CS 6501 Workshop on Building AI Agents

Prof. Henry Kautz, henry.kautz@virginia.edu

Spring 2026, Tuesdays & Thursdays 2:00pm - 3:15pm

Mechanical Engineering Building Room 339

bytedance-agent

Illustration of ByteDance-Seed M3 Agent

In this hands-on workshop, we will learn how to build AI agents: systems powered by large-language models that autonomously interact with services, tools, and other agents.

Much of the programming work will be completed during class. Due to its nature as a workshop, class attendance is mandatory. Attendance can only be excused for illness or career events such as away-games for atheletics or job interviews. Simply having a lot of work in other classes is not an excuse for skipping class. Students who miss classes without valid reasons will be disenrolled.

You will need to create accounts on the following platforms:

For most of our tutorial-type programming we will use open-source Llama models. For later projects, you might choose to employ API access to a state of the art model such as Claude, ChatGPT, or Grok. Each of these charges by usage rather a fixed monthly rate.

In addition to programming, we will read about one paper a week and discuss it in class.

Grading: 50% attendance and class participation, 50% class projects and project presentations.

Topics

Resources

Gemini for Google Workspace Prompting Guide 101. https://workspace.google.com/learning/content/gemini-prompt-guide

GPT-5 Prompting Guide. https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide

Claude Prompt Engineering Overview. https://docs.claude.com/en/docs/build-with-claude/prompt-engineering/overview

Effective context engineering for AI Agents. Anthropic Blog. https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents

"Building LLM applications for production", Chip Huyen's Blog, 2023. https://huyenchip.com/2023/04/11/llm-engineering.html

LEANN RAG Vector Database https://github.com/yichuan-w/LEANN

The Model Context Protocol (MCP) Course, sponsored by Hugging Face and Anthropic. https://huggingface.co/learn/mcp-course/en/unit0/introduction

Fine-tune a pretrained model, Hugging Face Documentation. https://huggingface.co/docs/transformers/training

Large Language Model, Stanford Course, by Percy Liang. https://stanford-cs324.github.io/winter2022/

Agent Design Patterns: A Hands-on Guide to Building Intelligent Systems. Antonio Gulli. Preview of e-book. 424 pages. https://docs.google.com/document/d/1rsaK53T3Lg5KoGwvf8ukOUvbELRtH-V0LnOIFDxBryE/preview

Papers

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv preprint arXiv:2305.10601. https://arxiv.org/abs/2305.10601

Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-Refine: Iterative Refinement with Self-Feedback. arXiv preprint arXiv:2303.17651. https://arxiv.org/abs/2303.17651

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. ReAct: Synergizing Reasoning and Acting in Language Models. arXiv preprint arXiv:2210.03629. https://arxiv.org/abs/2210.03629

Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, and Enhong Chen. 2024. A survey on multimodal large language models. Natl. Sci. Rev. 11, 12 (November 2024), nwae403. DOI:https://doi.org/10.1093/nsr/nwae403

Hanjia Lyu, Jinfa Huang, Daoan Zhang, Yongsheng Yu, Xinyi Mou, Jinsheng Pan, Zhengyuan Yang, Zhongyu Wei, Jiebo Luo. GPT-4V(ision) as A Social Media Analysis Engine. https://arxiv.org/abs/2311.07547

Jiacheng Miao, Joe R. Davis, Jonathan K. Pritchard, and James Zou. 2025. Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents. arXiv preprint arXiv:2509.06917. https://arxiv.org/abs/2509.06917