通过结构化环境提升大模型生成应用的可靠性，实现高成功率落地。
app.build: A Production Framework for Scaling Agentic Prompt-to-App Generation with Environment Scaffolding
- 构建多层验证与栈专用编排机制，提升生成应用的可用性。
- 30个任务中73.3%的应用可运行，30%达到完美质量评分。
- 适合希望在生产环境中部署AI应用生成系统的开发者。
我们提出 app.build（https://github.com/neondatabase/appdotbuild-agent），一个开源框架，通过系统化验证和结构化环境，提升基于大模型的应用生成效果。该方法结合多层验证流水线、栈特定编排和模型无关架构，在三个参考技术栈中实现。在30个生成任务上的评估表明，全面验证使应用可行性达73.3%，其中30%达到完美质量评分；在提供结构化环境的前提下，开源权重模型可达到闭源模型80.8%的性能。该框架已获社区采纳，至今已有超过3,000个应用生成。本工作表明，扩展可靠AI代理的关键在于扩展环境而非仅依赖模型，为面向生产的代理系统提供了实证支持与完整参考实现。
原文摘要 · Abstract (English)
We present app.build (https://github.com/neondatabase/appdotbuild-agent), an open-source framework that improves LLM-based application generation through systematic validation and structured environments. Our approach combines multi-layered validation pipelines, stack-specific orchestration, and model-agnostic architecture, implemented across three reference stacks. Through evaluation on 30 generation tasks, we demonstrate that comprehensive validation achieves 73.3% viability rate with 30% reaching perfect quality scores, while open-weights models achieve 80.8% of closed-model performance when provided structured environments. The open-source framework has been adopted by the community, with over 3,000 applications generated to date. This work demonstrates that scaling reliable AI agents requires scaling environments, not just models -- providing empirical insights and complete reference implementations for production-oriented agent systems.
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