A case study of evaluating AI agents on a neuroscience data-to-discovery pipeline
Agentic AI offers a promising path to automating software development bottlenecks in scientific research pipelines, particularly for stages that take domain experts days to months to build and where correctness and robustness matter more than implementation details. We present an empirical study of general-purpose coding agents on a fly optogenetics data-to-discovery pipeline. We assess agents on tasks and datasets substantially larger than existing benchmarks and evaluation criteria grounded in domain expert standards. We show that agents can solve several pipeline stages, suggesting stage-level automation is tractable. By analyzing agents' code iterations, we show they struggle most wit...
| Authors | Kai A. Horstmann, Ethan Lin, Alice A. Robie et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 June 2026 |
| Last updated at source | 10 August 2026 |
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