Course Overview
In this course, you’ll explore the core principles and strategies to consider when designing Agentic AI systems on AWS. You’ll learn how Agentic AI differs from traditional conversational systems, and how using agents helps build autonomous, goal-driven solutions that solve real-world problems.
Who should attend
This course is intended for:
- Software developers new to Agentic AI seeking foundational knowledge
- Technical professionals exploring AI capabilities who are interested in core components and applications of agentic AI
- Development teams evaluating Agentic AI solutions and needing to differentiate between agent types
Prerequisites
We recommend that attendees of this course have:
- Generative AI Essentials or equivalent work experience
- Basic AWS knowledge and software development experience
Course 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
- Identify basic implementation patterns for Agentic AI
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Course Content
M1: Exploring Agentic AI
This course introduction provides anstandard level of understanding for students in the course.
- What is an AI agent?
- Understanding agentic AI
- Types of AI agents
- Agentic AI applications
M2: Large Language Models (LLMs)
Discuss the benefits and challenges of using agents in your workflows with LLMs.
- What are LLMS?
- Understanding LLMs
- How are LLMs trained?
- LLM capabilities
- Challenges associated with LLMs
- Limitations of LLMs
- Fallacy of composition
M3: Innovations powering agents
Explore factors shaping agent development and its breadth of capabilities.
- Innovations shaping AI agents
- How AI-software integration works
- How chain of thought reasoning works
- Impact of multimodal inference
- Multi-agent collaboration
- Agentic frameworks to build agents
M4: Evolution timeline
Learn how long each phase is to take your agentic AI pipelines to production.
- Workflows to go from LLMs to agents
- Evolution of gen AI models
- Differences and similarities between assistants, agents, and AI systems
M5: Knowledge check + summary
Review what was learned in the course and advise on the next steps.
- Quiz questions
- Course summary
- Next steps and additional resources
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