用声明式框架让AI自动组合数据系统，解决复杂环境下的稳定部署难题。
Declarative Data Services: Structured Agentic Discovery for Composing Data Systems
- 基于四层契约结构分解搜索空间，实现有界、可追踪的智能组合。
- 在交易后端任务中成功收敛，而传统方法反复失败无法稳定运行。
- 适合需要自动化数据系统集成的研发团队，尤其关注可靠性与可复现性。
代理发现已证明大型语言模型能在基准条件下找到新颖算法、设计和代码。将该范式应用于多系统数据后端时面临更复杂挑战：搜索空间异构、验证标准为部署栈是否实际运行，且组合知识在预训练中分布不均。无界代理发现（即编码代理通过失败日志反馈迭代）即使加入迭代机制与显式组合知识，仍无法一致收敛至有效部署栈。为此，本文提出声明式数据服务（DDS），一种从用户声明意图出发、结构化探索数据系统组合的架构。该框架在逐层定义四种类型合约（意图、操作图、各系统技能、运行时归属），将全局搜索分解为多个有界子搜索；子代理分别在各自类型空间中搜索，框架则提供知识前向流动（内联技能引用）与错误反向传播（类型化信号）的通道。在交易后端工作负载上的验证表明，DDS 能成功收敛，而无界发现无法做到；运行时故障转化为可被后续部署内联引用的技能修补。本工作定位为真实世界数据系统组合中的早期原型，报告了关键经验教训。
原文摘要 · Abstract (English)
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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