AI Agents with LangGraph, Semantic Kernel, and AutoGen

Packt via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours/week
Sessions

Self-Paced
Level

Intermediate

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Overview

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This specialization features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the specialization. In this specialization, you'll learn to build autonomous AI agents using LangGraph, Semantic Kernel, and AutoGen. You'll master AI agent architecture and create agents that process complex queries, maintain state, and integrate human feedback for decision-making. It begins with an introduction to AI agents, setting up your environment, and building your first agent using LangGraph. You’ll automate decision-making processes and work with complex queries through demos. You’ll explore Semantic Kernel SDK, integrating Large Language Models (LLMs) and generative AI into your applications. You'll learn to build intelligent agents and chat applications for business solutions. You'll develop and deploy multi-agent systems using AutoGen, exploring agent types, multi-agent conversations, human feedback integration, and advanced tools like sequential, nested, and group chats. Practical projects will help you apply these concepts to real-world scenarios. This specialization is designed for developers and AI enthusiasts with basic programming knowledge and is of intermediate difficulty. By the end, you will be able to build sophisticated AI agents using LangGraph, Semantic Kernel, and AutoGen, and apply generative AI to real-world challenges.

Syllabus

Course 1: Building Autonomous AI Agents with LangGraph
Course 2: Semantic Kernel SDK for Intelligent Applications
Course 3: Mastering Multi-Agent Development with AutoGen

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the potential of autonomous AI agents with LangGraph in this comprehensive course. Designed for developers and AI enthusiasts, you’ll learn to build intelligent, adaptive agents capable of processing complex queries, maintaining state, and integrating human feedback for enhanced decision-making. By the end of this course, you'll have a hands-on understanding of AI agent architecture, key frameworks, and real-world applications. The journey begins with an introduction to AI agents, setting up your environment, and a demo of what you’ll achieve. Next, dive deep into AI agents’ functionality as you build your first agent using the OpenAI API. Experience practical examples of automation, interactivity, and complex query processing to enhance your agent’s capabilities. In the LangGraph module, you’ll explore the framework’s core concepts, from state management to tool integration. You’ll learn to add memory, human-in-the-loop mechanisms, and graphical interfaces for comprehensive AI solutions. The course culminates with a capstone project where you’ll develop an AI financial report writer agent, showcasing your mastery of LangGraph. This course is ideal for intermediate developers with basic Python knowledge looking to advance their AI expertise. Whether you’re building tools for automation or exploring new AI paradigms, this course will elevate your skills and set you apart in the AI landscape.
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you'll dive deep into the world of multi-agent systems, mastering AutoGen, and understanding how these agents interact in real-time. Starting with a solid foundation on setting up your development environment, you'll gain expertise in creating, configuring, and deploying agents within AutoGen. By working through hands-on activities, you'll build agents from the ground up, create multi-agent conversations, and explore the integration of human feedback. You will also learn how to design, deploy, and optimize real-world agent applications such as customer service automation and research paper writing. Through this course, you will explore AutoGen's key building blocks, its various agent types, and conversation patterns that will allow you to build sophisticated, real-time agent-based systems. Practical use cases will guide you in applying these concepts to real-world challenges, making your learning experience immediately applicable. This course is ideal for anyone looking to understand multi-agent systems, the AutoGen framework, and how to use them to create meaningful interactions. With no prior experience required, it’s an accessible starting point for anyone interested in the field of artificial intelligence and multi-agent development.
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This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Learn how to build production-ready AI applications using Microsoft's Semantic Kernel SDK and Azure OpenAI. You'll gain practical experience creating intelligent, context-aware applications that leverage large language models, plugins, Retrieval-Augmented Generation (RAG), and enterprise AI design patterns while following modern .NET development practices. The course begins by introducing generative AI, large language models, and the Semantic Kernel architecture before guiding you through environment setup, Azure OpenAI integration, and your first AI-powered chat application. You'll then explore prompt engineering, reusable prompt templates, built-in plugins, native plugins, automatic function calling, and best practices for developing maintainable AI solutions. Next, you'll build a context-aware ASP.NET Core assistant with authentication, persistent chat history, document management, and intelligent workflows. Finally, you'll implement document processing pipelines, OCR, embeddings, vector-based retrieval, and Retrieval-Augmented Generation (RAG) to create document-aware assistants capable of delivering grounded responses with citations. This course is ideal for .NET developers, software engineers, AI application developers, solution architects, and technical professionals interested in enterprise AI development. Familiarity with C#, ASP.NET Core, and basic cloud concepts is recommended. The course is designed for learners at the Intermediate level. By the end of the course, you will be able to build intelligent applications using Semantic Kernel, integrate Azure OpenAI services, develop reusable AI plugins, implement context-aware assistants with persistent memory, create document processing pipelines, and deploy RAG-powered enterprise solutions that deliver accurate, grounded AI experiences.