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 Agentic AI Design Patterns
    • Course Introduction
    • What are AI Agents
    • Why Design Patterns
    • Overview of Design Patterns
    • Quiz
    • Course Handouts
    • Reading Material
  • 2
    The Reflection Pattern
    • Introduction to the Reflection Pattern and its Core Components
    • Real-world Applications of the Reflection Pattern
    • Quiz
    • Reading Material
  • 3
    The Tool-Use Pattern
    • Introduction to the Tool-Use Pattern
    • Core Elements of the Tool Use Pattern
    • Real-world Applications of Tool Use Pattern
    • Quiz
    • Reading Material
  • 4
    The Planning Pattern
    • Introduction to the Planning Pattern
    • Static vs Reflective Planning in Agentic AI
    • Introduction to the ReAct Framework
    • Real-world Applications of Planning Patterns
    • Quiz
    • Reading Material
  • 5
    The Multi-Agent Pattern
    • Introduction to the Multi-Agent Pattern
    • Core Components of the Multi-Agent Pattern Architecture
    • Real-world Applications of Multi-Agent Patterns
    • Quiz
    • Reading Material
  • 6
    Best Practices and Key Takeaways
    • Best Practices to Build Effective Agentic AI Systems
    • Reading Material

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

Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya

Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include “Top 10 Data Scientists in India, 2020”, “40 under 40 Data Scientists, 2021”, “Google Developer Expert in Machine Learning, 2019”, “Top 50 AI Thought Leaders, 2022” , “Google Cloud Champion in Cloud AI/ML, 2022”, “40 under 40, 2025” and other numerous global accolades.

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.