Your agents aren't human.
Every warehouse, schema, dashboard, and query language was shaped for a human analyst. So we make agents cosplay as one. Matterbeam lets the agent pick its own model — graph, vector, columnar, search — and spins up the engine to match. Then throws it away.
Agent-provisioned materialization
immutable facts → whatever shape the agent asked forDuckDB slice
Ad-hoc columnar cut for a quick aggregate.
Graph
Traverse relationships the agent cares about.
Vector index
Similarity search, tuned to this task.
Search
Fast lookup over exactly the fields it needs.
We handed machines a system designed for meetings.
Star schemas, BI dashboards, curated marts, one blessed SQL dialect — all of it optimized for a human squinting at a report on a Tuesday. Then we point an autonomous agent at it and act surprised when it struggles.
The agent has to adapt
The infrastructure adapts
They provision through it.
The agent doesn't ask your warehouse a question and wait its turn. It describes the shape it needs, and Matterbeam materializes a right-sized engine from the raw facts — tuned to the task, alive for as long as the task, gone after.
A warehouse can't reshape itself.
It is the shape.
Snowflake and Databricks are one fixed model with a meter attached — you rent a room and query it their way. Because Matterbeam keeps everything as immutable facts, every model is just a materialization. Any engine, any shape, on demand, thrown away when the agent's done. The incumbents literally can't route work off their own meter without cannibalizing the bill.
The infrastructure should map to the agent.
Not the other decade.
Stop making machines think like a 1996 business analyst. Let them choose the model, spin up the engine, and get on with it. That's what data infrastructure looks like when you build it for who's actually using it now.