AgentsSeptember 18, 2026via InfoQ AI/ML
DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags
Why it matters
DoorDash's production agent system demonstrates practical autonomous workflow automation at enterprise scale: agents handling code review, validation, and git operations with human-in-the-loop approval. This is a credible case study for practitioners evaluating agents for internal tooling and technical debt reduction.
Key signals
- 60,000+ feature flags across 623 repositories
- 90% success rate (45 of 50 flags tested produced usable PRs)
- 13.8 minutes average cleanup time per flag
- $4.79 average cost per cleanup
- Multi-agent workflow with live experimentation data integration (MCP)
- Human approval gate + isolated Git worktrees + parallel agent execution
- Automated validation step
The hook
Not a pilot. DoorDash deployed multi-agent LLMs across 60,000 feature flags — 90% success rate, $4.79 per cleanup.
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…