AgenticDB: Self-Evolving Reconfiguration Framework for Database Workloads

Database configuration tuning is critical for workload performance, but remains difficult on real deployments. Existing tuners may leave important regions of the configuration space unexplored, require costly workload replays, and provide limited support for safely applying and recovering configuration changes. To address these limitations, we propose AgenticDB, a self-evolving agentic framework for database workload reconfiguration. Specifically, AgenticDB implements a context-grounded harness over a broad yet safe cross-layer action space spanning DBMS and OS parameters. The harness operates through two coupled technical routes. Context-grounded bottleneck diagnosis interprets workload characteristics, deployment context, and runtime-state evidence to identify likely execution bottlenecks and prescribe targeted DBMS/OS actions. Closed-loop context evolution consolidates execution feedback and reusable experience to update subsequent diagnoses, reconfiguration focus, and loop decisions, reducing low-value exploration and unnecessary workload replays. These routes are supported by an execution-aware lifecycle that validates, applies, and verifies configuration changes and restores the last safe DBMS/OS state when failures occur. We evaluate AgenticDB on MySQL and PostgreSQL using YCSB, Sysbench, and TPC-H workloads. AgenticDB achieves the strongest observed performance across all evaluated workloads, improving over the strongest baseline by 118.1