The Analytics Data Warehouse (ADW) team at Grab supports over 1,000 users and manages more than 15,000 tables. To alleviate the time-consuming demands of repetitive tasks, the team implemented a multi-agent AI system. This system autonomously handles simpler inquiries and collaborates on more complex requests, reclaiming significant engineering bandwidth and unlocking hundreds of hours of productivity each month.

Grab is a leading technology company in Southeast Asia, offering a wide range of services, including ride-hailing, food delivery, and digital payments. Through its platform, Grab provides convenience, accessibility, and reliability to millions of users across the region. From their blog developers can learn from Grab's innovative approach to technology and business, gaining insights into building scalable, customer-centric platforms that address real-world challenges and improve people's lives.

Grab Tech Blog

Grab's Analytics Data Warehouse team was spending ~40% of engineering time on repetitive support tasks. They built a multi-agent AI system using LangGraph and FastAPI that routes Slack requests through two pathways: an Enhancement Agent for code changes (creating GitLab MRs, running tests) and an Investigation pathway with four specialized agents (Classifier, Data Agent, Code Search Agent, On-call Agent, Summarizer). Key engineering challenges solved include context/token management, tool proliferation (trimmed from 30+ tools), SQL safety guardrails with PII protection, and a human-in-the-loop review model. The system reduced issue resolution from hours to minutes, reclaimed several FTE-equivalents of engineering bandwidth, and continuously improves via annotated feedback loops.

From firefighting to building: How AI agents restored our team’s core productivity

The architecture: Two pathways, five specialized agents