Skip to content

DBCooker : A Database Kernel-Specialized Coding Agent Harness

Aug 2026 · Proceedings of the VLDB Endowment · Vol 19, pp. 4842-4845 · 1 citation · 9 references

TL;DR

Conference attendees will experience how DBCooker seamlessly transforms high-level SQL function requests into validated, kernel-ready code for mainstream databases (e.g., SQLite, PostgreSQL, DuckDB).

Abstract

Database kernels continuously incorporate new built-in functions to support diverse applications. Synthesizing these native functions is highly complex, as it requires identifying multiple internal units, placing them in the correct source files, and reusing specific internal references. Although recent advancement in LLM-based coding agent frameworks (harnesses) exhibit superior capability, they are typically general-purpose and struggle with the massive and highly coupled database kernel repositories. To address this, we demonstrate DBCooker , a database kernel-specialized coding agent harness. DBCooker offers three core functionalities: (1) Kernel Dependency Inspector , which enables users to inspect internal units and cross-file references via hybrid declaration collection, distinctive unit identification, and cross-unit reference analysis; (2) Interactive Synthesis Planner , which constructs high-level implementation plans through progressive code synthesis and three-stage code validation, allowing developers to verify and refine plans before execution; and (3) Progressive Debugging Workspace , which integrates with database-specific pipelines to provide efficient feedback and adaptive repair, powered by techniques such as operation-as-tool abstraction and memory-augmented orchestration. Conference attendees will experience how DBCooker seamlessly transforms high-level SQL function requests into validated, kernel-ready code for mainstream databases (e.g., SQLite, PostgreSQL, DuckDB).

View source

Similar papers

#artificial intelligence Preprint Aug 2026

Super Library Agent: Joint Generation and Maintenance of Multiple Applications Beyond the Single Codebase

This work introduces the Super Library Agent problem, where an agent sequentially generates a portfolio of N related applications while maintaining a shared Super Library of reusable cross-application components, and addresses candidate-guided extraction over code chunk summaries, pre-extraction codebase consolidation,...

Daegyu Sung, Yukyeong Lee, Geon Park et al. · 0 citations
#artificial intelligence Preprint Sep 2026

MaxKernel: Agentic Kernel Generation for TPUs

This work presents MaxKernel, a multi-agent system that implements three distinct paradigms for TPU kernel development: a Human-in-the-Loop (HITL) agent for collaborative, step-by-step design; an Autonomous (Auto) agent that executes a fully automated, metric/trace-driven optimization loop; and a Graph-Based Autonomous...

Shang-Kun Wang, Nina Cai, Charles Hoong et al. · 0 citations
#artificial intelligence Preprint Sep 2026

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

CUA-Universe is introduced, a scalable environment-to-data pipeline that turns real desktop software into hybrid GUI+CLI environments and training on this data shifts behavior from inefficient GUI interaction and brittle CLI scripting toward effective GUI+CLI orchestration.

Hao-Ting Shi, Wen-Hao Wang, Wei-Cheng Fang et al. · 5 citations · ⚡1
#artificial intelligence Preprint Sep 2026

Git4Data: Database-Native Version Control for AI Agents

Git4Data is presented, a database-native version-control layer for agentic workflows that sheds light on how relational databases can better support AI agents through efficient versioning.

Hongshen Gou, Zu-Yu Zhang, Yu-Ze Sun et al. · 0 citations
Preprint Aug 2026

JetStream: Generating Query Accelerators for Existing Database Systems

JetStream is presented, a system for generating query-specific accelerators that instead extend an existing DBMS, and results show that aggressive generated specialization can be integrated seamlessly with existing DBMSes and support dynamic workloads.

Akhilesh Balasingam, Amadou Latyr Ngom, Geoffrey X. Yu et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.