DoorDash Builds Open Data Architecture for Agentic AI

SiliconANGLE reports that DoorDash has spent about 10 years constructing an open data architecture founded on open storage, open compute, and compute-agnostic design to serve consumers, merchants, and delivery workers across real-time logistics. Speaking at Snowflake Summit 2026, Jajoo, head of data engineering, data platform, and business intelligence at DoorDash, said "the machine user is outpacing the human user in consumption of analytics data." SiliconANGLE and Chris Child, Snowflake's vice president of product for data engineering, described how DoorDash adopted Apache Iceberg across its estate to reduce data-movement costs and latency, noting the open approach is cheaper and faster because it removes repeated copy steps between systems.

ByteByteGo reports that DoorDash built an LLM testing system after observing subtle hallucinations in its customer-support chatbot, with examples where the model misread order-status fields and recommended incorrect refund policies. ByteByteGo notes DoorDash handles hundreds of thousands of support contacts every day, which motivated investment in automated testing for model deployments.

Industry-pattern observation: at petabyte scale, organizations increasingly prefer open data formats and compute-agnostic stacks to avoid repeatedly copying data between systems. DoorDash's approach aligns with a broader enterprise move toward table formats like Apache Iceberg to reduce egress, lower latency, and let engineers focus on application logic rather than data plumbing.

For practitioners, the two stories together highlight two operational pillars for scaling agentic AI in production: an open, queryable data substrate that minimizes expensive hops and keeps fresh state available to models; and robust, scenario-driven testing plus cost telemetry for LLM behavior. The pillars address operational cost and model reliability in different parts of the stack.

Track adoption of open table formats and compute-agnostic tooling across other logistics and marketplace platforms, and whether more teams pair data-platform investments with systematic LLM testing and spend monitoring to catch subtle hallucinations and cost regressions early.