7 Checks Before Deploying Analytics Agents
DaxtonValehart · reddit · 2026-07-14
This post emphasizes that the semantic layer is not a data dictionary, but executable business logic. Even if an analytics agent generates valid SQL, it can still deliver incorrect business answers due to flaws in metric definitions, join relationships, or time semantics.
Before deploying an analytics agent to production, the author recommends checking 7 key areas:
- Ground Truth Set: Manually calculate answers for 10 real-world questions to serve as a regression baseline.
- Paraphrase Consistency: Asking about the same metric using different phrasing must yield consistent results.
- Joins and Granularity: Clearly define 1:1, 1:many, and many:many relationships along with aggregation granularity to avoid duplicate counts.
- Permissions: Do not rely solely on table-level permissions; implement access control within the semantic layer as well.
- Ownership and Versioning: Metric definitions must be versioned to track changes easily.
- Cross-Interface Consistency: Agents, dashboards, APIs, and workbooks must all return the exact same set of metrics.
- Regression Testing: Re-run historical samples after any definition change to ensure no unexpected drift occurs.
In summary, the semantic layer acts as a contract, and the agent is merely one of its access points.
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