AI工程流程嵌入自动审计，实现合规交付
Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery
- 将敏捷开发与验证模型结合，每轮任务自动生成审计文档
- 100%需求覆盖验证通过，每轮仅需6次人工提示
- 适合需要合规审计的高可靠系统开发团队
当前AI辅助工程流程缺乏在高速交付中保持任务级验证与监管可追溯性的机制。Agile V通过在每个任务周期内嵌入独立验证与审计文档生成，填补这一空白。该框架融合敏捷迭代与V模型验证，形成持续循环的无限闭环，部署专用AI代理分别负责需求、设计、构建、测试和合规，并设置强制人工审批节点。我们验证了三个假设：（H1）审计就绪文档可作为开发副产品自动生成；（H2）通过独立测试生成可实现100%需求级验证；（H3）每轮可仅用个位数人工交互完成已验证增量交付。一项关于硬件在环系统（约500行代码，8个需求，54项测试）的可行性案例研究支持所有假设：审计文档自动产出（H1），需求级通过率达100%（H2），每轮仅需6条提示（H3），相比COCOMO II基准估算成本降低10-50倍（悲观到乐观假设范围）。我们邀请独立复现以验证泛化能力。
原文摘要 · Abstract (English)
Current AI-assisted engineering workflows lack a built-in mechanism to maintain task-level verification and regulatory traceability at machine-speed delivery. Agile V addresses this gap by embedding independent verification and audit artifact generation into each task cycle. The framework merges Agile iteration with V-Model verification into a continuous Infinity Loop, deploying specialized AI agents for requirements, design, build, test, and compliance, governed by mandatory human approval gates. We evaluate three hypotheses: (H1) audit-ready artifacts emerge as a by-product of development, (H2) 100% requirement-level verification is achievable with independent test generation, and (H3) verified increments can be delivered with single-digit human interactions per cycle. A feasibility case study on a Hardware-in-the-Loop system (about 500 LOC, 8 requirements, 54 tests) supports all three hypotheses: audit-ready documentation was generated automatically (H1), 100% requirement-level pass rate was achieved (H2), and only 6 prompts per cycle were required (H3), yielding an estimated 10-50x cost reduction versus a COCOMO II baseline (sensitivity range from pessimistic to optimistic assumptions). We invite independent replication to validate generalizability.
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