DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags

Leela Kumili — Fri, 18 Sep 2026 13:50:00 GMT

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

Building an Internal Developer Platform with Artificial Intelligence

Ben Linders — Thu, 17 Sep 2026 11:11:00 GMT

Agents are becoming the new developer platform, using semantic search with data from tools like Git, Slack, and Jira for context. Things to consider are setting guardrails to block or allow things, and using logs, metrics, and traces to understand agent behavior.

By Ben Linders

InfoQ - Developer Experience - News

DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags

Building an Internal Developer Platform with Artificial Intelligence