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

2026-08-07

AI Summary

This work addresses the critical gap in existing approaches to AI-native large-scale agile development, which lack machine-actionable closed-loop assurance mechanisms to dynamically maintain system safety, trustworthiness, and compliance. The paper introduces, for the first time, the concept of an “assurance loop,” identifies six key machine-actionability gaps, and proposes a high-level architecture centered on a shared semantic assurance layer. This architecture integrates formal methods, testing, simulation, assurance cases, digital twins, and runtime techniques to enable semantic interoperability and collaborative reasoning across heterogeneous evidence sources. It empowers AI agents—under human supervision—to autonomously assess evidence credibility, sustain verification validity, and enforce behavioral boundaries, thereby establishing a theoretical and engineering foundation for human-in-the-loop, trustworthy AI-native development ecosystems.

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.

Problem

• assurance closure
• AI-native software development
• large-scale agile
• machine-operable assurance
• human-on-the-loop

Innovation

• assurance closure
• AI-native software development
• semantic assurance layer
• machine-operable assurance
• human-on-the-loop