How Zscaler cut PR review time by 90% using dbt context and multi-agent AI (OpenAI)

Zscaler, a leading cloud-based cybersecurity company, significantly reduced its pull request (PR) review time by implementing an AI-powered multi-agent system called PRISM (PR Review Intelligence System Mentor), incorporating dbt's structured context, OpenAI, and other tools like GitHub and Snowflake. This innovation cut reviewer time by 90%, saving an estimated 2,100 engineering hours annually. As Zscaler expanded, the need for efficient data governance grew, leading to the development of this system to automate and streamline the PR process, which was previously a bottleneck due to the increasing volume and complexity of reviews. By leveraging dbt's context, including lineage, CI performance metrics, and metadata, the system automates governance and provides targeted feedback, enhancing data quality and enabling faster development cycles. The transition from a centralized to a self-service analytics model initially increased data velocity but highlighted governance challenges, which PRISM effectively addresses by ensuring consistent enforcement of standards and improving overall workflow efficiency.

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