Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
Ricardo Britto
Why It Matters
What makes this one worth your time
As AI increasingly automates software engineering, ensuring the reliability and accountability of these systems is crucial for developers and researchers alike.
The paper proposes a framework for enhancing assurance in AI-driven software development.
Summary
The paper discusses the concept of assurance closure in AI-native large-scale agile software development, identifying gaps in existing mechanisms and proposing a high-level architecture with capabilities for machine-operable assurance reasoning.
Key contributions
- Identification of six gaps in current assurance reasoning mechanisms.
- Proposal of a high-level architecture for machine-operable assurance.
- Formulation of four research questions aimed at enhancing AI-native R&D.
Notable insights
- The concept of assurance closure is critical for bounding agent authority in AI systems.
- Identifying six residual gaps highlights the complexity of integrating assurance mechanisms into agile workflows.
Possible limitations
- Not stated in the abstract.
Abstract
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.