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：在组织设计中，如何分配人类与人工智能系统之间的工作是一项核心挑战。多数方法将其视为二元选择，但运营现实更为丰富：人类和AI通常根据情境、疲劳程度和利害关系共享任务或承担互补角色。如何管理这种分配——平衡效率、监督与人类能力——仍是一个开放性问题。本文提出了人类-AI自适应共生框架（HAAS），一个在软件工程和制造业中实现自适应任务分配的已部署框架。HAAS结合了两个耦合组件：一个基于规则、在任何学习发生之前强制执行治理约束的专家系统；以及一个基于结果反馈选择可行协作模式的情境强盗学习器。任务-智能体适应性通过五个可审计的认知维度以及一个涵盖从纯人类到全自主的五模式自治光谱来表示，并嵌入跨越这两个领域的可复现基准测试中。研究得出三项实证发现：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任务转化为监督协作，并产生特定领域的成本与收益。第二，在制造业中，更强大的治理能同时提升运营绩效并减少疲劳——这种工作量缓冲效应与通常将治理视为纯粹负担的框架相矛盾。第三，没有单一的治理设置能在所有情境中占据主导地位；随着学习者在受治理的动作空间内积累经验，适度治理的竞争力逐渐增强。这些发现共同将HAAS定位为一个部署前的工作台，用于在组织承诺之前比较和检查人类-AI分配策略。