In this module, you’ll explore foundational concepts behind agentic frameworks and multi-agent systems, learning their role in AI application design. You’ll then examine essential design patterns that help structure AI workflows into modular and maintainable systems. Through hands-on labs using LangGraph, you'll gain experience implementing core workflow patterns that serve as building blocks for more complex AI solutions.

This module introduces you to CrewAI and its core components, including agents, tasks, and crews. Through instructional videos and hands-on labs, you’ll learn to structure a CrewAI application, generate structured outputs, and extend capabilities with custom tools. You’ll gain practical experience by incrementally building CrewAI workflows and combining key features in an applied lab.

In this module, you’ll be introduced to two alternative agentic frameworks for building structured multi-agent AI applications: IBM’s BeeAI and AG2 (AutoGen). Through guided videos and hands-on labs, you’ll explore BeeAI’s architecture for creating agents and workflows, integrating tools, and managing memory. You’ll also examine AG2’s core components and learn how to configure multi-agent conversations using different patterns. By the end of the module, you will be able to implement basic agents using BeeAI and design structured, multi-agent conversations with AG2 for use cases like healthcare chatbots.