Mui Group @ ASDRP

AI Agents Research

Welcome to our new Researchers!

Our goals for this year's cohort include:

Learn foundations of AI/ML and agentic techniques
Appreciate the advances in LLMs and become familiar with toolchains for using them as consumers
Understand what is algorithmic bias, and in particular bias in AI/ML/LLMs
Study a few major algorithmic faux pas made by well intentioned engineers. We will study (possibly) unintentional consequences of algorithms leading to unfair biases.
Hypothesize how to mitigate as much as possible biases inherent in computer algorithms and data sets, as as to minimize harm to society.

We intend for you to engage in serious scientific investigation in groups, and to learn how to leverage each group member in your research. No major research is done alone in the real world. If you are interested in more specificity, here are our minimal research expectations for this group.

Research Assistant: this term, we have the pleasure of having Ananya G. and Theodore M. be our research assistants. They will be sharing their current research with you as well as trying to help each of your group research effort.

2025 Fall Schedule

Our first meeting will be Sep 27 (Saturday) to welcome the fall cohorts of researchers!

2025/09/27

OpenAI Agents Framwork & Guardrails
repo: github.com/philmui/research2025
recording: [link]

Topics that we will try to cover through our weekend meetings:

Agent Frameworks:
- OpenAI agents
- CrewAI agents
- Amazon Strands
- LlamaIndex Agents
- Langchain Agents
- Google ADK agents
Agent Safety & Guardrails
Multi-Agents Orchestration
Agent Optimization
Knowledge Graphs
Using VLM to outperform OCR
Evaluation: how to use common datasets for validating agents

2025/10/04

Input & output guardrails
code: [link]
recording: [link]
Readings:
- Trailhead: Define the Agent Guardrails [link]
- Boyuan Zheng, Zeyi Liao, Scott Salisbury, Zeyuan Liu, Michael Lin, Qinyuan Zheng, Zifan Wang, Xiang Deng, Dawn Song, Huan Sun, Yu Su. "WebGuard: Building a Generalizable Guardrail for Web Agents" arXiv:2507.14293, July 18, 2025 [link] [github]