A data team experimenting with Snowflake Cortex agents for semantic analytics reports inconsistent query outcomes and trust issues across 300 stakeholders.

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    "summary": "A data team experimenting with Snowflake Cortex agents for semantic analytics reports inconsistent query outcomes and trust issues across 300 stakeholders.",
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      {
        "url": "https://www.reddit.com/r/snowflake/comments/1xjx8xq/comment/19rwoeqa7jrtt/",
        "depth": 0,
        "score": 5,
        "author": "ArielCoding",
        "excerpt": "Metrics defined in Tableau live in Tableau, so an AI tool querying Snowflake can’t see them and may write its own logic for things like revenue or churn. Snowflake can import a Tableau workbook to build a semantic view from those definitions, giving AI one official source, and checking its answers against question you already know the answers to is how you earn trust before rollout.",
        "posted_at": "2026-10-31T01:43:36.000Z",
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        "author": "FriendlySyllabub2026",
        "excerpt": "If the tables the semantic view is over have access to root level data, semantic views and cortex agents can absolutely help you.\n\n I’d start out testing Semantic View Auto Pilot, which lets you use a tableau report to creat the semantic views automatically, then use coco to refine it.\n\n Good luck!",
        "posted_at": "2026-10-31T01:54:00.000Z",
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        "author": "Apprehensive_Tie2494",
        "excerpt": "Got it, we tried few metrics into semantic models and agents are getting sql and executing from the root level but the answers are slightly different for each user. Sometime they are getting same for all and sometimes kind of slightly different outcomes which is not right at all,",
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        "author": "Apprehensive_Tie2494",
        "excerpt": "Roughly 20 queries. The problem is stakeholders are verifying with their original query after the agent run so they are not confident on AI queries and also 300 stakeholders how they should trust confidently like these are right and certified ? Cortex, Claude , Gemini are generating results but stakeholders trust is the key without going back to verify their queries.",
        "posted_at": "2026-10-31T22:25:34.000Z",
        "author_url": "https://www.reddit.com/user/Apprehensive_Tie2494/"
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        "url": "https://www.reddit.com/r/snowflake/comments/1xjx8xq/comment/19ryzy1mr8yaq/",
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        "author": "refaelos",
        "excerpt": "+1 to checking against questions you already know the answers to, but I'd keep doing it after rollout, not just before. re-run that list every time a model or schema changes, so drift shows up before a stakeholder finds it. and for the \"why\" questions, which a semantic layer won't answer for you anyway, make every answer show the SQL it ran and when the data last landed. people trust an answer the",
        "posted_at": "2026-11-01T08:56:30.000Z",
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      "[comment u/Apprehensive_Tie2494] Cortex, Claude , Gemini are generating results but stakeholders trust is the key without going back to verify their queries."
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    "post_text": "In my org we have built a models, relationships, business definitions including proper documentations, and tableau manage our standard metrics. The stakeholders are asking like deep analysis at root level analytics rather just metrics like what changes, why changed, trend on one account etc. so I felt building with semantic layer would not solve what stakeholders are looking for so what are your thoughts on it? If we run with existing models how we could know AI is generating certified answers for all stakeholders? How we could build the trust? kind of confusing. Is this something happening in your org as well?",
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