The context platform
The context layer every agent shares.
Agentlake reads your data as it lands, infers how tables relate and what columns mean, and keeps one shared context graph that every agent reads from and writes back to. Your data team owns it; every answer inherits it.
What Agentlake does
One graph, continuously learning from your stack.
Reads as data lands
Schema and sample observations stream in continuously, so the catalog reflects what is actually in your warehouse, not a stale wiki page.
Infers meaning and relationships
Embrasure proposes joins, entities, metrics, and dimensions, each with a confidence score and the evidence behind it.
One graph every agent shares
context.query returns the right objects, paths, and observations for a task; context.write lets agents and people record what they learn.
Provenance on every edge
Declared keys, semantic joins, and inferred links each carry their source and confidence, so you can see why two tables connect.
Retrieval you can audit
Every assembled context carries a trace of what was searched, traversed, and matched to ground the answer.
Owned by your data team
Assertions and rules your team confirms become durable truth the whole organization builds on.
Why context is the difference
The right context is the difference between a correct answer and a confident wrong one.
Most agents fail not because the model is weak but because they are missing the one join, definition, or rule that makes an answer correct. Agentlake is where that knowledge accumulates: the more your data team confirms, the better every agent gets, and none of it has to be rebuilt for the next question.