A Case for Agentic Tuning: From Documentation to Action in PostgreSQL

• URL: http://arxiv.org/abs/2605.19988v1
• Date: Tue, 19 May 2026 15:26:28 GMT
• Title: A Case for Agentic Tuning: From Documentation to Action in PostgreSQL
• Abstract summary: PerfEvolve translates expert tuning methodologies into executable skills.<n>PerfEvolve outperforms state-of-the-art documentation-driven tuning baselines by up to 35.2%.
• Score: 41.66343157817528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Documentation has long guided computer system tuning by distilling expert knowledge into per-parameter recommendations. Yet such guides capture only what experts conclude, discarding how they reason. This fundamental gap manifests in three concrete deficiencies: documentation grows stale as software evolves, fails under heterogeneous workloads, and ignores inter-parameter dependencies. We propose shifting from static documentation to dynamic action for system tuning. We introduce PerfEvolve, which translates expert tuning methodologies into executable skills that equip LLM-based agents to perform version-consistency verification, workload-specific profiling, and multi-parameter joint optimization. Evaluated on PostgreSQL under TPC-C and TPC-H benchmarks, PerfEvolve outperforms state-of-the-art documentation-driven tuning baselines by up to 35.2%. The tool is available at https://github.com/ISCAS-OSLab/PerfEvolve.

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