Data must be discoverable and trustworthy
An agent cannot produce dependable outcomes without reliable context. Structured and unstructured data needs business meaning, ownership, freshness, and access metadata so the agent can choose the right source.
Knowledge catalogs, semantic search, and unified policy can turn scattered archives into a queryable layer. Accessing data where it already lives may also reduce the risk and cost of large migrations.
Governance in the answer path
Data governance for agents must be enforced at query time. If a user cannot view a document, semantic retrieval must not place its content into model context. Propagating permission to document chunks prevents indirect leakage.
Source quality should be visible to the agent. Freshness, owner, approval state, and validity scope help rank evidence. When two documents conflict, the system should identify the stronger source or surface the ambiguity to a human.
Feedback also needs structure. A report about an incorrect answer should remain linked to the document, query, and agent version so the data team can fix the cause rather than patching one output.
From the user's question to a valid document
Suppose a user asks about a contract's status. The agent must distinguish the current version from an old draft, enforce that user's document permission, and point to the relevant clause. Semantic search alone does not guarantee this; ownership, validity dates, and approval status must travel with the text through retrieval.
Build questions with known answers and measure retrieval accuracy separately from the fluency of model output. Keep unanswerable questions and conflicting documents in the test set too. A good system exposes uncertain evidence and says when a dependable answer is unavailable.
Measure retrieval quality separately
Start with a few high-value data domains and assign owners, shared definitions, and freshness indicators. Agent answers should remain traceable to sources so errors can be corrected and user trust preserved.
This Liyan Knowledge article is an editorial synthesis based on the original source.View original source
