Towards Assurance Closure in AI-Native Large-Scale Agile Software Development

arXiv:2608.07317v1 Announce Type: cross Abstract: The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents execute more of the engineering process. Realizing that end-state requires more than better code generation: it requires assurance closure, meaning that the system can establish what must be true, determine and obtain appropriate evidence, judge the credibility of that evidence, preserve its validity through change, and use the resulting uncertainty to bound agent authority. Existing work already provides many of the necessary mechanisms across formal methods, testing, simulation, assurance cases, digital twins, and runtime assurance. We identify six residual gaps in making the surrounding assurance reasoning sufficiently machine-operable, propose a high-level architecture with six corresponding capabilities built on a shared semantic assurance layer, and formulate four research questions to turn that architecture into dependable, human-on-the-loop, AI-native R&D.

Related

• The AI-Native Large-Scale Agile Software Development Manifesto
• FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
• Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings
• Quality and Security Signals in AI-Generated Python Refactoring Pull Requests

Source: arXiv cs.AI | 2026-08-10