Enhancing Agent Retrieval With Structured Chart Extraction
Databricks, Thursday, August 27th, 2026
Databricks tests making charts machine-readable so agents can answer questions requiring values read from figures.
Enterprises increasingly ask agents to work with proprietary documents, but much of the important information lives inside figures and charts rather than prose. Databricks reports that customers find agents struggle with questions requiring reading and counting values in charts, which limits reliability in real document workflows.
The post argues that for agents to work dependably across diverse enterprise settings, charts need to be made more interpretable rather than treated as images.
It describes experiments in structured chart extraction, converting the visual encoding back into data an agent can reason over directly.