Name: AI Agents and Agentic AI with Python & Generative AI
Brand: Vanderbilt University
Availability: OnlineOnly
Rating: 4.7 (53 reviews)

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Learner Reviews & Feedback for AI Agents and Agentic AI with Python & Generative AI by Vanderbilt University

4.7

stars

221 ratings

About the Course

AI Agents Are the Next Leap in Software. Learn to Build Them in Python. AI agents aren't passive tools. They think, act, and solve problems—without waiting for instructions. That's the future of software. And in this course, you'll learn how to build it. Frameworks come and go. Principles last. This course cuts through the noise to teach you how AI agents really work—using Python, the leading language for AI development. Forget tutorials on trendy APIs that'll be dead by next quarter. You'll learn to build AI agents from the ground up. No fluff. No shortcuts. Just the core architecture that powers intelligent systems—knowledge that stays useful no matter how fast the landscape shifts. In this course, you will: - Master Python-based agent architectural fundamentals - Understand the core GAME components (Goals, Actions, Memory, Environment) that make AI agents tick and how they work together in a cohesive Python system - Leverage Python's strengths for efficient agent development - Use Python's dynamic typing, decorators, and metaprogramming to create flexible, maintainable agent frameworks with minimal boilerplate code - Rapidly prototype and implement Python agents - Learn techniques to quickly design Python agent capabilities with prompt engineering before writing a single line of code, then efficiently translate your designs into working Python implementations - Connect Python AI agents to real-world systems - Build Python agents that can interact with file systems, APIs, and other external services - Create Python-powered tool-using AI assistants - Develop Python agents that can analyze files, manage data, and automate complex workflows by combining LLM reasoning with Python's extensive libraries and ecosystem - Build Python developer productivity agents - Create specialized Python agents that help you write code, generate tests, and produce documentation to accelerate your software development process Why Principles Matter More Than Frameworks The AI landscape is changing weekly, but the core principles of agent design remain constant. By understanding how to build agents from scratch, you'll gain: - Transferable knowledge that works across any LLM or AI technology Deep debugging skills because you'll understand what's happening at every level - Framework independence that frees you from dependency on third-party libraries and allows you to succeed with any of them - Future-proof expertise that will still be relevant when today's popular tools are long forgotten By the end of this course, you won't just know how to use AI agents—you'll know how to build them in Python, customize them, and deploy them to solve real business problems. This course will teach you these concepts using OpenAI's APIs, which require paid access, but the principles and techniques can be adapted to other LLMs....

Top reviews

Aug 14, 2025

This course is about how to build AI agent with the basic loop structure and it also has detailed code to explain what is the best code structure to make it reuseable and less error prune

Jul 28, 2025

Very informative. Lots of code examples. Thorough videos, showcasing and explaining the topics precisely, accurately and in detail. _No AI dubbing_. Fantastic course, genuinely.

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51 - 56 of 56 Reviews for AI Agents and Agentic AI with Python & Generative AI

By Jari v d M

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May 8, 2025

awesome!

By Sekar A

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Aug 16, 2025

good

By Nikolai G

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Aug 4, 2025

The course presents useful concepts, but I encountered two inconveniences: 1. Although the course is marked as suitable for beginners familiar with Python, in module 3 there is a large AI agent project composed of an unstructured list of classes. It took time to analyze and draw a UML diagram to understand all the relationships in this core module of the AI agent. 2. For this course, I chose to use LLM models running locally on my machine via Ollama (models: Magicoder, GPT, LLaMA3). I ran the course code locally, and each time I faced issues during the agent loop execution—often the LLM failed to stay within the tool-based narrative set by the prompts. I don’t know how it would perform using the OpenAI API.