Databricks has developed an AI-assisted platform to streamline the debugging process for thousands of databases across major cloud providers like AWS, Azure, and GCP. Before implementing this AI solution, engineers faced significant challenges due to fragmented tools and processes. For example, during a typical MySQL incident, engineers had to switch between various dashboards and command-line interfaces, wasting time and complicating investigations. The new platform consolidates metrics and logs, allowing engineers to query service health and performance using natural language, drastically reducing debugging time by up to 90%. The project began during a hackathon, where a prototype was created to unify key database metrics into a single view. As they gathered feedback from engineers, Databricks identified common pain points: junior staff struggled with where to start, and senior engineers found existing tools cumbersome. The team observed on-call sessions to understand these issues firsthand. This led to the realization that merely providing data wasn’t enough; there needed to be intelligent guidance to help engineers navigate troubleshooting effectively. A pivotal development was the introduction of a chat assistant that transformed the debugging process by making it interactive. The team focused on creating a strong foundation for the platform, emphasizing centralization, fine-grained access control, and unified orchestration across different regions and regulatory domains. By centralizing data and context, they enhanced the AI’s ability to reason over operational issues, which included overcoming challenges related to context fragmentation and governance boundaries. This allowed for effective integration of AI capabilities, enabling engineers to retrieve essential information quickly and efficiently, thereby improving overall operational efficiency.