This portfolio sample shows how I evaluate AI agents and automated workflows to find reliability problems, broken logic, and weak edge-case handling. I review whether the system completes the intended task, uses tools correctly, follows instructions, handles ambiguity, and produces an output that can actually be trusted.

The work focuses on identifying failure points before they become real user problems, including incomplete task execution, incorrect assumptions, workflow dead ends, hallucinations, and verification gaps. This is a portfolio demonstration of my AI agent evaluation and red-teaming work. No confidential client information is included.

Thorough breakdown! Robust edge-case handling and reliability checks are definitely the trickiest part of building reliable AI agents 🤖📊

This is a retail sales and profit dashboard I built from scratch to turn complex spreadsheet data into easy-to-read visuals. The main goal of this project was to make a messy dataset meaningful so a business owner can instantly check how their store is performing.

Here is how I worked on this project:

Data Cleaning: I went through the entire raw file in Excel, filtered out bad entries, and organized the rows to make sure the dashboard numbers are completely accurate.

KPI Tracking: I used Excel formulas to pull out core business metrics, such as total revenue, total orders, and the overall profit margin (12.47%).

Designing Charts: I set up clean visuals to show key trends, like sales by shipping modes, customer segments, and a breakdown of top-performing products.

I also added dynamic slicers, so you can click on any region or year and the whole screen updates automatically. I handled this entire project manually from start to finish.

Tools used: Advanced Excel (Pivot Tables, Slicers, Formulas, Dashboard Design)