From Knobs to Actions: Agentic Bayesian Optimization for Self-Improving System Tuning

Modern systems such as Large Language Model (LLM) inference engines and databases expose large configuration spaces with strongly interacting parameters. Their workloads can change, making automated tuning essential. Bayesian optimization (BO) is sample-efficient for expensive black-box functions, but BO-based autotuners often underuse runtime metrics and parameter relationships, require many evaluations in high-dimensional spaces, and adapt slowly to workload shifts. We observe that transitions from suboptimal to better configurations follow recurring directions in parameter space. These directions remain partly stable across similar workloads, and LLMs can infer candidate directions from system knowledge. Building on this observation, we propose EvoTune, an agentic BO framework that searches over semantic Actions and refines them using trial evidence. EvoTune maintains an Action–Metric–Objective Graph to combine LLM-based diagnosis with model-based Action selection, while BO optimizes execution magnitudes and evidence and feasibility checks govern Action and graph updates. Across eight static workloads, EvoTune achieves the best observed endpoint, with average and maximum gains over a common initial reference of 33.47% and 55.19%, respectively. Under workload shifts, its post-switch throughput exceeds the strongest compared baseline by 11.42% on databases and 25.79% on Spark.