An unvalidated multi-agent framework is proposed to automate clinical data pipelines from ingestion to inference for tabular and imaging data, with no reported benchmarks.
Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines
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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-defined requirements. Bel Esprit employs a multi-agent framework where subagents collaborate to clarify requirements, build, validate, and populate pipelines with appropriate models. We demonstrate the effectiveness of this framework in generating pipelines from ambiguous user queries, using both human-curated and synthetic data. A detailed error analysis highlights ongoing challenges in pipeline construction. Bel Esprit is available for a free trial at https://belesprit.aixplain.com.
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Agentic AI framework for End-to-End Medical Data Inference
An unvalidated multi-agent framework is proposed to automate clinical data pipelines from ingestion to inference for tabular and imaging data, with no reported benchmarks.