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.

翻译：当前AI辅助的工程工作流缺乏内置机制，以在机器速度交付中维持任务级验证与监管可追溯性。敏捷V通过将独立验证与审计工件生成嵌入每个任务周期，解决了这一缺口。该框架将敏捷迭代与V模型验证融合为一个连续的无限循环，部署专用AI代理负责需求、设计、构建、测试与合规，并由强制性人工审批门控管理。我们评估了三个假设：(H1) 可审计工件作为开发的副产品自动生成，(H2) 通过独立测试生成可实现100%需求级验证，(H3) 经验证的增量交付每周期仅需个位数人工交互。在一个硬件在环系统（约500行代码、8项需求、54项测试）的可行性案例研究中，所有三个假设均得到支持：可审计文档被自动生成（H1），实现了100%需求级通过率（H2），每周期仅需6次提示（H3），相较于COCOMO II基线预计可降低10-50倍成本（敏感度范围基于悲观至乐观假设）。我们邀请独立复现以验证其普适性。