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.

翻译：生成式人工智能、智能副驾与自主代理系统在知识工作中的快速部署已造成一项运营缺口：现有框架均未解决如何组织日常团队工作的问题，这些团队中的人工智能代理需与人类共同执行实质性的委派任务。敏捷开发、DevOps、MLOps及人工智能治理框架虽各自涉及相关领域，但均未将混合团队建模为统一的交付单元。本文提出人机融合框架（HAIF）：这是一个基于协议、可扩展的运营体系，围绕四大核心原则构建，包含形式化任务委派决策模型、具备可量化过渡标准的分级自治机制，以及专为融入现有敏捷与看板工作流设计的反馈系统，且无需小型团队增设额外角色。本框架遵循设计科学研究方法论开发。HAIF明确回应了核心的采纳悖论：人工智能能力越强，框架所要求的监督机制就越难被证明合理——然而缺乏监督带来的后果也越严重。本文提供了领域特定的验证清单、非软件环境的适配指南，并对框架的结构性局限进行了探讨——包括日益普遍的人类与人工智能持续协同生产模式，这种模式对离散型任务委派模型提出了挑战。本框架与工具链无关，支持迭代式采纳。实证验证被列为未来研究方向。