Temporal Data Provenance

We usually treat provenance as a question of source: where did this come from, and can I trust it? There is a second axis that matters just as much, especially for AI: time.

A fact is rarely true forever. Some information is evergreen, like a birthdate. Most of it has a lifespan. A price, a job title, a piece of medical guidance, the name of a country’s leader: each was true for a window, and stale outside it. Provenance that records the source but not the timeframe gives you half the picture.

Temporal data provenance closes that gap. Alongside “where did this come from”, it records when a fact was first seen, the period it was claimed to be true, how long that is expected to hold, and when an update was likely. That metadata turns a flat snapshot of the web into something you can reason over across time.

This matters most for agents. LLMs process information quickly but lose track of where it sits in time, which is how you end up citing advice that expired two years ago. As token budgets push us to feed models smaller and smaller fragments, each fragment has to carry its own temporal context, or precision quietly erodes.

It also helps with trust and debugging. When an agentic flow chains dozens of retrievals, knowing exactly when and where each fragment came from is what lets you trace a wrong answer back to its source.

Get provenance right on both axes, source and time, and you can do something most search still cannot: organise the past, and reason clearly about what is true now.