通过构建块组合可验证的概念模型：面向智能体AI工作流的设计时验证
Composing Verifiable Conceptual Models via Building Blocks: Towards Design-Time Verification of Agentic AI Workflows
- Team EVERGREEN Inria Centre Inria d’Université Côte d’Azur（法国国家信息与自动化研究所蔚蓝海岸大学中心EVERGREEN团队）
- Department of Data Science William & Mary（威廉与玛丽学院数据科学系）
- Office of Enterprise Research and Innovation Old Dominion University（欧道明大学企业研究与创新办公室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种设计时验证方法，通过12条结构规则检查智能体工作流中可重用构建块的兼容性，实现设计缺陷检测。
AI中文摘要：
智能体AI系统通过协调决策、工具和外部动作的工作流架构来编排多个基于LLM的智能体。虽然当前平台强调运行时保障，但对系统设计期间验证工作流的支持很少。从建模与仿真的角度来看，这一差距类似于组合概念模型而不验证其构建块是否一致交互。我们提出了一种设计时验证方法，将智能体工作流建模为可重用构建块的组合，并通过十二条结构规则检查它们的兼容性。我们在一个软件原型中实现了这些规则，并使用两个公开数据集进行评估：48个已知设计缺陷的工作流和168个保留工作流逻辑但改变图结构的变体。结果表明，即使通过结构变换（如在智能体间拆分任务）掩盖有缺陷的设计，我们的验证器也能可靠地检测到违规。未来的工作可以将我们的验证与构建块的社区仓库相结合，以组合安全的智能体工作流。
英文摘要：
Agentic AI systems orchestrate multiple LLM-based agents through workflow architectures that coordinate decisions, tools, and external actions. While current platforms emphasize runtime safeguards, little support exists for verifying workflows during system design. From a Modeling \& Simulation perspective, this gap is analogous to composing conceptual models without verifying whether their building blocks interact coherently. We propose a design-time verification approach that models agentic workflows as compositions of reusable building blocks and checks their compatibility through twelve structural rules. We implemented these rules in a software prototype and evaluated them using two openly released datasets: 48 workflows with known design flaws and 168 variants that preserve workflow logic but alter graph structure. Results show that our verifier reliably detects violations even when flawed designs are obscured through structural transformations such as splitting tasks between agents. Future works could combine our verification with community repositories of building blocks to compose safe agentic workflows.
