Agentic & Multi-Agent AI
Architectures, tool use, orchestration, autonomous agent ecosystems, and evaluation of LLM-based multi-agent workflows.
Audrey Rah
Artificial Intelligence | Agentic & Multi-Agent Systems | RAG | AI Governance
Researcher and Ph.D. Candidate in Computer Engineering at the University of Houston developing trustworthy, explainable, and enterprise-oriented AI systems spanning agentic AI, multi-agent architectures, retrieval-augmented generation, AI governance, cybersecurity, virtual reality, biomedical sensing, and intelligent data systems.
Research identity
The primary focus of this portfolio is artificial intelligence: agentic and multi-agent systems, retrieval-augmented generation, generative AI, AI governance, semantic tracing, and AI observability in enterprise settings. Interdisciplinary work in virtual reality, EEG/BCI, biomedical sensing, FPGA systems, and enterprise network data engineering remains an important part of the research record.
Primary research areas
RAG & Generative AI
Retrieval-augmented generation, generative AI systems, and integrated enterprise governance architectures for RAG pipelines.
AI Governance & Cybersecurity
Semantic tracing, observability, explainable and trustworthy AI, identity-recovery governance, and cybersecurity controls.
Enterprise AI & Data Systems
Enterprise AI architectures, HPE Aruba Central API ETL automation, and campus network analytics platforms.
Virtual Reality & Biomedical AI
VR stress detection, VR stroke rehabilitation, and immersive systems for healthcare and education.
EEG / BCI / Intelligent Sensing
High-density EEG, IMU and video-based MoBI research, and FPGA-oriented intelligent sensing including fall-detection themes.
Selected research
Semantic Tracing in LLM-Based Multi-Agent Systems Using LangChain, LangGraph, and LangSmith for AI Governance Preprint
Retrieval-Augmented Generation (RAG), Generative AI, and Agentic AI Governance: An Integrated Enterprise Governance Prioritization Architecture Preprint
Agentic and Multi-agent Systems: A Systematic Review of Tool Use, Benchmarks, and Governance Preprint
Professional and research experience
AI systems and governance
- Agentic AI and multi-agent systems research
- Retrieval-augmented generation (RAG) and generative AI
- AI governance, semantic tracing, and AI observability
- Enterprise AI architectures and autonomous agent ecosystems
Applied research and engineering
- University of Houston UIT enterprise network data platform
- HPE Aruba Central API and ETL automation
- EEG, IMU, and video-based MoBI research
- VR stress detection and VR stroke rehabilitation
- FPGA fall-detection research
Current work
Current writing and systems work emphasizes multi-agent architectures, RAG and generative AI governance, semantic tracing with LangChain, LangGraph, and LangSmith, and enterprise-oriented evaluation. Complementary threads include VR-based sensing, EEG/BCI analysis, and operational network data platforms.
Publications
The full record currently lists 22 works, with peer-reviewed journal and conference papers distinguished from preprints, Research Square papers, SSRN papers, ResearchGate-hosted manuscripts, and Zenodo records.
Contact and scholarly profiles
Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, USA.