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A Microsoft research paper outlining an 'AI Software Factory' that accelerates the full software development lifecycle for data systems, reporting up to 3x engineering efficiency and 22x token efficiency in large-scale internal deployments, along with self-improving feedback loops via fine-tuning.
This paper proposes Declarative Data Services (DDS), an architecture for structured agentic discovery of data-system compositions from declarative user intent. It decomposes the global search into bounded sub-searches and shows convergence on a trading-backend workload where unbounded discovery fails.
The Pragmatic Engineer Podcast will feature an interview with Martin Kleppmann on designing data-intensive applications.