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Starts 5 June 2025 10:44

Ends 5 June 2025

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AI Agent Developer

Master Python-based AI agent development with hands-on experience in designing architectures, implementing tool use, building custom GPTs, and applying responsible AI practices for real-world applications across industries.
Vanderbilt University via Coursera

Vanderbilt University

21 Courses


Vanderbilt University is a private research university in Nashville, TN, offering world-class interdisciplinary education and award-winning research opportunities.

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Overview

In this Specialization, you’ll gain hands-on experience developing AI agents using Python, OpenAI tools, and prompt engineering techniques. You’ll learn to design agent architectures, implement tool use and memory, build custom GPTs, and apply best practices for responsible, trustworthy AI.

By the end, you’ll be able to create and deploy intelligent software agents for real-world tasks across a range of industries.

Syllabus

  • Introduction to AI Agents
  • Overview of AI agents and their applications
    Introduction to Python and its role in AI development
    Setting up the development environment
  • Designing AI Agent Architectures
  • Understanding agent architecture components
    Exploration of different architecture paradigms
    Designing user-centric agent interactions
  • Building with OpenAI Tools
  • Overview of OpenAI APIs and their capabilities
    Integrating APIs into Python applications
    Building simple AI agents using OpenAI tools
  • Advanced Prompt Engineering
  • Techniques for crafting effective prompts
    Using prompt engineering to guide agent behavior
    Experimentation with complex prompting scenarios
  • Tool Use and Memory in AI Agents
  • Implementing tool usage within agents
    Integrating memory functions into AI agents
    Strategies for maintaining context and continuity
  • Developing Custom GPT Models
  • Understanding GPT architecture and customization
    Training custom models with domain-specific data
    Deploying custom GPTs for specialized applications
  • Best Practices for Responsible AI
  • Addressing bias and fairness in AI systems
    Ensuring transparency and explainability
    Strategies for secure and ethical AI development
  • Deploying AI Agents
  • Preparing AI agents for deployment
    Testing and debugging AI agents in real-world scenarios
    Utilizing cloud services for scaling AI applications
  • Industry Applications and Case Studies
  • Exploration of AI agents in various industries
    Analyzing successful deployment case studies
    Identifying potential future applications
  • Capstone Project
  • Designing and developing a fully functional AI agent
    Presentation and peer review of projects
    Feedback and iterations for project improvement

Taught by

Dr. Jules White


Subjects

Computer Science