Over the next few years, I think agent traces are going to become one of the fastest-growing data types in the AI stack. As more applications become agentic, every interaction can generate a tree of model calls, tool invocations, retrievals, sandbox executions, verifier results and intermediate state.
A modern tracing stack, I think, needs multiple layers.
1. Developers need a fast/hot operational experience for debugging and interactive exploration
2. Durable trace store to handle large-scale retention, selective reads, nested attributes and analytical access.
I like what LangSmith has done with SmithDB. They are combining Vortex as the physical columnar format with DataFusion as the query engine, and designing directly around object storage, selective reads, JSON filtering and trace-oriented access patterns.
The opportunity I see next is adding stronger table semantics around this kind of physical representation. A table format brings snapshots, schema evolution, atomic commits, retention, time travel, catalog integration and interoperability across compute engines. If newer formats like Vortex can eventually sit cleanly underneath a table format, the architecture gets interesting. Iceberg can decide which files and dataset version matter, Vortex minimizes what gets read inside those files, and DataFusion or other compute engines can execute the query.