Databricks and similar enterprise data platforms have spent a great deal of effort and time to full-proof their product suite with relevant observability and tracing. Not surprisingly this is needed as part of enterprise support especially in regulated sectors. But for the specific case of sophisticated data science and analytics agents there is a gap in the observability suite not just for Databricks but across all big and small analytics and data science agent providers. In the case of Databricks, even with notebooks as a primary user interface, given the offerings across data lineage, data management and MLflow, the level of control and tracing is no doubt high. However both large vendors like Databricks and Snowflake and smaller analytics and data science agents suppliers share an observability gap. The gap is inherent to coding agent architectures and does not apply equally to all agents. A text-to-SQL assistant can be wrong in an ‘obvious’ way: the result makes no sense.…