By my self

GridMind AI is a persistent, self-improving and governed multi-agent harness on MongoDB Atlas that turns a failing power grid into an independently verified fix. Problem: AI data centers add megawatts to distribution grids built for homes and shops. Engineers must find where limits break, why, what to change, and prove the change works. What it does: 10 specialized agents (circuit modeling, power flow, violation detection, root cause, corrective action, data center, optimization, independent verification, recursive learning, engineering report) analyze the public CIGRE MV benchmark network with a 4 MW AI data center. An AC power-flow solver finds 17 violations at the evening peak (14 undervoltages down to 0.895 pu, 3 overloaded transformers). Agents diagnose each one with numbers, size 19 remedies by simulation and evaluate 179 plans. An independent verification agent reloads the model from MongoDB and re-solves it at peak and light load before anything is labeled fixed. Result: a $62,000 plan (capacitor bank, transformer tap change, shifting AI-training load, battery dispatch): 17 violations resolved, 0 remaining, 0 new; losses down 26%. MongoDB Atlas is the harness backbone: circuit model, every simulation result, persisted run state (runs survive restarts and resume), audit logs, and a recursive-learning memory of verified lessons that later runs retrieve (our AI-surge scenario reused the first run's lessons and was verified again). An interactive single-line diagram shows original, proposed and verified states. The language model never calculates electrical values; it only explains solver evidence. Built with: MongoDB Atlas, Python, FastAPI, pandapower, SciPy, React, TypeScript, React Flow, Claude API. Public data: CIGRE benchmark, SimBench load profiles, Open-Meteo weather.