DBAgent: An RL-Based Agent for Autonomous Database Operations and Maintenance

Abstract

Database Operations and Maintenance (O&M) is a critical but complex and labor-intensive task. Recent LLM-based assistants promise to lower the barrier by reading manuals/tickets and exploring diagnostic search trees. However, existing LLM-based solutions fall short due to fundamental limitations in learning from expert demonstration. Such design fails to internalize domain dynamics (how interventions change plans, resources, etc.), and struggles under workload and statistics drift. To address this, we present DBAgent, an autonomous agent for Huawei Cloud Data Warehouse Service (DWS) integrated with Autopilot (DWS's production monitoring, alerting, and auto-remediation service). DBAgent consumes DWS telemetry (e.g., KPIs and execution plans) and Autopilot alerts to diagnose and remediate incidents in production clusters. DBAgent emulates an expert's iterative Think-Act-Observe problem-solving loop with a policy trained via reinforcement learning (RL). It couples dynamic tool use for information gathering, a multimodal perception module for database-native signals, and an RL-based reasoning engine that plans, verifies, and generates evidence-grounded remediation recommendations. Extensive experiments show that DBAgent handles a broad range of complex O&M tasks. It surpasses the strongest baseline by +23% success rate and produces evidence-grounded reports with low hallucination (~5%) on DWS benchmark.