Why Foundation Models Haven’t Replaced Classical Machine Learning

Agentic systems are essential for transforming complex, fragmented enterprise data into production-ready machine learning models, particularly for classical tasks like forecasting and fraud detection where foundation models often underperform. Disarray addresses the persistent challenge of data silos by constructing a comprehensive context graph that maps semantic relationships across disparate systems, including code, documentation, and legacy infrastructure. This approach moves beyond traditional AutoML by automating the entire data engineering lifecycle, from pipeline construction to feature engineering, while maintaining human-in-the-loop oversight. By leveraging institutional knowledge and entity resolution, these agents reduce the search space for model architectures, enabling more efficient, high-quality experimentation. This framework prioritizes context-aware autonomy, allowing data scientists and engineers to scale their output while ensuring rigorous accountability and data governance in high-stakes production environments.