Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework
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2025
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| author | Ganesaraja, Ramprasath N, Swathika AP, Saravanan Rathinasamy, Kamalkumar Amancharla, Chetana Das, Rahul Panse, Sahil Dilip Batwe, Aditya Vijayan, Dileep Ashok, Veena P, Thanushree A Rao, Kausthubh J Olivero, Alden Roshan Manthena, Rajeshwar Reddy A, Asmitha Yuga Sre Tripathi, Harsh Selvaraj, Suganya Chin, Vito Bhaskar, Kasthuri Rangan Bhaskar, Kasthuri Rangan R, Venkatraman Vijayakumar, Sajit |
| author_facet | Ganesaraja, Ramprasath N, Swathika AP, Saravanan Rathinasamy, Kamalkumar Amancharla, Chetana Das, Rahul Panse, Sahil Dilip Batwe, Aditya Vijayan, Dileep Ashok, Veena P, Thanushree A Rao, Kausthubh J Olivero, Alden Roshan Manthena, Rajeshwar Reddy A, Asmitha Yuga Sre Tripathi, Harsh Selvaraj, Suganya Chin, Vito Bhaskar, Kasthuri Rangan Bhaskar, Kasthuri Rangan R, Venkatraman Vijayakumar, Sajit |
| contents | We present AI4UI, a framework of autonomous front-end development agents purpose-built to meet the rigorous requirements of enterprise-grade application delivery. Unlike general-purpose code assistants designed for rapid prototyping, AI4UI focuses on production readiness delivering secure, scalable, compliant, and maintainable UI code integrated seamlessly into enterprise workflows. AI4UI operates with targeted human-in-the-loop involvement: at the design stage, developers embed a Gen-AI-friendly grammar into Figma prototypes to encode requirements for precise interpretation; and at the post processing stage, domain experts refine outputs for nuanced design adjustments, domain-specific optimizations, and compliance needs. Between these stages, AI4UI runs fully autonomously, converting designs into engineering-ready UI code. Technical contributions include a Figma grammar for autonomous interpretation, domain-aware knowledge graphs, a secure abstract/package code integration strategy, expertise driven architecture templates, and a change-oriented workflow coordinated by specialized agent roles. In large-scale benchmarks against industry baselines and leading competitor systems, AI4UI achieved 97.24% platform compatibility, 87.10% compilation success, 86.98% security compliance, 78.00% feature implementation success, 73.50% code-review quality, and 73.36% UI/UX consistency. In blind preference studies with 200 expert evaluators, AI4UI emerged as one of the leaders demonstrating strong competitive standing among leading solutions. Operating asynchronously, AI4UI generates thousands of validated UI screens in weeks rather than months, compressing delivery timeline |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework Ganesaraja, Ramprasath N, Swathika AP, Saravanan Rathinasamy, Kamalkumar Amancharla, Chetana Das, Rahul Panse, Sahil Dilip Batwe, Aditya Vijayan, Dileep Ashok, Veena P, Thanushree A Rao, Kausthubh J Olivero, Alden Roshan Manthena, Rajeshwar Reddy A, Asmitha Yuga Sre Tripathi, Harsh Selvaraj, Suganya Chin, Vito Bhaskar, Kasthuri Rangan Bhaskar, Kasthuri Rangan R, Venkatraman Vijayakumar, Sajit Software Engineering Artificial Intelligence We present AI4UI, a framework of autonomous front-end development agents purpose-built to meet the rigorous requirements of enterprise-grade application delivery. Unlike general-purpose code assistants designed for rapid prototyping, AI4UI focuses on production readiness delivering secure, scalable, compliant, and maintainable UI code integrated seamlessly into enterprise workflows. AI4UI operates with targeted human-in-the-loop involvement: at the design stage, developers embed a Gen-AI-friendly grammar into Figma prototypes to encode requirements for precise interpretation; and at the post processing stage, domain experts refine outputs for nuanced design adjustments, domain-specific optimizations, and compliance needs. Between these stages, AI4UI runs fully autonomously, converting designs into engineering-ready UI code. Technical contributions include a Figma grammar for autonomous interpretation, domain-aware knowledge graphs, a secure abstract/package code integration strategy, expertise driven architecture templates, and a change-oriented workflow coordinated by specialized agent roles. In large-scale benchmarks against industry baselines and leading competitor systems, AI4UI achieved 97.24% platform compatibility, 87.10% compilation success, 86.98% security compliance, 78.00% feature implementation success, 73.50% code-review quality, and 73.36% UI/UX consistency. In blind preference studies with 200 expert evaluators, AI4UI emerged as one of the leaders demonstrating strong competitive standing among leading solutions. Operating asynchronously, AI4UI generates thousands of validated UI screens in weeks rather than months, compressing delivery timeline |
| title | Beyond Prototyping: Autonomous, Enterprise-Grade Frontend Development from Pixel to Production via a Specialized Multi-Agent Framework |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2512.06046 |