  Computational notebooks became indispensable tools for research-related
development, offering unprecedented interactivity and flexibility in the
development process. However, these benefits come at the cost of
reproducibility and an increased potential for bugs. There are many tools for
bug fixing; however, they are generally targeted at the classical linear code.
With the rise of code-fluent Large Language Models, a new stream of smart
bug-fixing tools has emerged. However, the applicability of those tools is
still problematic for non-linear computational notebooks. In this paper, we
propose a potential solution for resolving errors in computational notebooks
via an iterative LLM-based agent. We discuss the questions raised by this
approach and share a novel dataset of computational notebooks containing bugs
to facilitate the research of the proposed approach.
