Databricks transformed its database debugging process by developing an AI-powered platform that slashed debugging time by up to 90%. Before this, engineers had to navigate multiple tools—Grafana for metrics, internal dashboards for client workloads, command-line interfaces for MySQL status, and cloud provider consoles for slow query logs. This fragmented approach was inefficient and often led to human error. The initial attempts to streamline the process during a hackathon showed promise, but it became clear that direct observation of engineers was needed to understand their real challenges. The engineering team faced several issues. Fragmented tooling meant engineers had to gather context from various sources before addressing problems, taking up valuable time. There was also a lack of clear guidance on effective mitigation actions, forcing engineers to either wait for senior experts or conduct extensive investigations themselves. The platform evolved through iterations, starting with a static checklist that failed to resonate with users. Subsequent versions focused on anomaly detection but still fell short of providing actionable insights. The breakthrough came with an interactive chat assistant that enables engineers to engage in a dialogue, asking follow-up questions and receiving tailored guidance. This shift turned debugging from a sequence of isolated actions into a continuous, conversational process. Building this platform required a robust architectural foundation to manage the complexity of thousands of database instances across multiple regions and clouds. Key architectural principles included a central-first sharded architecture to prevent context fragmentation and ensure secure, efficient access to debugging data.