Name: AI Agents and Agentic AI with Python & Generative AI
Brand: Vanderbilt University
Availability: OnlineOnly
Rating: 4.6 (82 reviews)

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

4.6

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355 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

Jul 2, 2025

One of the best practical courses I have completed. The course seems to have a humorous take on graded exercises, while the real value lies in the ungraded exercises.

Jul 29, 2025

This was a great course and Dr. White is so very well-versed in the subject. His thinking "outside the box" in problem-solving is quite remarkable.

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76 - 83 of 83 Reviews for AI Agents and Agentic AI with Python & Generative AI

By Eve R

•

Sep 18, 2025

The core of the information is really good. But... for a "beginner" course, a LOT of python and programming knowledge is assumed. There are also very few graded assignments, which made it easy for me (with significant programming xp) to breeze through the course; but also didn't really solidify anything. I went with the recommended google.colab suggestion for running code; but it's not like I have files there. This would have benefitted from more programmer-esque setup and course structure (like, implement this part of the agent). More guidance on how to set up a local environment and and sample project to follow along with would have been really nice.