--- title: "Building a Context Layer for AI Agents" url: https://daily.dev/posts/building-a-context-layer-for-ai-agents-z4l85u5v6 source_url: https://www.snowflake.com/en/blog/snowflake-internal-context-layer-for-ai-agents type: article source: "Snowflake" published: 2026-08-17T21:07:03.035Z updated: 2026-08-17T21:07:28.388Z tags: ["ai-agents", "data-engineering", "snowflake"] reading_time: 7 upvotes: 1 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building a Context Layer for AI Agents **[Snowflake](https://daily.dev/sources/snowflake)** · 7 min read · 1 upvotes · 0 comments ## Summary Snowflake explains how it built an internal semantic layer to give AI agents and BI tools a consistent, governed understanding of business data. Semantic views sit between raw tables and consumers, translating physical schemas into standardized facts, dimensions, and metrics, improving accuracy, speed, and cost for AI agents. The post covers internal usage stats (400+ users, 5,400+ queries via a product data science agent in one month), best practices around versioning and testing semantic views with dbt, prioritizing performance with materializations and Dynamic Tables, curating and routing queries with Cortex Agents and Cortex Sense, and interoperability with the open Apache Ossie semantic standard. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How much does adding a semantic layer improve text-to-SQL accuracy for AI agents? Adding semantic context to a data model increased text-to-SQL accuracy from about 20% to more than 90% across 40 business questions tested against the TPC-DS benchmark, according to AtScale's benchmark testing. This illustrates why enterprises building AI agents on top of raw tables often see poor query accuracy without a semantic translation layer. _Anyone evaluating semantic layers for AI accuracy can track real-world benchmarks like this on daily.dev._ ### What is a semantic view in Snowflake and how does it help AI agents query data? A semantic view is a top-level Snowflake object that sits between raw physical tables and downstream consumers like dashboards or AI agents, translating governed business language into database schemas made of facts, dimensions, and metrics. This lets agents skip time-consuming table discovery and sampling, execute SQL directly, and use materialized preaggregated data for lower token cost and latency. _Teams designing agent-ready data architectures can follow semantic layer patterns like this on daily.dev._ ## Similar posts on daily.dev - [Scaling a System of Action](https://daily.dev/posts/scaling-a-system-of-action-meraxdi0n) · Snowflake Community · 1 upvotes · 0 comments - [Snowflake Semantic Views: Unleashing Structured Data](https://daily.dev/posts/snowflake-semantic-views-unleashing-structured-data-n312vpnxy) · Snowflake Community · 1 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#data-engineering](https://daily.dev/tags/data-engineering), [#snowflake](https://daily.dev/tags/snowflake) [View this post on daily.dev](https://daily.dev/posts/building-a-context-layer-for-ai-agents-z4l85u5v6)