This specialization offers a hands-on introduction to building Agentic AI systems using LangGraph, AutoGen, CrewAI, and Model Context Protocol (MCP). Learn to create autonomous AI agents, orchestrate multi-agent workflows, integrate AI with enterprise tools and services, and develop scalable AI applications. Explore agent collaboration, workflow automation, and enterprise deployment strategies to build intelligent systems that drive business outcomes. By the end of this program, you will be able to: - Build Agentic AI Systems: Create autonomous AI agents that can reason, plan, and execute tasks - Orchestrate Multi-Agent Workflows: Use LangGraph, AutoGen, and CrewAI to coordinate intelligent agent collaboration - Integrate AI Applications: Connect AI systems with APIs, tools, and services using MCP and enterprise integration practices - Deploy Enterprise AI Solutions: Develop scalable and production-ready Agentic AI applications for business use cases Ideal for beginners, software developers, AI practitioners, automation professionals, solution architects, and technology leaders looking to build skills in Agentic AI, multi-agent systems, AI orchestration, and enterprise AI development.
Übungsprojekt
Project Overview: Building a Personal AI Agent Using MCP and LangChain In this project, you'll act as an AI Engineer setting up a development environment to build a personal AI agent that responds to natural language queries. You'll combine LangChain and the Model Context Protocol (MCP) in a VS Code environment, configuring MCP servers for tools such as Airbnb, Playwright, and DuckDuckGo search, and powering the agent with a GROQ-hosted LLM. The project culminates in testing the agent against a real-world natural language query for Airbnb listings to confirm it returns accurate, meaningful responses. Project tasks include: - Set Up the Development Environment - Initialize the MCP Project and Launch VS Code - Configure MCP Settings in VS Code - Prepare the Project Configuration Files - Build and Test the Agent
