Improving the Notebook Agent: Error Clustering

This post details the implementation of error clustering using K-Means on agent thinking messages to identify and categorize failure modes within the Notebook Agent. It describes the process of embedding messages, clustering by tool, labeling clusters with LLMs, and visualizing results with t-SNE. Specific findings include issues with Dataframe SQL usage, SQL syntax errors, and cell timeouts. The post outlines the resulting improvements: prompt enhancements for Dataframe SQL, a tool for searching Hex docs, improved handling of cell timeouts with user consultation, and a 'notebook DAG' tool to enhance relational understanding of notebook structure. It also highlights the impact of clear error messages on agent recovery and strategy.