Engineering teams building context pipelines for AI agents face a fundamental tradeoff: where does the work of transforming raw data into usable context happen — at write time or read time? Relational databases burden readers with joins and aggregations; search indexes burden writers with keeping pre-processed documents fresh. At agent scale, both models break down. The emerging solution is a live context graph — an incremental transform layer that sits between raw writes and agent readers, continuously converting unstructured or siloed data into always-current, composable contextual building blocks. This reduces token and compute costs for readers while freeing writers from maintaining complex pipelines, and enables single-digit-second write-to-context latency for interactive agent experiences.
