Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
TL;DR
Six residual gaps in making the surrounding assurance reasoning sufficiently machine-operable are identified, a high-level architecture with six corresponding capabilities built on a shared semantic assurance layer is proposed, and four research questions are formulated to turn that architecture into dependable, human-on-the-loop, AI-native R&D.
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.