Übersicht
Agentic AI Engineer with LangChain and LangGraph is a program that teaches Python developers how to turn large‑language‑model applications into fully autonomous agents. You begin with LangChain fundamentals—prompt templates, chains, memory, and single‑tool agents—then progress to multi‑tool planning, self‑critique loops, and deployment practices. Finally, you integrate external knowledge through retrieval‑augmented generation, long‑term memory, and multi‑agent collaboration.
Lehrplan
- LangChain Agentic AI Fundamentals
This course provides a comprehensive overview of building intelligent agentic applications using LangChain. Participants will learn to create simple LangChain applications and explore structured outputs. The curriculum progresses through multi-step workflows, transitioning from LLM calls to developing agents, and covers essential agentic design patterns. Key concepts include extending agents with tools, managing functions, and understanding state management within LangGraph. Students will also implement short-term agent memory techniques and complete a hands-on project: building a report-generating agent.
- Building AI Agents with LangGraph
This course guides learners through the essentials of implementing intelligent agents using LangGraph. Learners will explore external tools and APIs, and learn how to integrate them effectively within their agents. Key lessons include interacting with databases and implementing LangGraph Database Agents for efficient data retrieval. The course covers advanced topics like Retrieval Augmented Generation, incorporating human-in-the-loop strategies, and ensuring agent observability and reliability. Finally, learners will apply these concepts in a hands-on project, designing an Energy Advisor agent, which synthesizes their knowledge into a practical application.
- Advanced Agentic AI Techniques
This course equips learners with the essential skills and knowledge to design and implement sophisticated agent-based systems. The course covers long-term memory integration within agents, emphasizing the LangGraph framework. Participants explore multi-agent architectures and state management, focusing on effective orchestration and data routing. Through hands-on projects, learners will implement agentic systems, culminating in the development of an Autonomous Knowledge Agent.
Unterrichtet von
Henrique Santana, Gerald Parker, Christopher Agostino and Joshua Bernhard
Fachgebiete
Computer Science
