Charles Xie

An agent that runs twenty steps doesn't issue one retrieval — it issues dozens, and it writes about as much as it reads. The trace it leaves behind is write-heavy, time-ordered, and queried at three granularities: step, span, trajectory.

That's the same shape as an RL rollout store. Not similar to — the same. The model labs hit this two years earlier under different names: semantic dedup, decontamination, hard-negative mining.

Agent memory and training data are converging on one set of operators — operational writes and analytical scans over the same data, with no ETL in between. That convergence is why we took Milvus from a vector database to a Vector Lakebase. This talk walks through the workloads that forced the shift, and what the architecture looks like underneath.