Dealing with the Next Bottleneck: Build Management at AI Scale with Helen Altshuler

AI-driven software development is creating massive bottlenecks in build and test infrastructure, forcing organizations to rethink their developer workflows. As code volume and velocity surge, traditional CI/CD pipelines struggle, making pull requests a primary throttle for production. Bazel, a build system originally engineered by Google for massive monorepos, has become the industry standard for managing this scale. By leveraging remote execution and parallelization, platforms like EngFlow allow companies such as Snowflake and Databricks to handle billions of build actions while maintaining cost efficiency. Beyond raw performance, the future of build management involves making infrastructure "agent-ready," where deterministic build data enables AI to resolve dependencies and optimize build graphs autonomously. This shift is critical as organizations move away from manual code reviews toward automated, risk-based verification processes to keep pace with the rapid output of AI agents.