Back office ops · Production

DoorDash builds a multi-agent internal AI platform for unified data and operations intelligence

The problem

DoorDash's knowledge was distributed across experimentation platforms, metrics hubs, dashboards, wikis, and Slack, requiring significant context-switching to answer complex business questions.

First attempt

Existing self-service tools were sub-optimal because they assumed users already knew which data sources to query and how to interpret them correctly; skillset gaps and the risk of misinterpretation limited their usefulness for critical analyses.

Workflow diagram · grounded in source
1
User submits business query
trigger
“Investigate the drop in conversions in the Midwest last week”
2
Multistage contextual retrieval
ai_action
“our engine addresses this by using an algorithm that combines traditional best-match-25 keyword search with dense semantic search, followed by a sophisticated re-ranker using reciprocal rank fusion, or RRF”
3
Schema-aware SQL generation
ai_action
“we use our DescribeTable AI tool with pre-cached examples. This tool provides the agent with compact, engine-agnostic column definitions. Crucially, it enriches this schema information with example values for each column that are pre-cac…”
4
Statistical query validation
validation
“rigorous, multi-stage validation process we call Zero-Data Statistical Query Validation and Autocorrection. This includes automated linting for code style and markdown enforcement, but its core is an EXPLAIN-based check for query correct…”
5
LLM-as-judge quality evaluation
feedback_loop
“automated LLM-as-judge evaluation framework. For a platform intended to guide high-stakes business decisions, "good enough" isn't an option. This framework systematically runs predefined question-and-answer scenarios against our agents. …”
6
Answer delivered in Slack or IDE
output
“An analyst investigating a trend can pull data directly into a Slack conversation, or an engineer can generate boilerplate code without leaving their editor”
Reported outcome

DoorDash's agentic platform now enables operators to get trustworthy, evidence-backed answers in seconds, democratizing data access for business leaders and operations managers without requiring them to write SQL.

Reported metrics
Time to answeranswer in seconds, not hours
Decision-making and execution speeddramatically accelerating decision-making and execution across the company
Reported stack
LangGraphGoogle DocsGoogle SheetsSlackCursorJiraDeepEvalMCPA2Avector database
Source
https://careersatdoordash.com/blog/beyond-single-agents-doordash-building-collaborative-ai-ecosystem/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

DoorDash's agentic platform now enables operators to get trustworthy, evidence-backed answers in seconds, democratizing data access for business leaders and operations managers without requiring them to write SQL.

What tools did this team use?

LangGraph, Google Docs, Google Sheets, Slack, Cursor, Jira, DeepEval, MCP, A2A, vector database.

What results were reported?

Time to answer: answer in seconds, not hours; Decision-making and execution speed: dramatically accelerating decision-making and execution across the company (source-reported, not independently verified).

What failed first in this deployment?

Existing self-service tools were sub-optimal because they assumed users already knew which data sources to query and how to interpret them correctly; skillset gaps and the risk of misinterpretation limited their usefu…

How is this back office ops AI workflow structured?

User submits business query → Multistage contextual retrieval → Schema-aware SQL generation → Statistical query validation → LLM-as-judge quality evaluation → Answer delivered in Slack or IDE.

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