How we built data-driven AI Golden Paths at Datadog

AI Golden Paths are standardized workflows and configurations intended to make AI-assisted development more reliable, efficient, secure, and cost-conscious across teams. Datadog’s Frontend Augmented by AI guild develops these paths by identifying undesirable agent behaviors, selecting the least expansive effective controls—such as interface configurations, automated tests and hooks, skills, or documentation—and defining ownership and implementation scope. The approach emphasizes data-driven evaluation, using controlled experiments for broad or complex controls and simpler observation for low-risk changes, while dashboards and monitoring help track cost, duration, token use, output quality, and consistency over time. In one experiment, removing bloated root-level AGENTS.md documentation improved average run duration by 13%, reduced input tokens by 16%, and lowered spending by 10%, although output consistency fell by about 7%; the team plans to recover this through more targeted controls. The process aims to prevent unnecessary context bloat while continually refining agent guidance as models, repositories, and development practices evolve.

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