AI agents that build their own data tables for analysis

On the Max Agency podcast, hosted by LangChain CEO Harrison Chase, ListenLabs AI Co-Founder and CTO Florian Juengermann explains how his agents approach data analysis by treating responses as rows in a table, with columns representing extracted features or questions. The agent can dynamically add new columns — for example, asking "what is the user sentiment toward this specific topic?" — and fill in values that are either open-ended summaries or categorical labels like positive, negative, or neutral. To populate each row, the system spawns highly constrained sub-agents that perform a single classification call, which Juengermann describes as analogous to a map-reduce operation. The resulting table can then be charted using tools like Python.