How we used AI agents to migrate GitLab rate limiting

A small team at GitLab conducted an experiment to determine if AI agents could assist in migrating part of their legacy rate-limiting system without compromising safety. The experiment was successful, demonstrating that AI agents can be effective but also highlighting areas for improvement in existing workflows. The team used GitLab, the GitLab Duo Agent Platform, and other tools to unify two rate-limiting paths into a single implementation, focusing on observability, testability, and operational consistency. The process involved a structured loop with AI agents drafting specs, implementing changes, and reviewing merge requests, while humans retained control over scope, architecture, and final reviews. The project faced challenges such as a shadow-mode miss and infrastructure constraints, which underscored the importance of human oversight and judgment. By mid-June, the migration was successfully completed for all cohorts, with plans to address the higher-volume RackAttack layer next. The experiment illustrated the value of both AI agents and the human element in achieving a successful migration.

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