@inproceedings{kim-etal-2025-bel,
title = "Bel Esprit: Multi-Agent Framework for Building {AI} Model Pipelines",
author = "Kim, Yunsu and
Abdelaziz, Ahmedelmogtaba and
Castro Ferreira, Thiago and
Al-Badrashiny, Mohamed and
Sawaf, Hassan",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.32/",
doi = "10.18653/v1/2025.acl-demo.32",
pages = "329--339",
ISBN = "979-8-89176-253-4",
abstract = "As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user requirements. Bel Esprit uses a multi-agent framework where subagents collaborate to clarify requirements, build, validate, and populate pipelines with appropriate models. We demonstrate its effectiveness in generating pipelines from ambiguous user queries, using both human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline building.Bel Esprit is available for a free trial at https://belesprit.aixplain.com."
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<abstract>As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user requirements. Bel Esprit uses a multi-agent framework where subagents collaborate to clarify requirements, build, validate, and populate pipelines with appropriate models. We demonstrate its effectiveness in generating pipelines from ambiguous user queries, using both human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline building.Bel Esprit is available for a free trial at https://belesprit.aixplain.com.</abstract>
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%0 Conference Proceedings
%T Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines
%A Kim, Yunsu
%A Abdelaziz, Ahmedelmogtaba
%A Castro Ferreira, Thiago
%A Al-Badrashiny, Mohamed
%A Sawaf, Hassan
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F kim-etal-2025-bel
%X As the demand for artificial intelligence (AI) grows to address complex real-world tasks, single models are often insufficient, requiring the integration of multiple models into pipelines. This paper introduces Bel Esprit, a conversational agent designed to construct AI model pipelines based on user requirements. Bel Esprit uses a multi-agent framework where subagents collaborate to clarify requirements, build, validate, and populate pipelines with appropriate models. We demonstrate its effectiveness in generating pipelines from ambiguous user queries, using both human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline building.Bel Esprit is available for a free trial at https://belesprit.aixplain.com.
%R 10.18653/v1/2025.acl-demo.32
%U https://aclanthology.org/2025.acl-demo.32/
%U https://doi.org/10.18653/v1/2025.acl-demo.32
%P 329-339
Markdown (Informal)
[Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines](https://aclanthology.org/2025.acl-demo.32/) (Kim et al., ACL 2025)
ACL
- Yunsu Kim, Ahmedelmogtaba Abdelaziz, Thiago Castro Ferreira, Mohamed Al-Badrashiny, and Hassan Sawaf. 2025. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 329–339, Vienna, Austria. Association for Computational Linguistics.