How a reusable benchmark fleet made Triton configuration measurable across hundreds of production and shadow models, with mean QPS lift rising from 1.75x at 4 vCPU to 6.78x at 32 vCPU.
Category Archives: Gen AI
Inside Vera, DoorDash’s Data Agent
The DoorDash Data team is responsible for generating accurate data insights and answers to key business questions:
Why did the order volume dip in California last week?
How has a new product been pacing since launch?
Which merchants are at risk of churning from our marketing products?
Automating Feature-Flag Cleanup at Scale with a Multi-Agent LLM System
DoorDash’s experimentation platform manages over 60,000 feature flags across roughly 623 repositories.
Delegating Engineering Work To Cloud-Based Agents
Flux is DoorDash’s cloud-based agents platform for engineers.
How DoorDash Built a Centralized Gateway for AI Agent-Tool Access
AI agents become useful when they can take action in real systems.
Building Ask DoorDash (Part 4): A Platform for Building and Evolving Agents
Introduction
We built Ask DoorDash on a common platform that lets domain teams build and evolve their agents without rebuilding the systems beneath them.
How we learned to trust our AI code reviewer at DoorDash
How we built a measurement layer that tells us where, why, and how much to trust an agentic code reviewer, and why a single metric never could.
Building Food Metadata with LLM Juries, Context Optimization & Multimodal AI
DoorDash serves a vast and diverse set of merchants, with every restaurant, menu, and dish expressed in its own unique way.
Building Ask DoorDash (Part 3): Evaluation
Following our earlier engineering overview of Ask DoorDash, this third post in the blog series takes a close look at the evaluation harness behind the system.
Inside One Engineer’s Journey to Master Long-Running Agents
Here comes the mandatory “AI Moves Fast” disclaimer: when I first drafted this article in early March, I began by saying something about how “AI is coming.” and how we should get ready for it.