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

arXiv: https://arxiv.org/abs/2605.19988 · HTML: https://mlsanity.com/paper/2605.19988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 19 May 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 20 of 20)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.03157/) (2023)
- [D-Bot: Database Diagnosis System using Large Language Models](https://mlsanity.com/paper/2312.01454/) (2023)
- [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017)
- [Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2110.12654/) (2021)
- [DB-BERT: a Database Tuning Tool that "Reads the Manual"](https://mlsanity.com/paper/2112.10925/) (2021)
- [λ-Tune: Harnessing Large Language Models for Automated Database System Tuning](https://mlsanity.com/paper/2411.03500/) (2024)
- [LlamaTune: Sample-Efficient DBMS Configuration Tuning](https://mlsanity.com/paper/2203.05128/) (2022)
- [Automatic Database Configuration Debugging using Retrieval-Augmented Language Models](https://mlsanity.com/paper/2412.07548/) (2024)
- [Black or White? How to Develop an AutoTuner for Memory-based Analytics [Extended Version]](https://mlsanity.com/paper/2002.11780/) (2020)
- [E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model](https://mlsanity.com/paper/2404.11581/) (2024)
- [The Case for Automatic Database Administration using Deep Reinforcement Learning](https://mlsanity.com/paper/1801.05643/) (2018)
- [Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation](https://mlsanity.com/paper/2408.02213/) (2024)
- [TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning](https://mlsanity.com/paper/2508.12551/) (2025)
- [ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance](https://mlsanity.com/paper/1910.09644/) (2019)
- [L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3](https://mlsanity.com/paper/2511.01602/) (2025)

## Abstract (arXiv)

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.