From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists
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. However, designing and implementing AI solutions remains challenging for many researchers due to the expertise required in the design and development of end-to-end AI systems. To address this gap, we present Domain-Driven Adaptable AI Pipelines (DDAP), a controlled, human-in-the-loop, agentic framework that leverages large language models to guide users in a systematic construction of AI pipelines and their corresponding implementation c...
| Authors | Hyacinth Ali, Jessie Galasso-Carbonnel, Houari Sahraoui |
|---|---|
| Primary category | cs.IR |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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