Understand the fundamentals of Agentic AI, how intelligent agents differ from traditional AI systems, and how they make decisions and interact with their environments. Get started with practical implementations, including a simple AI chatbot and an email agent.
Explore the architecture of AI agents, the differences between single-agent and multi-agent systems, and popular development frameworks. Learn how to implement a multi-agent system using the AutoGen library.
Discover how Large Language Models power intelligent agents through prompting, reasoning, decision-making, and tool integration. Build practical AI agents using LangGraph and LangChain.
Learn how multiple AI agents communicate, coordinate tasks, and work together to achieve shared objectives. Explore agent interaction, conflict management, and collaborative workflows through practical examples.
Apply your knowledge through practical projects involving multi-agent development, AI-powered application building, system monitoring, and workflow automation. Work with tools and frameworks including LangGraph, CrewAI, Cursor AI, Claude, and Antigravity.
Explore career opportunities in the growing field of Agentic AI and prepare for technical interviews. Understand relevant career paths, interview topics, and recommended practices for developing AI agent systems.