From analytics engineer to context engineer

dbt Labs argues that enterprises can reduce the cost and improve the reliability of AI agents by moving raw structured, semi-structured, and unstructured data from vendor MCP connections into a governed context layer in their data warehouse. Drawing on its experience with Gong call transcripts, the company says that directly querying raw data through AI tools created high token and API costs, while ingesting, summarizing, and modeling the same data in the warehouse reduced transcript volume by 20 times and token costs by roughly 98%. The post describes “context engineering” as an extension of analytics engineering in which data teams prepare business context for AI agents rather than only metrics for dashboards, incorporating sources such as emails, support tickets, PDFs, chat logs, and recordings. It recommends compressing, enriching, describing, and governing data; processing expensive LLM reads once in batch jobs; maintaining context incrementally; and providing a shared, reusable context layer accessible to multiple agents. The proposed approach uses Fivetran to move and index data and dbt to model, orchestrate, and apply warehouse-native AI functions, positioning data teams as central owners of trusted AI context without requiring a major change to their existing data stack.

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