Course Description

This course introduces the core concepts of Agentic AI, covering the evolution, key characteristics, and industry applications of AI agents. You will explore various agent models—from reactive to hybrid architectures—and understand how Generative AI has reshaped agent behavior and design.o mastering LangGraph, focusing on graph-based architectures for advanced AI agents.

Through comprehensive modules and practical exercises, you will:

  • Understand how AI agents differ from traditional software, their key characteristics, and their role in modern applications.

  • Explore reactive, deliberative, and hybrid models, including the impact of Generative AI on agent behaviors.

  • Learn the industry-proven patterns: Reflection, Tool Use, Planning, and Multi-Agent Collaboration to enhance adaptability and decision-making.

Course curriculum

  • 1
    Introduction to AI Agents and Agentic Design
    • Agentic AI Unleashing the Future of Autonomy
    • What is Agentic AI
    • Why Design Patterns
    • Overview of Design patterns
    • Course Handouts
    • Quiz
  • 2
    Agentic AI Reflection Pattern
    • What is the Reflection Pattern ?
    • Why use the Reflection Pattern
    • Key components of the Reflection Pattern Architecture
    • Real-world examples of the Reflection Pattern
    • Quiz
  • 3
    Tool Use Pattern
    • What is the Tool use Pattern in Agentic AI?
    • Why use the Tool Use Pattern
    • Key Components of the Tool Use Pattern Architecture
    • Real-world Examples of Tool Use Pattern
    • Quiz
  • 4
    Agentic AI Planning Pattern
    • What is the Planning Pattern?
    • Why use the Planning Pattern
    • Key components of the Planning Pattern Architecture
    • Real-world examples of the Planning Pattern
    • Quiz
  • 5
    Multi-Agent Pattern
    • What is the Multi-Agent Pattern in Agentic AI?
    • Why use the Multi-Agent Pattern
    • Key Components of the Multi-Agent Pattern Architecture
    • Quiz

Who Should Enroll

  • Developers, engineers, and AI practitioners looking to design and implement autonomous AI agents using structured design patterns.

  • Software architects and ML professionals eager to explore scalable, intelligent AI systems with multi-agent collaboration and advanced decision-making.

  • AI enthusiasts with foundational knowledge who want to deepen their expertise in agentic AI architectures and real-world applications.

About Instructor

Eleni Verteouri GenAI Tech Lead at UBS

Eleni Verteouri is a GenAI Tech Lead and Director at UBS with over a decade of impactful work in model development. As an AI guest lecturer at ETH Zurich and ZHAW School of Management and Law and a mentor at the Tenity Startup Incubator, Eleni has made significant contributions to shaping modern financial technologies. Her achievements have been recognized with the Forbes Cyprus 20 Women in Tech Award 2024. She holds a Master of Science in Quantitative Finance from ETH Zurich and a Master of Engineering in Electrical and Computer Engineering from the University of Patras.
About Instructor

FAQ

  • What is Agentic AI, and why is it important in system architecture?

    Agentic AI refers to AI systems that operate autonomously, adapt dynamically, and make independent decisions. In system architecture, Agentic AI enables scalable, self-improving, and interactive AI applications by leveraging structured design patterns.

  • What will I learn in this course?

    You will gain in-depth knowledge of Agentic AI design patterns, including reflection, tool use, planning, and multi-agent collaboration. The course also covers architectural principles and real-world applications of these patterns to build efficient, scalable AI systems.

  • Do I need prior experience with AI systems to take this course?

    A basic understanding of AI concepts and software development is recommended. Familiarity with AI frameworks and software design principles can be helpful, but the course provides foundational knowledge to get started.

  • How do design patterns enhance AI agent development?

    Design patterns provide a structured approach to building intelligent AI agents, enabling them to plan, interact with tools, self-reflect, and collaborate effectively. These patterns improve modularity, adaptability, and problem-solving efficiency in AI systems.

  • Will this course cover real-world applications of AI design patterns?

    Yes, the course includes practical case studies and real-world implementations to demonstrate how Agentic AI design patterns are applied in industries such as automation, analytics, and enterprise AI solutions.

  • Why are design patterns crucial for scalable AI architectures?

    Design patterns help standardize AI system development, allowing for modular, reusable, and efficient AI architectures. They enhance decision-making, scalability, and system robustness, making AI applications more adaptive and future-proof.