How we built LangChain’s agent-first data stack

Over the past year, the data team has overhauled its data stack to better support self-service analysis and agent-based interactions, shifting from a traditional BI tool to a system centered around Hex. This transformation enables agents to handle data requests autonomously by providing business context, clear definitions, and trusted sources, reducing the data team's bottleneck and allowing them to focus on complex queries and strategic projects. The new stack's architecture is designed to accommodate varied user needs, from polished dashboards to conversational interfaces, enhancing accessibility across the company. Contextual details, such as dbt-managed data models, semantic models, workspace guides, and endorsements, are crucial for accurate agent responses, with GitHub and observability tools facilitating ongoing improvements. The shift has led to widespread adoption, with the data agent managing significantly more requests than the data team could previously handle, and has underscored the importance of robust data modeling and context management in fostering reliable data-driven insights.

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