Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI
The purpose of our paper is to develop a unified multi-agent architecture that automates end-to-end machine learning (ML) pipeline generation from datasets and natural-language (NL) goals, improving efficiency, robustness and explainability. A five-agent system is proposed to handle profiling, intent parsing, microservice recommendation, Directed Acyclic Graph (DAG) construction and execution. It integrates code-grounded Retrieval-Augmented Generation (RAG) for microservice understanding, an explainable hybrid recommender combining multiple criteria, a self-healing mechanism using Large Language Model (LLM)-based error interpretation and adaptive learning from execution history. The appro...
| Authors | Adela Bara, Gabriela Dobrita, Simona-Vasilica Oprea |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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