FUNDING · CODING · #625
DoorDash uses multi-agent LLMs to automate cleanup of 60,000 feature flags
DoorDash built a multi-agent LLM workflow (using Google’s Agent Development Kit and Claude models) to identify and remove stale feature flags across ~623 repositories and ~60,000 flags. In a 50-flag evaluation the system produced usable pull requests for 45 flags, averaging 13.8 minutes and $4.79 per cleanup, running agents in isolated Git worktrees, validating builds/tests/coverage, and integrating with Jira and the Model Context Protocol (MCP).
KEY POINTS
- DoorDash built a multi-agent LLM workflow (using Google’s Agent Development Kit and Claude models) to identify and remove stale feature flags across ~623 repositories and ~60,000 flags.
- In a 50-flag evaluation the system produced usable pull requests for 45 flags, averaging 13.8 minutes and $4.79 per cleanup, running agents in isolated Git worktrees, validating builds/tests/coverage, and integrating with Jira and the Model Context Protocol (MCP).
- This shows a practical, production-scale use of multi-agent LLMs to reduce developer effort and cost for large-scale code maintenance while integrating with existing CI/tooling.
WHY IT MATTERS
This shows a practical, production-scale use of multi-agent LLMs to reduce developer effort and cost for large-scale code maintenance while integrating with existing CI/tooling.