Configuration tuning is critical to database performance but remains difficult in real deployments. Despite notable advances, prior methods still leave substantial performance potential unexplored, suffer from low tuning efficiency, and provide limited support for configuration validation and failure recovery. To address these limitations, we propose AgenticDB, a self-evolving agentic framework for database workload reconfiguration. AgenticDB uses a large language model (LLM)-based DBA Planner to jointly reconfigure database knobs and operating system (OS) parameters through two key mechanisms. First, context-grounded bottleneck diagnosis uses workload characteristics, configuration state, and observed runtime behavior to identify the current performance bottleneck and recommend targeted database management system (DBMS)/OS reconfiguration actions. Second, closed-loop context evolution uses observed performance and runtime-state changes as feedback to update the bottleneck diagnosis, guide subsequent decisions, and terminate the reconfiguration loop when performance plateaus. It also consolidates accumulated reconfiguration experience for reuse on workloads with similar characteristics. Beyond these two mechanisms, AgenticDB improves reliability by validating each proposed configuration before applying it and automatically recovering from failures. We evaluate AgenticDB on MySQL and PostgreSQL using YCSB, Sysbench, and TPC-H. Compared with SOTA methods, AgenticDB outperforms the best-performing baseline by 118.1% on average and reduces the total time-to-best across workloads by 22.6%. Further analyses show that validation and recovery improve reconfiguration reliability. The consolidated experience also helps AgenticDB reach high-performing configurations earlier on workloads with similar characteristics.

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