DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags
Must ReadInfoQ · 2026-09-18 13:50 UTC AI
AI Summary
DoorDash deployed a multi‑agent LLM system to automate cleanup of over 60,000 stale feature flags across 623 repositories. The workflow integrates live experimentation data, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In a test of 50 flags, 45 produced usable pull requests in an average of 13.8 minutes at a cost of $4.79 per cleanup.
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Because the article showcases how LLM‑driven AI agents can dramatically accelerate large‑scale feature‑flag maintenance, directly aligning with your interest in AI agents and LLMs.
Content
DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup. By Leela Kumili