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# SKILLs MD for Analytics: How We Made Snowflake Intelligence Agents Reliable for Production

**[Snowflake Community](https://daily.dev/sources/snowflake_comm)** · 9 min read · 41 upvotes · 1 comments

## Summary

PDQ solved AI agent hallucinations in production analytics by encoding Standard Operating Procedures as SKILLs—version-controlled, agent-executable contracts that define inputs, logic, validation, and guardrails. Instead of scaling multiple specialized agents with long prompts, they built one agent with a library of SKILLs deployed via Git-Ops to Snowflake Dynamic Tables and indexed through Cortex Search. This approach eliminated inconsistent answers, improved quality through mandatory validation steps, and made agent reasoning auditable like code. The architecture separates SKILL discovery (lightweight semantic search) from execution (loading complete SOPs) to preserve context windows while ensuring deterministic analytical workflows.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/snowflake/skills-md-for-analytics-how-we-made-snowflake-intelligence-agents-reliable-for-production-17168d3fc613>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#data-engineering](https://daily.dev/tags/data-engineering), [#llm](https://daily.dev/tags/llm), [#mlops](https://daily.dev/tags/mlops), [#snowflake](https://daily.dev/tags/snowflake)

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