Make money doing the work you believe in
DoorDash is building AI agent swarms that it says will work like ant colonies.
Most companies are still figuring out how to get a single AI agent to reliably complete a task.
DoorDash has already moved through three phases:
Deterministic workflows (simple, predictable, no agentic capabilities)
Single agents (one agent reasons through a problem, queries multiple tools, delivers an answer)
Swarms (peer agents passing work between each other asynchronously)
The swarm model is still experimental. But for a logistics business juggling drivers, restaurants, customers, and real-time pricing, a swarm model could have a significant impact.
What makes all of this possible is their sophisticated AI agent infrastructure (see attached).
Their architecture connects source data (Snowflake, Google Drive, Confluence, BI reports), a vector database for searchable knowledge, a context builder for RAG and few-shot prompting, and a toolkit of specialized tools (SQL, Slack, Wiki, feedback processors).
If you're building agents internally, Doordash is a fine example of why infrastructure is so important.
I go deeper into this and other AI agent use cases in the latest DoP Deep Dive: departmentofproduct.sub…
