Whitepaper
1 min read

Grounding enterprise AI: the architectural options

Three ways to give an AI agent context. Only one of them survives an audit.

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The world that was

Metadata management platforms cataloged and governed data for human analysts. AI agents weren't part of the design, and now every agent is guessing at context nobody compiled for it.

The shift

Agents don't just read dashboards. They generate SQL, cite figures, and act on definitions that used to live only in a person's head. Guessing at context, silently, is now a production risk.

Why patching fails

Two common fixes, and where each one stops short.

Score What it looks like
0 Metadata exists for the BI layer only
1 Warehouse and BI covered; transformation and sources dark
2 Cloud-native systems covered; legacy, on-prem, or file-based sources dark
3 Full path covered with one or two known gaps you can name
4 Every hop from origin to consumption has a metadata record

Runtime semantic layers

A new dependency sitting in the query path, not a fix for the underlying disagreement.

Document-inferred ontology graphs

Aggregated and described, but not enforced anywhere downstream.

The architecture

Compiled context: definitions resolved once, at compile time, and pushed into every system and every agent that consumes them. Deterministic, not inferred. Request a demo to see how it's built.