Deciding how to distribute work between humans and AI systems is a central challenge in organisational design. Most approaches treat this as a binary choice, yet the operational reality is richer: humans and AI routinely share tasks or take complementary roles depending on context, fatigue, and the stakes involved. Governing that distribution -- balancing efficiency, oversight, and human capability -- remains an open problem. This paper presents Human-AI Adaptive Symbiosis (HAAS), an implemented framework for adaptive task allocation in software engineering and manufacturing. HAAS combines two coupled components: a rule-based expert system that enforces governance constraints before any learning occurs, and a contextual-bandit learner that selects among feasible collaboration modes from outcome feedback. Task-agent fit is represented through five auditable cognitive dimensions and a five-mode autonomy spectrum -- from human-only to fully autonomous -- embedded in a reproducible benchmark spanning both domains. Three empirical findings emerge. First, governance is not a binary switch but a tunable design variable: tighter constraints predictably convert autonomous AI assignments into supervised collaborations, with domain-specific costs and benefits. Second, in manufacturing, stronger governance can improve operational performance and reduce fatigue simultaneously -- a workload-buffering effect that contradicts the usual framing of governance as pure overhead. Third, no single governance setting dominates across all contexts; moderate governance becomes increasingly competitive as the learner accumulates experience within the governed action space. Together, these findings position HAAS as a pre-deployment workbench for comparing and inspecting human--AI allocation policies before organisational commitment.

翻译：如何分配人类与人工智能系统之间的工作，是组织设计中的核心挑战。现有方法大多将其视为二元选择，然而实际运作远比此丰富：人类与AI根据情境、疲劳程度及任务重要性，时常共享任务或扮演互补角色。如何在效率、监督与人类能力之间平衡，以管理这种分配，仍是一个悬而未决的问题。本文提出人类-AI自适应共生（HAAS）框架，这是一个在软件工程和制造业中实现自适应任务分配的实证系统。HAAS融合了两个耦合组件：一个基于规则的专家系统，在学习行为发生前强制执行治理约束；以及一个上下文多臂赌博机学习器，根据结果反馈在可行的协作模式中进行选择。任务-智能体适配度通过五个可审计的认知维度以及一个涵盖从纯人工到全自动五级自治模式的光谱来表示，该光谱嵌入在跨这两个领域的可复现基准中。研究发现三个实证结果：第一，治理并非二元开关，而是可调节的设计变量；更严格的约束可预测地将自主AI任务分配转化为受监督的协作，并伴随领域特定的成本与收益。第二，在制造业中，更强的治理能同时提升运营绩效并降低疲劳程度——这种工作量缓冲效应与通常将治理视为纯粹额外负担的认知相悖。第三，没有任何一种治理设置能在所有情境中占据主导地位；随着学习器在受规制行动空间中积累经验，适度治理的竞争力会逐渐增强。综合来看，这些发现将HAAS定位为一种部署前的工作台，用于在组织做出承诺前，比较和审视人类与AI的分配策略。