engineering.grab.com
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ksl
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Grab’s Analytics Data Warehouse team deployed a multi-agent system – five specialized agents handling schema queries, code search, on-call triage, and data enhancement – to reclaim roughly 40% of engineering time lost to repetitive operational tasks. The stack runs on FastAPI, LangGraph, Redis, and PostgreSQL, with each agent scoped to a narrow domain rather than one monolithic LLM doing everything. That architectural choice matters. Teams across Southeast Asia’s largest super-app were drowning in Jira tickets and Slack questions that didn’t require deep engineering judgment, just fast context retrieval. The pattern of decomposing toil into agent-sized chunks instead of building a single chatbot is becoming a recurring playbook at companies operating large internal data platforms.
