AI-Build-AI is an autonomous AI agent that automatically builds, trains, and evaluates machine learning models for diverse tasks. The repository contains solutions to over 20 benchmarks and competitions (Kaggle, IOAI, NatureBench, MLE-Bench) spanning tabular classification, image segmentation, object counting, protein variant effect prediction, video prediction, graph node classification, and more. Architecturally, it is a collection of self-contained task modules under `tasks/`, each with a consistent pattern: `config.py` (hyperparameters), `model.py` (architecture), `train.py` (training loop), `inference.py` (standalone prediction), and `predictions/` (outputs). The agent employs state-of-the-art backbones (ESM-2, Cosmos-Predict2.5, SegFormer, EfficientNet, GBDTs, GNNs) and advanced techniques including ensemble search, EMA, test-time augmentation, pseudo-labeling, and cost-sensitive thresholding. While the code is functionally impressive and demonstrates high craftsmanship in isolated components (e.g., ensemble search, graph feature engineering), it suffers from significant code duplication across tasks, hardcoded paths, and tight competition-specific optimizations that limit general reusability. The repository is actively maintained and shows strong engineering practices within its autonomous agent paradigm.