Why an AI agent's revenue query was $3.5 million wrong, and how AWS fixed it with dbt Semantic Layer
A plausible AI revenue query was $3.5 million wrong. AWS explains how business context helps prevent those errors.
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How Ecolab used an AI migration agent to shrink 84,000 lines of legacy SQL by 6x
Ecolab and Datafold used an AI migration agent to rebuild a decade of tangled Snowflake logic as clean, documented dbt models.
Ship AI agents like tables: dbt materializations for Snowflake CoWork
Under Armour turned Snowflake AI agents into dbt models, giving every agent the same review and CI process as every table.
Why a semantic layer is the difference between a 96% and an 80% accurate AI analyst
Why conversational analytics stalls and the data fundamentals that make it reliable.
Why dbt Labs thinks AI agents will finally make the semantic layer stick
Why AI agents can make semantic layers stick, if teams can solve the trust problem.
What Okta learned governing context for AI agents at scale
Okta rebuilt how it governs context for AI agents, using dbt as the single source of truth.
Showing our work: a controlled experiment in context engineering
We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.