构建可机器操作的AI原生软件开发保障体系，让AI在可控范围内自主执行。
Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
- 基于统一语义层设计六项核心能力，支撑AI全流程可信运行。
- 提出四大研究问题，推动保障机制从人工向机器闭环演进。
- 适合关注AI辅助开发可信性与人机协同的科研与工程团队。
AI原生宣言构想了一种大规模敏捷软件开发模式，人类负责意图、风险与异常管理，而智能体承担更多工程任务。实现这一目标不仅需要更优的代码生成，更需达成‘保障闭环’——即系统能确定必须为真的事实，获取并评估证据，维护证据有效性，并利用不确定性来界定代理权限。现有工作已在形式化方法、测试、仿真、保障案例、数字孪生和运行时保障中提供了诸多必要机制。本文识别出六项关键缺口，提出一个以共享语义保障层为基础的高层架构，包含六项对应能力，并提出四个研究问题，旨在将该架构转化为可靠、人机协同、可信赖的AI原生研发体系。
原文摘要 · Abstract (English)
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.
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