Self-Healing Multi-Agent AI Automates ML Pipeline Generation from Natural Language

A new research paper, arXiv:2604.27096, presents a unified multi-agent architecture designed to autonomously generate end-to-end machine learning (ML) pipelines from datasets and natural-language (NL) goals. The system, which relies on five specialized agents working in concert, aims to improve efficiency, robustness, and explainability in ML workflows. By interpreting user intent expressed in plain language and automatically producing executable pipelines, the approach reduces manual effort and accelerates model development. The architecture includes a self-healing mechanism that allows agents to detect and correct errors during pipeline execution, enhancing reliability.