Spec Driven Development SDD has consolidated the idea that the specification rather than the code should be the primary artefact governing AI assisted work. Tools such as GitHub Spec Kit, and proposals such as Constitutional SDD, have formalised this principle in the software domain, while the executable data-contracts literature has extended it to schema and quality enforcement at run time.
arXiv:2608. 19475v1 Announce Type: cross Abstract: This work introduces a formal semantic-block model for specifications and an execution-judged benchmark for evaluating specification quality independently of model capability.
By Oleg Grynets, Dmytro Kostetskyi, Vasyl Lyashkevych
arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.
By Kailong Ren, Fubo Sun, Jiachen Liu, Liu Yang, Zimo Yin, Jiaying Li, Congli Yin, Ming He, Yu Huo, Jiawei Liu, Zeping Chen, Yubin Huangfu, Ronghua Li, Yixuan Wu, Xing Su, Yanzhi Xu, Likang Wu, Hongke Zhao, Lei Zhang, Xiaohui Geng, Jianping Fan
The paper proposes a contract‑centered architecture for agentic runtimes, defining four shared responsibility objects—Skill, Harness, Scaffold, and an external data substrate—to manage capabilities, runtime, control boundaries, and data governance in enterprise AI deployments. It introduces a falsifiable hypothesis (P1) about cost‑aware capability‑capacity separability and outlines six measurable design conditions, proposing a cluster‑period randomized crossover experiment to test the hypothesis. The work presents a contract‑bounded runtime architecture, a source‑preserving data substrate, and a measurement protocol, though no implementation or empirical results are reported.
By Yaxiao Liu, Pengbo Liu, Yiwen Liu, Yihua Guan, Zhenghe Hou, Jiaxing Song
The paper introduces GROUND, a framework that limits large language model (LLM) analytics to a governed semantic layer for enterprise data warehouses. GROUND supplies approved metrics, dimensions, join paths, filters, and security rules, then validates generated SQL against these constraints before execution, retrying or abstaining on violations. In benchmarks, GROUND eliminates hallucinations across all evaluated categories and prevents row‑level security breaches, outperforming schema‑only, schema‑RAG, and semantic‑only approaches.
By Aravind Sasidharan Pillai
arXiv:2604. 13097v3 Announce Type: replace-cross Abstract: Embodied agents increasingly rely on modular capabilities that are installed, upgraded, composed, and governed at runtime, yet the interfaces between these modules are specified only at the level of message types, so integration failures surface only during execution.
By Xue Qin, Simin Luan, Cong Yang, Zhijun Li
The paper introduces Spec-Driven Agentic Development (SDAD), a framework that leverages large language models to ingest extensive functional requirement documents and repository context in a single workflow, turning specification quality into the engine for autonomous software delivery. SDAD blends disciplined upfront formalisation with rapid implementation, encompassing intent capture, machine‑readable specifications, agentic synthesis, and multi‑agent verification with human sign‑off. It positions AI‑code as a fourth production paradigm, compares it to traditional Waterfall and Agile approaches, and extends the model to team role evolution, quantitative governance metrics, and a staged migration blueprint for practical adoption.
By Vu Hung Nguyen, Thanh Nguyen
arXiv:2606. 03907v1 Announce Type: cross Abstract: Agentic AI coding tools write code with increasing autonomy and in doing so decide when to import a library and when to implement functionality from scratch.
By Jai Lal Lulla, Matthias Galster, Jie M. Zhang, Sebastian Baltes, Christoph Treude
arXiv:2606. 20615v3 Announce Type: replace Abstract: AI agents now act as first-class members of the software development lifecycle, but the instruments teams use to direct them enforce nothing: process encoded in prompts is flexible but unenforceable, while workflow formalisms are enforceable but do not model autonomous agents.
By Ylli Prifti, Pasquale De Meo, Alessandro Provetti
arXiv:2607. 08028v1 Announce Type: new Abstract: Enterprise large language model (LLM) applications often begin as prototypes whose behavior is carried by prompts and retrieval context.
By Joongho Ahn, Moonsoo Kim
arXiv:2608.21208v1 Announce Type: cross
Abstract: This paper investigates cross-agent specification portability using Oracle-to-PostgreSQL migration as a controlled software transformation task. The...
By Oleg Grynets, Oleksii Ilchuk, Dariia Zatulna, Vasyl Lyashkevych
arXiv:2608. 12440v1 Announce Type: cross Abstract: This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour.
By Joel Abenhaim