Declarative Data Services: Structured Agentic Discovery for Composing Data Systems

• URL: http://arxiv.org/abs/2605.20690v2
• Date: Tue, 26 May 2026 04:52:11 GMT
• Title: Declarative Data Services: Structured Agentic Discovery for Composing Data Systems
• Abstract summary: Declarative Data Services (DDS) is an architecture for structured agentic discovery of data-system compositions from declarative user intent.<n>As a proof of life on a trading-backend workload, converges where unbounded discovery does not; runtime failures become skill patches.
• Score: 1.8167057409188236
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Unbounded agentic discovery, a coding agent iterating on failure-log feedback, fails to converge consistently on a working stack even when iteration and explicit composition knowledge are added. We propose Declarative Data Services (DDS), an architecture for structured agentic discovery of data-system compositions from declarative user intent. The framework owns four typed contracts at successive layers (intent, operator DAG, per-system skills, runtime attribution) that decompose the global search into bounded sub-searches; sub-agents search each typed space, while the framework provides the channels by which knowledge flows forward as inline skill citations and errors route backward as typed signals. As a proof of life on a trading-backend workload, DDS converges where unbounded discovery does not; runtime failures become skill patches that the next deployment cites inline. We position this as an early prototype reporting lessons from real-world data-system composition.

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