Notebook-based alignment of human and agentic reasoning in single-cell biology

Agentic AI is increasingly deployed on complex problems, often using chain-of-thought prompting to ground predictions in stepwise reasoning. In biomedical research, assistive agents could make this reasoning accessible to human scientists: for example, intermediate conclusions could be critically evaluated based on shared reasoning and sycophancy – the tendency to affirm a user’s claims regardless of their validity – could be mitigated. However, this interaction requires an alignment of reasoning between agents and humans that is difficult when multiple data modalities are considered, for example in single-cell biology, where human scientists reason through computational notebooks that contain code, results, and text. To address this issue, we developed kai , an agentic AI that iteratively generates analyses in computational notebooks. We find that kai can flexibly use multiple tools in sequence to solve complex cell type annotation problems. Compared with one-shot generation, kai demonstrates improved robustness against code errors and sycophancy, improved reasoning, and the ability to formulate and address questions on data. kai ’s design is model-agnostic and, therefore, scales directly with advancements in large language models.