让旧版AI工作流可进化，支持渐进式改造与自动诊断。
Toward Self-Evolution-Ready Workflow Harnesses: A Reversible Migration Path and Convertibility Taxonomy for Expert LLM Pipelines
- 提出可逆迁移路径，将静态工作流拆解为可组合、可审计的模块
- 设计三级可转换性分类（A/B/C），动态判断工作流适配度并路由
- 适合需要升级现有LLM流水线的开发者与系统架构师
尽管经过专家验证的「LLM + 脚本」工作流已展现出显著价值，但它们仍处于静态状态：编码了宝贵的领域知识，却无法根据反馈进行执行调整。现有代理研究主要聚焦于从零开始构建的新代理和合成基准，未解决活跃遗留工作流的迁移问题。为弥合这一空白，我们提出一种可逆的类藤蔓（Strangler-Fig）迁移路径，将遗留工作流重构为可组合、带类型、可审计的阶段。该框架的核心是一个三层可转换性分类（A/B/C），作为系统调度器中的路由阶段，用于诊断工作流的准备就绪程度并决定其处理路径。
原文摘要 · Abstract (English)
While expert-validated "LLM + script" workflows deliver significant value, they remain static: they encode hard-won domain knowledge yet fail to adapt execution based on feedback. Existing agent research predominantly targets greenfield agents and synthetic benchmarks, leaving the migration of active legacy workflows unresolved. To bridge this gap, we present a reversible, Strangler-Fig migration path that refactors legacy workflows into composable, typed, and auditable stages. Central to this framework is a three-tier convertibility taxonomy (A/B/C), implemented as a routing stage within the system harness, which diagnoses a workflow's readiness and routes it accordingly.
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