发表机构
EPAM Systems（EPAM Systems）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过三类企业级生成式AI应用比较框架适用性，提出分层架构中概率组件生成提案、确定性组件掌控关键决策，并强调组件级框架选择需依赖明确契约与独立验证。
AI 中文摘要
本研究比较了三类企业级生成式AI应用的框架适用性：基于文档的开发助手、电子邮件与查询自动化系统，以及Oracle到PostgreSQL迁移工具。分析评估了组件边界、编排、策略检索或推理、模型集成、确定性验证、持久化、可观测性和运营效率。在这三个应用中，证据支持分层架构，其中概率性组件生成提案，而确定性组件保留对路由、授权、验证、持久化、幂等性和最终结果的权威。结果表明，框架适用性取决于应用、部署条件和组件职责：搜索质量、工作流控制、安全行为和迁移验证不能简化为单一的跨应用评估。因此，研究证实了在组件级别进行框架选择的需求，并辅以明确的契约、应用特定证据和独立的验证边界。
英文摘要
This study compares framework suitability across three classes of enterprise generative AI applications: a documentation-based development assistant, an email and inquiry automation system, and an Oracle-to-PostgreSQL migration tool. The analysis evaluates component boundaries, orchestration, policy retrieval or reasoning, model integration, deterministic validation, persistence, observability, and operational efficiency. Across the three applications, the evidence supports layered architectures in which probabilistic components generate proposals, while deterministic components retain authority over routing, authorization, validation, persistence, idempotency, and final outcomes. The results indicate that framework suitability depends on the application, deployment conditions, and component responsibility: search quality, workflow control, safety behavior, and migration validation cannot be reduced to a single cross-application assessment. Therefore, the study substantiates the need for framework selection at the component level, supported by explicit contracts, application-specific evidence, and independent validation boundaries.
Comments33 pages, 2 figures, 34 tables, 58 references
