or equivalent industry experience.
Technical Background: Basic knowledge of AWS infrastructure and general software development experience.
Course outline
Section 1: From LLMs to Agents
Understanding Large Language Models (LLMs)
Innovations powering agents
Evolution timeline from LLMs to Agents
Section 2: Exploring Agentic AI
Understanding Agentic AI
Types of AI agents
Agentic AI applications
Section 3: Understanding Agentic AI Workflows
Workflow patterns
Amazon Bedrock flows overview
Demo: Amazon Bedrock Flows
Section 4: Introducing Autonomous Agents
How Autonomous Agents work
ReAct
ReWoo
Multi-agent collaboration
AWS Agentic AI solutions
Section 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
Section 6: Agentic AI with Amazon Bedrock
Hands-on lab: Explore Amazon Bedrock Agents integrated with Amazon Bedrock Knowledge Bases and Amazon Bedrock Guardrails
Amazon Bedrock Agents
Amazon Bedrock AgentCore
Demo: Amazon Bedrock Agents
Section 7: Building DIY Solutions
DIY solutions
Observability and Monitoring
Agent Interoperability