🤖 AI Summary
This work addresses the frequent failures in current autonomous AI coding systems—stemming from uncontrolled engineering processes in software and hardware development, particularly in configuration, dependency management, permission handling, and hardware verification. To tackle these challenges, the authors propose the Agentic Agile-V framework, which anchors the development lifecycle in the Agile V-model and introduces a task-level SCOPE-V loop (Specify, Constrain, Orchestrate, Prove, Evolve, Verify) to translate conversational intent into structured engineering artifacts and verifiable evidence. Key contributions include an agent-oriented minimal input artifact taxonomy, a gated mechanism for converting dialogue into formal contracts, a risk-adaptive workflow, and an evidence-bundle-based artifact acceptance model. Empirical results demonstrate that this approach significantly enhances the reliability and controllability of AI-assisted development in complex projects, yielding more stable delivery outcomes and higher verification pass rates.
📝 Abstract
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.