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If an enterprise AI agent answers “What was revenue in Q1?”, correct SQL is only half the answer. If the business definition changed from “Recognized Revenue” to “Recognized Revenue − Approved Adjustments,” the agent also needs a rule for which definition applies to Q1. Versioning business semantics makes that choice explicit and preserves the meaning of earlier answers.
Why business definitions need versions
A query can run successfully against the right data and still produce an answer that cannot be interpreted consistently over time. The problem is not necessarily the SQL: the business meaning of revenue may have changed while its name stayed the same.
For example, an organization might first define Revenue as “Recognized Revenue,” then approve a revised definition that subtracts Approved Adjustments. If the revised expression simply replaces the old one, a later query for Q1 may silently use a different meaning from the one used in an earlier report. Versioning keeps the concept’s identity stable while preserving distinct, reviewable definitions. The practitioner guidance in the semantic-versioning article treats this as a design recommendation, not a formal industry standard.
What to record for each semantic version
Model a business concept as a stable object with versioned definitions, rather than as a mutable label attached to one current expression. A useful record includes:
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- Stable identity: a durable identifier for the concept, such as
revenue, independent of its wording or formula. - Version and definition: a version identifier, human-readable explanation, and executable expression or calculation logic.
- Owner and lifecycle state: the accountable business or data owner and a state such as draft, approved, published, or deprecated.
- Business effective interval: the period in which the definition is intended to apply, with explicit start and end boundaries where relevant.
- Publication and approval provenance: when it was approved or published, who authorized it, and the rationale or change record.
- Dependencies and mappings: other metrics, dimensions, models, or physical data fields that depend on it, plus the mapping to the underlying data.
This separates the durable idea from the changing rule used to calculate it. It also gives an agent enough context to select a version and lets reviewers see what that selection means.
Keep publication time separate from effective time
A semantic change has at least two relevant timelines. System time records when the organization approved, published, or made a version available in its catalog. Business effective time records when the organization intends that definition to govern the data.
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Those dates need not match. A revised definition might be approved in April but intended to apply starting January. Storing only the publication date could make an agent apply the new definition too late; storing only the effective date could obscure when the organization learned of or authorized the change. Record both, and define how the system should handle a query when the requested period and publication history intersect.
Choose a policy for historical questions
“What was Revenue in January?” can ask for different things. The policy should be explicit rather than inferred from whichever version happens to be current.
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| Interpretation | Meaning | Useful when |
|---|---|---|
| As was | Use the definition that was in force for January at the time, preserving the historical interpretation. | Reproducing a prior report, audit trail, or decision. |
| Restated | Apply the current definition to January’s data, even if that definition was approved later. | Re-expressing historical results under today’s business rule. |
Neither interpretation is universally correct. The choice depends on the reporting purpose and the organization’s governance policy. An agent should identify or request the intended mode when the question does not make it clear.
Make cross-period comparisons semantically comparable
“Compare Q1 and Q3 Revenue” raises a related but distinct issue. If a definition changed between those periods, using each period’s then-current version may preserve historical meaning but make the numbers less comparable. Applying one current version to both periods may improve consistency under today’s rule, but it changes how the earlier period is represented.
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Set an organizational policy for comparisons across a definition change: use period-specific definitions, restate all periods to one selected version, or present both views when the distinction matters. Record the selected version for each period so the comparison is explainable. Do not let an agent mix versions without signaling that choice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Govern changes before they become authoritative
A practical release process makes semantic changes visible and reviewable instead of allowing a draft to become the agent’s answer by accident:
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- Classify materiality. Decide whether a change is wording-only, a mapping correction, or a change to calculation or business interpretation. Route material changes for appropriate review.
- Review a semantic diff. Show what changed in the definition, expression, effective interval, or mapping—not just a code diff.
- Check dependencies. Identify downstream metrics, dashboards, models, and agent contexts that could change meaning or results.
- Validate the candidate. Test the calculation and its mappings against relevant data and expected cases before approval.
- Approve and publish deliberately. Enforce ownership and access controls, record approval provenance, and prevent draft or deprecated versions from being treated as authoritative.
- Preserve prior versions. Retain the definitions and metadata needed to interpret earlier reports and reconstruct historical answers.
Record semantic lineage in every agent answer
For an answer that may need review, log more than the SQL or query text. Preserve the resolved semantic object, selected version, effective date or interval, interpretation policy (such as as-was or restated), and mapping to the physical data. Together with the query and its result context, these details help establish what the answer meant when it was produced.
Where current platform features fit
Catalog and analytics platforms can provide useful building blocks, but a feature set is not proof that the complete versioning policy is implemented or that an agent will return correct answers.
- Databricks Unity Catalog: its documentation describes business metrics, terms, organizational structures, reusable metric views, governed Pages, and certification or deprecation signals. Metric views separate measure definitions from dimensions and are documented for use across SQL, notebooks, dashboards, Genie Agents, alerts, and external BI. See Unity Catalog semantics and metric views.
- Microsoft Fabric IQ: Microsoft describes shared business context over OneLake data, Power BI semantic models, and ontology. Its ontology documentation covers entity types, properties, relationships, data bindings, and agent grounding; the documentation labels ontology as preview, so availability can change. See Fabric IQ and Fabric ontology.
These documented capabilities can support semantic organization and reuse. Organizations still need to decide how versions, effective dates, approvals, historical questions, cross-period comparisons, and answer lineage work in their own environment.
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