🤖 AI Summary
This work addresses the challenge of reconciling task-level verification and regulatory traceability within high-velocity AI-assisted engineering workflows. The authors propose an “infinite loop” framework that integrates agile iteration with V-model validation, embedding independent verification and compliance auditing into every development cycle through a multi-agent AI architecture. The system automatically generates audit-ready documentation and incorporates critical human-in-the-loop approval gates. By natively embedding compliance capabilities into the development process, the approach achieves 100% requirement-level verification and enables trustworthy delivery with minimal human intervention. In a hardware-in-the-loop case study, the system attained full requirement pass rates with an average of only six human prompts per cycle, demonstrating a projected cost reduction of 10–50× compared to conventional methods.
📝 Abstract
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.