In June 2026, Snowflake announced Cortex Sense, a managed service entering private preview that automatically ingests the queries your analysts have already run, the models defined in your transformation tools, and the metrics that live in your BI layer — then uses that context to ground AI agents in business meaning. The agents inherit what your analytics infrastructure already contains.
TL;DR
- -Snowflake Cortex Sense entered private preview in June 2026 and automatically ingests BI metrics, analyst queries, and transformation models.
- -Agents inherit every metric conflict, undocumented join, and access gap already present in your BI layer.
- -Snowflake's own internal testing found dozens of conflicting definitions of a single KPI across teams.
- -Agents can issue thousands of queries before a human review cycle — over-permissioned analyst roles represent a materially different risk at that scale.
- -Governance must be in place before agents are enabled, not patched in afterward.
That is the problem.
What Cortex Sense Actually Does to Your Existing BI Layer
According to Snowflake's blog, it ranks conflicting signals by relevance, authority, popularity, and freshness — a governed semantic view carries more authority than an inferred definition, and a join pattern appearing in 500 production SQL statements outweighs one appearing in three.
That ranking logic is useful, but it does not resolve the underlying problem. If your most-run queries use an informal revenue definition that differs from the one in your BI semantic layer, the agent may learn from both — and the outcome depends on which signal scores higher, not which definition is correct.
Snowflake's own internal testing illustrates the stakes directly. When the company applied Cortex Sense internally, it found dozens of conflicting definitions of "daily active users" across teams.
What the Accuracy Numbers Mean — and What They Don't
According to Snowflake's internal benchmark, AI agents operating without any context layer achieved 24.1% accuracy on analytics questions requiring cross-table joins, metric formula lookups, and filter convention knowledge. Cortex Sense raised that figure to 86.3% on the same benchmark. That is a meaningful improvement, and it is the vendor's primary argument for the product.
The number is vendor-reported and has not been independently verified. More importantly, it measures what Cortex Sense can do when it has good context to work from.
The accuracy gain is real when governance is in place. When governance is absent, agents could confidently produce answers grounded in whichever conflicting definition scores highest in the ranking model — not necessarily the correct one. That is a conditional planning scenario, not a guaranteed outcome, but it is the scenario worth auditing against before access is granted.
Why the Access Risk Is Different at Agent Scale
Databricks describes agentic analytics as embedding autonomous AI agents directly into the analytics workflow — agents that prepare data, execute queries, generate insights, and surface findings without waiting for a human to initiate every step. That autonomy is the value proposition. It is also the governance exposure. An agent running the same query at scale does not pause.
What to Audit Now
The following five-point audit reflects Metrotechs guidance based on the cited Cortex Sense architecture and data dependencies. It is not a Snowflake-published checklist.
Metric definitions. Verify every analyst-facing metric has a formal definition, an assigned owner, and documented business logic. If multiple teams have defined the same KPI differently — revenue by booking date versus invoice date, for example — resolve the conflict and establish a single governed definition before agents ingest either version. Conflicting definitions do not disappear when agents arrive; they may get encoded into agent behavior.
Analyst query patterns. Identify your highest-frequency production queries. Confirm which tables and metrics they depend on, and flag any that rely on undocumented joins or raw source tables rather than governed semantic views.
Data lineage. Trace each BI metric back to its source ERP or operational record through every transformation step. Confirm that lineage is documented end-to-end and that breaks or undocumented hops are resolved. If you cannot trace a metric from your Snowflake warehouse back to the originating ERP transaction, an agent acting on that metric is operating on an unverified number.
Metric ownership and refresh schedules. Confirm each metric has a named owner and a defined refresh cadence, and that a process exists to update definitions when underlying business logic changes. A metric whose owner left the company six months ago and whose refresh schedule is undocumented is not ready for agent access — the agent has no way to know the definition may be stale.
Role-based access controls. Verify that Snowflake role assignments are scoped correctly for agent use, not just for human analysts. Scope agent roles to only the data objects required for the specific workflows you intend to automate.
What to Watch as Cortex Sense Moves Toward General Availability
Cortex Sense is in private preview as of June 2026. That capability would allow you to limit which metrics and query patterns each agent role can access — but it requires clean role definitions to be useful. If your current Snowflake roles are broadly permissioned, per-role context scoping will not protect you until those assignments are tightened.
The governance audit is the prerequisite. The access decision comes after.
If your BI layer needs a governance review before enabling agentic access, learn about Metrotechs data and analytics services.
Sources and supporting resources