AI Agent Development with LangGraph, AutoGen, and Semantic Kernel

The transition from simple chatbots to autonomous AI agents is reshaping how we solve complex problems using large language models. This course provides a comprehensive guide to building intelligent systems that can reason, use tools, and work together to achieve specific goals. You will gain the skills to move beyond basic prompts and create sophisticated, adaptive AI workflows that handle real-world complexity. By following this written program, you will transform from a beginner into a developer capable of orchestrating multi-agent environments. You will learn to manage conversation states, implement feedback loops, and connect AI models to external data and functions. What you'll learn: - Understand the fundamental architecture of autonomous agents and agentic reasoning loops - Build complex, stateful workflows using LangGraph to manage cyclical processes and logic - Implement multi-agent collaboration patterns using the AutoGen framework for task delegation - Apply Semantic Kernel to integrate AI capabilities into structured software environments - Integrate human-in-the-loop patterns to ensure safety and accuracy in agent-driven decisions - Configure tool-calling and function-execution capabilities for real-world system interactions - Practice designing agentic systems that utilize Retrieval-Augmented Generation (RAG) for grounded responses The course begins with foundational definitions and the core principles of agentic design before guiding you through the specific implementation patterns of the industry's leading frameworks. You will explore how to structure agent memory, manage multi-turn conversations, and debug complex interactions. This course is designed for developers and AI enthusiasts who are new to agentic frameworks; no prior experience with LangGraph or AutoGen is required. Start building intelligent agents that go beyond simple chat interactions today.