Specification-delta-driven data governance: an empirical study of the «spec-delta» as the unit of change in lakehouse data platforms

AI coding agents implement software from naturallanguage instructions, but the intent behind that work is lost when stated only in chat sessions. Spec-driven development (SDD) addresses this by having developers and agents write specifications that guide implementation, yet evidence on its use comes largely from tool vendors. We study SDD in practice through a thematic analysis of Reddit discussions, interviews with 21 practitioners, including eight tool makers, and 266 active and popular repositories in SpecMine, a corpus of 470,795 spec files from 73,030 public GitHub repositories that we release. Practitioners agreed that specs should capture intent, but many also included implementation details, used SDD selectively and mainly for new features, and reported more reliable agent behavior alongside greater planning and review costs. Our findings show that implementation detail may help agents while making specs prone to decay, that tool-specific record structures shape what people and agents see, and that participants described spec-code checks mainly when changes are completed rather than over time. We also find that individual benefits may come from clarifying intent before delegation, while team benefits may depend on SDD becoming a shared process. We derive implications for tool builders, practitioners, and researchers.

Spec-driven development (SDD) with AI coding agents provides a structured workflow, but agents often remain "context blind" in large, evolving repositories, leading to hallucinated APIs and architectural violations. We present Spec Kit Agents, a multi-agent SDD pipeline (with PM and developer roles) that adds phase-level, context-grounding hooks. Read-only probing hooks ground each stage (Specify, Plan, Tasks, Implement) in repository evidence, while validation hooks check intermediate artifacts against the environment. We evaluate 128 runs covering 32 features across five repositories. Context-grounding hooks improve judged quality by +0.15 on a 1-5 composite LLM-as-judge score (+3.0 percent of the full score; Wilcoxon signed-rank, p < 0.05) while maintaining 99.7-100 percent repository-level test compatibility. We further evaluate the framework on SWE-bench Lite, where augmentation hooks improve baseline by 1.7 percent, achieving 58.2 percent Pass@1.

Enterprise data platforms face an enduring tension between domain self-service and holistic governance. The data mesh paradigm proposed decentralized domain ownership as a remedy, but pure implementations frequently underdeliver: teams inherit new responsibilities without the platform maturity, tooling, or coordination mechanisms needed to exercise them effectively. This paper argues that the flexibility-versus-control trade-off can be relaxed through an AI-augmented hub-and-spoke model layered on a modern lakehouse architecture. A central hub (Center of Excellence) provides shared platform services, policy automation, and AI-enabled governance, automatically standardizing data products, generating quality rules, drafting data contracts, and reviewing changes for regressions. Domain spokes own business semantics, product backlogs, and local iteration cadence, progressively assuming greater responsibility as they mature. The same LLMs that automate governance tasks also lower the barrier for domain practitioners to develop genuine cross-functional expertise spanning business and data engineering, enabling spoke teams to take on greater end-to-end ownership without proportionally increasing their dependence on the hub. Natural-language conversational interfaces further democratize access for business users, exposing historically underutilized enterprise data. On the organizational side, we propose a staged framework that shifts ownership from hub to spokes, avoiding both centralized bottlenecks and uncoordinated decentralization. We evaluate the architecture through three outcome metrics: data product adoption, time-to-find, and time-to-insight, that tie platform success to measurable business value rather than internal activity.