Agentic AI coding systems can inspect repositories, plan implementation steps, edit files, call tools, run tests, and submit pull requests. These capabilities make software and hardware development faster in some settings, but current evidence does not support the simple claim that autonomous code generation automatically improves engineering outcomes. Controlled studies report productivity gains in some enterprise tasks, slowdowns in mature open-source work, moderate but heterogeneous meta-analytic effects, and persistent failures in repository setup, dependency handling, permission gating, and hardware verification. This paper argues that the central problem is no longer prompt engineering; it is engineering process control. It synthesizes evidence from agentic software engineering, GitHub-scale adoption studies, repository-level agent configuration, productivity trials, issue-resolution benchmarks, and hardware/RTL verification research. It proposes Agentic Agile-V, a process framework that uses Agile-V as the lifecycle backbone and a task-level SCOPE-V loop - Specify, Constrain, Orchestrate, Prove, Evolve, and Verify - to convert conversational intent into structured engineering artifacts and acceptance evidence. The paper contributes: (i) a taxonomy of minimum input artifacts for agentic software, firmware, and hardware work; (ii) a conversation-to-contract gate that separates exploratory dialogue from implementation; (iii) risk-adaptive feature, bug-fix, testing, and hardware workflows; and (iv) an evidence-bundle acceptance model for agent-generated artifacts. The paper concludes that agentic AI does not eliminate engineering discipline; it increases the value of requirements, constraints, traceability, independent verification, and human approval.

翻译：智能体AI编码系统能够检查仓库、规划实现步骤、编辑文件、调用工具、运行测试并提交拉取请求。这些能力在某些场景下加速了软硬件开发，但现有证据并不支持“自主代码生成自动改善工程成果”这一简单论断。受控研究报告显示，部分企业任务中生产率提升，成熟开源项目中出现减速，元分析效应中等但异质性高，且在仓库设置、依赖管理、权限门控及硬件验证方面持续存在失败。本文认为，核心问题已不再是提示工程，而是工程流程控制。本文综合了来自智能体软件工程、GitHub规模采用研究、仓库级智能体配置、生产率试验、问题解决基准测试及硬件/RTL验证研究的证据。本文提出Agentic Agile-V流程框架，以Agile-V作为生命周期主干，并采用任务级SCOPE-V循环——即指定（Specify）、约束（Constrain）、编排（Orchestrate）、证明（Prove）、演化（Evolve）与验证（Verify）——将对话意图转化为结构化工程工件与验收证据。本文贡献如下：(i) 针对智能体软件、固件及硬件工作的最小输入工件分类法；(ii) 将探索性对话与实现分离的“对话-合约”门控机制；(iii) 风险自适应的特性、缺陷修复、测试及硬件工作流；(iv) 面向智能体生成工件的证据包验收模型。本文结论：智能体AI并未消除工程纪律，反而提升了需求、约束、可追溯性、独立验证及人工审批的价值。