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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

Agoda Replaces 72-Shard SQL Server Price Cache with DragonflyDB

Leela Kumili — Mon, 14 Sep 2026 13:48:00 GMT

Agoda migrated its 1.5 TB hotel Price Cache from 72 SQL Server shards to DragonflyDB to handle growing read and write volumes. The migration used staged dual reads, parity validation, gradual traffic shifting, and decentralized failover detection. Agoda reports an approximately eightfold reduction in P99 read latency, with two DragonflyDB clusters providing high availability.

By Leela Kumili