From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
The paper introduces Domain-Driven Adaptable AI Pipelines (DDAP), a framework designed to assist non-expert scientists in creating AI solutions. DDAP guides users through a structured process that includes problem definition, environment specification, pipeline generation, and code generation. Experimental results indicate that DDAP can produce competitive AI models across various domains, although performance may vary by task type.
- ▪DDAP is a controlled, human-in-the-loop framework that utilizes large language models.
- ▪The framework is structured into four stages: problem definition, compute environment specification, pipeline generation, and code generation.
- ▪DDAP has been evaluated across multiple datasets in business, biology, and health science domains, achieving competitive results compared to expert-developed models.
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Computer Science > Information Retrieval arXiv:2605.18764 (cs) [Submitted on 10 Apr 2026] Title:From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists Authors:Hyacinth Ali, Jessie Galasso-Carbonnel, Houari Sahraoui View a PDF of the paper titled From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists, by Hyacinth Ali and 2 other authors View PDF HTML (experimental) Abstract:Artificial Intelligence (AI) pipelines have become integral to modern research, supporting fields such as Medical Sciences, Agriculture, and Social Sciences, and enabling large-scale data analysis, predictive modeling, and the automation of complex tasks.
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