Your agents need rules as well as access
An agent pointed at your warehouse will answer confidently and sometimes wrongly, and you will have no record of how it got there. TopQuadrant sits between the agent and your data: serving governed definitions, enforcing entitlements per request, and logging what was asked, what was returned, and which governed assets it resolved against.
Trusted to govern the data behind the world's most regulated decisions.
Agents fail on ungoverned data in a specific, predictable way
They pick a plausible table, apply a plausible definition, and return a plausible number. Nothing errors. The failure is silent, which is why model risk and audit functions are now asking the same question about agents that they ask about models: what did it read, what did it mean, and who approved that. Governed context is how you answer.
More accurate answers when the agent resolves against governed concepts instead of raw schemas.
Cheaper to operate than rebuilding context per agent and per application.
Of served requests logged with the governed assets and entitlements they resolved against.
Governed context for agents
Serve meaning, so the agent stops guessing
Agents call the governed layer and receive concepts, definitions, permitted relationships, and metric logic. They no longer have to guess which of forty tables holds the authoritative version, because the question resolves against the model rather than the physical store.
- Definitions on request. The agent receives what a term means before it composes a query.
- Constrained joins. Permitted relationships come from the ontology, so invented joins are not available.
- Standard interfaces. Expose governed context over API, SPARQL, and MCP so any agent framework can consume it.
Entitlements & guardrails
The agent inherits its caller’s permissions
Access rules are evaluated per request against the identity of the person the agent is acting for. A restricted field stays restricted whether it is requested through a dashboard or through a conversation, and prompt phrasing does not change the answer.
- Per-request evaluation. Entitlements resolve at query time, not at index time.
- Concept-level policy. Rules attach to governed assets, so they hold across every access path.
- Refusal with a reason. Blocked requests return why, so the user knows to request access rather than retry.
Traceability & evidence
Every answer has a record
Each request captures the question, the governed assets resolved, the entitlements applied, the data returned, and the version of every definition in play. That record is what a model risk review, an internal audit, or an AI Act conformity assessment asks for.
- Full request log. Question, resolution path, and result, retained and queryable.
- Version pinning. Know which version of each definition the answer was built on.
- Reproducible answers. Re-run a historical question against the definitions as they stood that day.
Model & agent inventory
Know which agents touch which data
Agents, the data domains they reach, their owners, and their review status are governed assets in the same graph as the data. When someone asks for the inventory, it is a query rather than a spreadsheet exercise.
- Inventory as data. Agents, owners, purposes, and approval status held and queried in one place.
- Data domain mapping. See which governed domains each agent is entitled to reach.
- Review cadence. Track validation status and next review date per agent.