HAIF：混合团队操作的人机集成框架
HAIF: A Human-AI Integration Framework for Hybrid Team Operations
AI总结：
HAIF框架旨在解决混合团队中AI与人类协作的组织问题，通过协议驱动、可扩展的操作系统，结合四个核心原则和反馈机制，实现无缝集成到现有工作流程中。
摘要：
生成式AI、合作者和代理系统在知识工作中的快速部署产生了操作缺口：现有的任何框架都无法解决如何在团队中组织日常工作的难题，其中AI代理执行实质性、被委托的任务与人类并行工作。敏捷、DevOps、MLOps和AI治理框架各自覆盖了相邻的关切，但没有一个模型将混合团队作为统一的交付单元。本文提出了人类-人工智能集成框架（HAIF）：一个基于协议、可扩展的操作系统，围绕四个核心原则、正式的委托决策模型、分层自主权以及具有可量化过渡标准的反馈机制，旨在融入现有的敏捷和看板工作流程，而无需为小型团队增加额外角色。该框架是按照设计科学研究方法开发的。HAIF明确解决了核心采用悖论：AI越有能力，就越难证明框架要求的监督越有必要——然而，不提供它所带来的后果却更大。本文包括了领域特定的验证检查表、非软件环境的适应指导，以及对框架结构限制的审查，包括日益常见的连续人机协同生产模式，这挑战了离散委托模型。该框架是工具无关的，并且设计用于迭代采用。实证验证被确定为未来的工作。
英文摘要：
The rapid deployment of generative AI, copilots, and agentic systems in knowledge work has created an operational gap: no existing framework addresses how to organize daily work in teams where AI agents perform substantive, delegated tasks alongside humans. Agile, DevOps, MLOps, and AI governance frameworks each cover adjacent concerns but none models the hybrid team as a coherent delivery unit. This paper proposes the Human-AI Integration Framework (HAIF): a protocol-based, scalable operational system built around four core principles, a formal delegation decision model, tiered autonomy with quantifiable transition criteria, and feedback mechanisms designed to integrate into existing Agile and Kanban workflows without requiring additional roles for small teams. The framework is developed following a Design Science Research methodology. HAIF explicitly addresses the central adoption paradox: the more capable AI becomes, the harder it is to justify the oversight the framework demands-and yet the greater the consequences of not providing it. The paper includes domain-specific validation checklists, adaptation guidance for non-software environments, and an examination of the framework's structural limitations-including the increasingly common pattern of continuous human-AI co-production that challenges the discrete delegation model. The framework is tool-agnostic and designed for iterative adoption. Empirical validation is identified as future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
