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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2602.20684 |
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| _version_ | 1866908850127372288 |
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| author | Koch, Christopher Wellbrock, Joshua Andreas |
| author_facet | Koch, Christopher Wellbrock, Joshua Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery Koch, Christopher Wellbrock, Joshua Andreas Software Engineering Artificial Intelligence Multiagent Systems 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. |
| title | Agile V: A Compliance-Ready Framework for AI-Augmented Engineering -- From Concept to Audit-Ready Delivery |
| topic | Software Engineering Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2602.20684 |