My Projects
Agentic AI systems I've built, from clinical assistants to enterprise document Q&A
Securing Agents - Multi-Agent Customer Support
Course Project | UT Austin McCombs - AI Agents for Business Applications | 2026
A LangGraph-orchestrated customer support assistant with production-style security guardrails: prompt-injection detection, output-safety scanning, tier-based access control, and a full audit trail across four specialist support agents.
Last-Mile Delivery Exception Agents
Course Project | UT Austin McCombs - AI Agents for Business Applications | 2026
A LangGraph multi-agent system that triages and resolves last-mile delivery exceptions - deduplicating noisy shipment logs, deciding a resolution against an operational playbook via RAG, escalating to a human when policy requires it, and drafting a personalized customer notification, with a full auditable decision trail.
AI-Powered EHR Assistant
Capstone Project | UT Austin McCombs - AI Agents for Business Applications | 2026
An agentic assistant that helps clinicians navigate electronic health records - retrieving patient history, summarizing chart notes, and answering natural-language questions grounded in structured and unstructured EHR data, with human-in-the-loop safeguards for clinical accuracy.
RAG System for Enterprise Document Q&A
Capstone Project | UT Austin McCombs - AI Agents for Business Applications | 2026
A Retrieval-Augmented Generation system that lets users ask natural-language questions over a long-form Harvard Business Review case study on Apple, combining vector search with an LLM to ground answers in the source document and reduce hallucination.