AI agents powered by Large Language Models (LLMs) are already transforming how we work, learn, and solve problems - and we are only at the beginning. As these systems grow more capable, they hold extraordinary promise for accelerating scientific discovery and democratizing access to high-quality medical, legal, and educational services worldwide. This is a project course coupled with rigorous lectures on the principles, methodologies, and cutting-edge research underlying agentic AI. Students undertake a substantial quarter-long project in either foundational methodology research or building novel agents in a domain of their choice. Topics covered include: (1) minimizing hallucination in question-answering and task-oriented agents using Retrieval-Augmented Generation (RAG) and formal task descriptions; (2) hybrid knowledge reasoning over databases, knowledge bases, and unstructured text; (3) AI-driven knowledge curation and discovery for scientific research; (4) improving the accuracy and interpretability of decision-making agents through formal methods; and (5) automated techniques for improving the accuracy and efficiency of long-horizon agents. Prerequisites: one of LINGUIST CS 180/CS 280, CS 124, CS 224N, CS 224S, or CS 224U.
3-4 units · Letter or Credit/No Credit
AI agents powered by Large Language Models (LLMs) are already transforming how we work, learn, and solve problems - and we are only at the beginning. As these systems grow more capable, they hold extraordinary promise for accelerating scientific discovery and democratizing access to high-quality medical, legal, and educational services worldwide. This is a project course coupled with rigorous lectures on the principles, methodologies, and cutting-edge research underlying agentic AI. Students undertake a substantial quarter-long project in either foundational methodology research or building novel agents in a domain of their choice. Topics covered include: (1) minimizing hallucination in question-answering and task-oriented agents using Retrieval-Augmented Generation (RAG) and formal task descriptions; (2) hybrid knowledge reasoning over databases, knowledge bases, and unstructured text; (3) AI-driven knowledge curation and discovery for scientific research; (4) improving the accuracy and interpretability of decision-making agents through formal methods; and (5) automated techniques for improving the accuracy and efficiency of long-horizon agents. Prerequisites: one of LINGUIST 180/280, CS 124, CS 224N, CS 224S, or CS 224U.
Offered in Autumn 2026 at Stanford University.