Designing a Multi-Agent System for Engineering Support at Scale: A Case Study From Grab

Grab’s Analytics Data Warehouse team introduced a multi-agent AI system to automate engineering support workflows across a large-scale data platform. The platform supports more than 1,000 internal users and manages over 15,000 tables. As usage increased, repetitive operational support and ad hoc investigations consumed significant engineering time. The multi-agent system handles internal requests such as data warehouse troubleshooting, SQL debugging, and platform support, shifting engineers toward development work. Requests are separated into investigation and enhancement workflows. Investigation workflows perform diagnostics like query analysis, log retrieval, schema lookup, and issue summarization. Enhancement workflows generate actionable outputs such as code changes, SQL fixes, and automated merge requests for review. The system uses a LangGraph-based workflow engine with FastAPI services for routing, tool execution, and state management.

"Grab's Analytics Data Warehouse (ADW) team has introduced a multi-agent AI system to automate engineering support workflows across its large-scale data platform, aiming to reduce repetitive operational work and improve resolution efficiency. The system is designed to handle internal engineering requests spanning data warehouse troubleshooting, SQL debugging, and platform support, while shifting engineers toward higher-value development work."

"The ADW platform supports more than 1,000 internal users and manages over 15,000 tables, serving as a core analytics infrastructure component within Grab. As usage grew, the engineering team observed that a significant portion of operational effort was being consumed by repetitive support tasks and ad hoc investigations, limiting time available for platform improvement and system design work."

"Grab's Central Data Team is leveraging a multi-agent system to automate repetitive operational work, reclaiming hundreds of engineering hours each month. This shift is unlocking critical engineering bandwidth and enabling a transition from reactive firefighting to higher-value system building."

"To address this, the team implemented a multi-agent architecture that separates incoming engineering requests into two primary workflows: investigation and enhancement. Investigation workflows are designed for diagnostic tasks such as query analysis, log retrieval, schema lookup, and issue summarization. Enhancement workflows focus on generating actionable outputs, including code changes, SQL fixes, and automated merge requests for review."

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