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 🤖📊

Developed a Wumpus World Knowledge Base Agent using React, implementing AI-based logical reasoning, BFS exploration, and resolution-based inference for safe navigation in a dynamic grid environment. The system generates a configurable world with rows, columns, and number of pits, allowing users to customize difficulty and explore AI decision-making behavior in real time. It intelligently uses percepts (breeze & stench) to build a knowledge base and determine safe paths while avoiding pits and the Wumpus using logical inference. Live Demo: https://lnkd.in/dn9dvguP GitHub: https://lnkd.in/dHWh85pc