AI Agents and Agentic AI with Python & Generative AI

Vanderbilt University via Coursera

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Overview

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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.

Syllabus

Agentic AI Concepts

In this module, you’ll learn the core concepts behind agentic AI—systems that can plan, act, and adapt based on feedback. You’ll explore patterns like flipped interaction, agent loops, and programmatic prompting, and see how memory and structured outputs enable agents to operate autonomously. Tip: Focus on how agents decide what to do next. That decision-making loop is the foundation of everything you’ll build later.

AI Agents, Tools, Actions, & Language

This module introduces the core components of AI agents, focusing on how they use structured prompts, tools, and actions to interact with real-world systems. You’ll learn how to design effective agent prompts using the GAIL framework, define tools clearly, and build agent loops that use feedback to make decisions. The module also covers function calling and best practices for creating reliable, structured agent behaviors.

GAME: A Conceptual Framework for AI Agents

This module introduces the GAME framework as a practical way to design AI agents before building them in code. You’ll explore how goals, actions, memory, and environment work together in an agent loop, how to simulate agent behavior in conversation, and how to translate the framework into modular, reusable Python code.

Agent Tool Mangement

In this module, you’ll learn how to design, organize, and maintain tools that AI agents use to take action. You’ll explore how Python decorators can keep tool definitions and documentation in sync, how to organize tools using tags and registries, and how to simplify agent development through reusable, well-structured tool systems.

Rethinking How Software is Built in the Age of AI Agents

In this module, you’ll explore how AI agents are changing who can build software, how software is designed, and how information can be accessed and used. You’ll examine how simple tools plus agent intelligence can create powerful systems, and how capabilities like multimodal reasoning, flexible translation, and perspective generation open up new ways to solve problems. Tip: As you go through this module, focus less on “the right answer” and more on how AI agents expand what is possible in designing software and working with information.