将人类监督独立成模块,让智能体工作流更安全可控。
A Decoupled Human-in-the-Loop System for Controlled Autonomy in Agentic Workflows
- 把人类干预设计为独立组件,通过接口与任务流程解耦。
- 提出四维框架,实现按需、上下文感知的人类介入。
- 适合需要可扩展治理的多智能体系统研发者使用。
AI代理在智能体工作流中执行任务并做出决策,对安全可控的自主性提出新要求。已有研究强调人类监督对透明度、问责性和可信度的重要性,但现有人工介入(HITL)机制通常嵌入应用逻辑,限制了跨多智能体环境的复用、一致性和可扩展性。本文提出一种解耦的HITL系统架构,将人类监督作为代理运行环境中的独立组件。该设计通过显式接口和结构化执行模型,将人类交互管理与应用流程分离。同时引入一个四维设计框架,形式化地定义了介入条件、角色分配、交互语义和通信通道,实现选择性且上下文感知的人类参与,保持系统一致性。该方法支持与新兴代理通信协议对齐,使HITL可作为协议层问题实现。通过外部化和结构化集成HITL,系统为智能体工作流中的可扩展治理和渐进式自主提供了基础。
原文摘要 · Abstract (English)
AI agents are increasingly deployed to execute tasks and make decisions within agentic workflows, introducing new requirements for safe and controlled autonomy. Prior work has established the importance of human oversight for ensuring transparency, accountability, and trustworthiness in such systems. However, existing implementations of Human-in-the-Loop (HITL) mechanisms are typically embedded within application logic, limiting reuse, consistency, and scalability across multi-agent environments. This paper presents a decoupled HITL system architecture that treats human oversight as an independent system component within the agent operating environment. The proposed design separates human interaction management from application workflows through explicit interfaces and a structured execution model. In addition, a design framework is introduced to formalize HITL integration along four dimensions: intervention conditions, role resolution, interaction semantics, and communication channel. This framework enables selective and context-aware human involvement while maintaining system-level consistency. The approach supports alignment with emerging agent communication protocols, allowing HITL to be implemented as a protocol-level concern. By externalizing HITL and structuring its integration, the system provides a foundation for scalable governance and progressive autonomy in agentic workflows.
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