AI 中文总结
本文提出自适应工作流智能（AWI），一种基于感知-认知-行动-反思循环的认知架构，通过反思机制和混合推理增强企业自动化在动态环境下的适应性，实验表明其比静态方法恢复更快且保持政策合规。
AI 中文摘要
企业系统日益依赖自动化工作流，然而许多人工智能驱动的解决方案在非平稳条件、不断演变的政策以及延迟的操作反馈下仍然脆弱。虽然强化学习和大型语言模型（LLM）智能体提供了部分适应性，但它们本身并不提供持续的反思机制，也不提供与受政策约束的企业运营的直接集成。本文介绍了自适应工作流智能（AWI），这是一种面向情境驱动企业智能体的认知架构，围绕四层感知-认知-行动-反思（PCAR）循环组织。AWI将反思视为持续政策精炼的机制，并将混合推理与反思记忆和反馈驱动的适应性相结合，以支持在环境漂移和操作约束下的决策。我们在一个以延迟结果和受控制度转变为特征的模拟企业决策工作流中评估了AWI。在漂移与延迟压力测试中，受护栏约束的自适应方法比静态自动化恢复得更快，同时保持政策合规性。在此设置中，AWI的反思组件适度减少了行为振荡和反馈方差，展示了反思性政策适应引入的稳定性-敏捷性权衡。
英文摘要
Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed operational feedback. While reinforcement learning and large language model (LLM) agents offer partial adaptability, they do not by themselves provide persistent reflection mechanisms or straightforward integration with policy-constrained enterprise operations. This paper introduces Adaptive Workflow Intelligence (AWI), a cognitive architecture for context-driven enterprise agents organized around a four-layer Perception-Cognition-Action-Reflection (PCAR) loop. AWI treats reflection as a mechanism for continuous policy refinement and combines hybrid reasoning with reflective memory and feedback-driven adaptation to support decision making under environmental drift and operational constraints. We evaluate AWI in a simulated enterprise decision workflow characterized by delayed outcomes and a controlled regime shift. In a drift-and-delay stress test, guardrail-constrained adaptive approaches recover more rapidly than static automation while maintaining policy compliance. Within this setting, AWI's reflective components modestly reduce behavioral oscillation and feedback variance, illustrating the stability-agility trade-off introduced by reflective policy adaptation.
Comments12 pages, simulation-based evaluation of adaptive workflow agents
