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

This post details the development and deployment of a multi-agent AI system to automate responses to data-related questions and assist with data pipeline enhancements for Grab's Analytics Data Warehouse (ADW) team. The system uses FastAPI and LangGraph for orchestration, with specialized agents (Data Agent, Code Search Agent, On-call Agent, Enhancement Agent) interacting with tools like Trino, Hive, Delta Lake, GitLab, Apache Spark, Airflow, Confluence, and Slack. It addresses the challenge of repetitive requests by automating context hunting and code changes, improving engineering productivity. The post contrasts this with a monolithic AI approach, highlighting the benefits of modularity and focused expertise. It also outlines two key workflows: semi-automated code changes for enhancement requests and a multi-agent investigation pathway for data issues.