From hand-tuned Go to self-optimizing code: Building BitsEvolve | Datadog

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This post details the evolution of Datadog's internal code optimization efforts, moving from manual Go code tuning to an agentic system called BitsEvolve. The manual phase focused on identifying and optimizing performance hotspots in critical, high-throughput services by analyzing assembly code and understanding real-world input distributions, leading to significant speedups and cost savings. The learnings from manual optimization, particularly the need for deep understanding of code behavior and the limitations of manual scaling, informed the development of BitsEvolve. This agentic system leverages evolutionary algorithms, inspired by research like AlphaEvolve, to automatically mutate, evaluate, and iterate on code variants against performance benchmarks, aiming to scale deep optimization work across the organization.