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DoorDash Automates Feature Flag Cleanup with Multi-Agent LLMs

By PulseAugur Editorial · [1 sources] ·

DoorDash has developed an automated system using multi-agent LLMs to clean up stale feature flags, which are a source of technical debt and operational risk. The system integrates with MCP for live rollout data and Jira for ticket processing, and it successfully generated pull requests for 90% of evaluated stale flags. This automated cleanup process is significantly more efficient than manual methods, with each cleanup averaging $4.79 and taking 13.8 minutes. AI

IMPACT Demonstrates a scalable, cost-effective LLM solution for managing technical debt in large codebases.

RANK_REASON Academic paper detailing an industrial application of LLMs for software engineering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DoorDash Automates Feature Flag Cleanup with Multi-Agent LLMs

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Academic paper detailing an industrial application of LLMs for software engineering. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Jai Datar ·

    Automating Feature Flag Cleanup at Scale with Multi-Agent LLM Systems: An Industrial Experience at DoorDash

    Feature flags are runtime configuration mechanisms used for gradual feature rollouts, experimentation, and kill switches. Once a feature is fully rolled out, the flag and its gated code become technical debt and can introduce operational risk through inadvertently enabling dorman…

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