How we ground agent actions in your knowledge base

A chatbot that hallucinates gives you a wrong sentence. An agent that hallucinates and then acts on it can send the wrong email, update the wrong record, or approve something that should have been escalated. The bar for grounding has to be higher once an agent can take action.

Retrieval before planning, not planning before retrieval

A common agent architecture plans first, then looks things up as needed during execution. We do it in the other order for anything touching your organization's data: before an agent proposes a plan, it retrieves relevant context from Xynora Workspace and includes it directly in the planning step. The agent is reasoning with your actual documents and records in front of it, not deciding what to look up based on an unverified assumption.

Every claim traces back to a source

When an agent's plan references a fact — a policy, a customer record, a prior decision — that reference is stored as a citation, the same mechanism used for search answers in Xynora Workspace. If a human reviews the run afterward, they're not reading a summary of what the agent believed; they're reading exactly what it retrieved and where it came from.

Boundaries are explicit, not implied

None of this replaces the need for approval boundaries. Grounding reduces the odds an agent acts on wrong information; it doesn't replace a human checkpoint on the actions your organization decides need one. Both matter, and we built for both from the start.