Agentic AI for User Facilities: Workshop Report

Q: What does this research mean for the field?

Recent advances in agentic AI, specifically LLM-based systems capable of orchestrating multi-step workflows, present new opportunities and address gaps in scientific user facilities not covered by previous AI for science reports. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Thorsten Hellert; David Abramov; Mahmoud Ajami; Edward S. Barnard; W. Bhimji; Gopika Bhardwaj; Tanny Chávez; Hazem Daoud; Tim J. Dunn; Kjiersten Fagnan; Steven Farrell; João Gabriel Felipe Machado Gazolla; Grbcic Luka; Guenzing Damian; Guo Jinghua; Yang Ha; Alexander Hexemer; Hayden Hoschouer; Ju Xiangyang; Konaté Kadidia; Gianluca Martino; McReynolds Dylan; M Annette Miller; Sara A. Miskovich; Peco Myint; Saad Naseem; Trent Northen; Maria Patrou; Gabriel E. Pérez; Paul Quinn; Haroon Rafique; Daniel Ratner; Aaron Reed; Eno Reyes; Hairong Shang; M A Smith; Antonin Sulc; Chris Tennant; Pawan K. Tripathi; Robert Underwood; Morgan Wall; Dan Wang; A Xiang Wu; Chenran Xu; Kevin G. Yager; Zhe Zhang

doi:10.1080/08940886.2026.2649097

Abstract

This workshop addressed a gap: the most recent DOE reports on AI for science [1–3] predate recent advances in agentic AI—LLM-based systems that can plan, orchestrate, and execute multi-step workflo...

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Hellert et al. (2026) studied this question.

synapsesocial.com/papers/6aaa3b7c98d24627373e88bc https://doi.org/10.1080/08940886.2026.2649097

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Agentic AI for User Facilities: Workshop Report

Abstract

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Cite This Study

Agentic AI for User Facilities: Workshop Report

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Cite This Study

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