Agentic AI ArchitectureCornell Certificate Program

While millions use AI chatbots daily, few understand the mechanics that drive them. This course builds a foundational understanding of how large language models actually work, developing the practical intuition that will make you a more effective AI user and builder.

You'll explore the architecture of neural networks and transformers, understand why LLMs sometimes fail at seemingly simple tasks, and learn strategies to mitigate hallucinations and other limitations. Through hands-on work with the OpenAI API, you'll transition from chatting with AI to programmatically integrating it into applications.

You'll learn how to achieve consistently better results through context engineering and effective prompt engineering techniques, including chain-of-thought reasoning and self-reflection. You'll also understand the evolution from base models to reasoning models, and build your first AI chatbot with streaming responses, memory, and personalized system prompts.

By the end of the course, you'll have the technical foundation and practical skills to build reliable AI applications.

LLMs are powerful, but they're limited by their training data and lack access to your organization's private information. In this course, you'll master Retrieval-Augmented Generation (RAG), the industry-standard architecture for giving AI access to proprietary knowledge.

You'll also explore advanced techniques, including RAG over relational databases (Text-to-SQL) and GraphRAG, to improve accuracy and reduce errors. Through hands-on projects, you'll develop production-ready RAG applications that ground AI responses in trusted, up-to-date information while dramatically reducing hallucinations.

This course bridges the gap between AI that thinks and AI that acts. You'll learn to build AI agents—LLMs equipped with tools, memory, and reasoning capabilities—that can execute workflows autonomously.

The course covers the core components of agents (the model, system prompt, tools, and memory) and explores practical architectural patterns such as prompt chaining, routing, parallelization, orchestrator–worker designs, and reflection loops. You'll also explore how agents communicate with one another through protocols and handoffs, and learn the Model Context Protocol (MCP), which enables you to build and consume standardized tool interfaces.

Through progressive projects, you'll develop everything from focused AI workflows to more autonomous agents capable of tackling open-ended objectives.

Unlocking the full potential of AI isn't just about technical skills. This course focuses on the strategic, ethical, and organizational dimensions of deploying AI, preparing you to lead AI initiatives that deliver value while managing risk.

You'll develop frameworks for evaluating AI opportunities across impact, feasibility, and ethical considerations, and learning how to identify high-ROI projects to avoid costly failures. Through an in-depth case study on AI-assisted hiring, you'll confront the complexities of algorithmic fairness and values alignment firsthand.

Finally, you'll address the human side of AI transformation, including workforce deskilling, resistance to change, and the risk of apprenticeship loss. You'll emerge equipped to lead AI adoption in ways that enhance human potential.