Declarative Data Services: Structured Agentic Discovery for Composing Data Systems
The article discusses a new framework called Declarative Data Services (DDS) aimed at improving the composition of data systems through structured agentic discovery. It highlights the challenges faced by existing methods in converging on effective data stacks and proposes a solution that breaks down the search process into manageable parts. The authors present DDS as a prototype that has shown promise in real-world applications, particularly in trading-backend workloads.
- ▪Declarative Data Services (DDS) is designed for structured agentic discovery of data-system compositions.
- ▪The framework addresses the challenges of heterogeneous search spaces and uneven composition knowledge.
- ▪DDS has demonstrated success in converging on working stacks where previous methods failed.
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Computer Science > Artificial Intelligence arXiv:2605.20690 (cs) [Submitted on 20 May 2026] Title:Declarative Data Services: Structured Agentic Discovery for Composing Data Systems Authors:Shanshan Ye, Duo Lu View a PDF of the paper titled Declarative Data Services: Structured Agentic Discovery for Composing Data Systems, by Shanshan Ye and 1 other authors View PDF HTML (experimental) Abstract:Agentic discovery has shown that LLM-driven search can find novel algorithms, designs, and code under benchmark conditions. Translating the paradigm to multi-system data backends surfaces a harder problem: the search space is heterogeneous, the verifier is whether a deployed stack actually runs, and composition knowledge is unevenly captured in pretraining.
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