AI ReliabilityPRODUCT AI-GOVERNANCE
AI Governance
Evidence that an AI system is under control
The same traces that debug an agent are the raw material for governance: what the system did, what it was allowed to do, what it cost, and who approved the change. The intent is to make that record queryable and exportable rather than reconstructed by hand before a review.
What it is meant to do4 CAPABILITIES
The capability we are building toward
- 01
Policy over traces
Express what an agent is permitted to do, and evaluate real traffic against it rather than reasoning about it in a document.
- 02
An auditable record of change
Which prompt, model, retrieval config and filters were live when a given decision was made — versioned together rather than separately.
- 03
Model and prompt inventory
A current answer to which models are in use, where, and by which team.
- 04
Exportable evidence
Governance questions arrive on someone else's schedule and in someone else's format. Export should not require an engineer.
Why it lives here
One pipeline, not another agent to install
AI Governance reads the same instrumented stream as everything else on the platform. That means one collector in your infrastructure, one redaction policy applied before anything leaves it, and one usage bill — rather than a separate vendor, agent and contract per question you want answered.