Agentic AI systems are increasingly being deployed as productive resources in organizational workflows, yet existing evaluation methods primarily measure isolated technical performance rather than economic contribution. This paper introduces \emph{Agentomics}, a workflow-based framework for valuing, attributing, and pricing human and artificial agents. The framework models a workflow as a configuration of heterogeneous agents whose collective performance determines gross value, deployment cost, reliability, and expected failure loss. Workflow value is treated as a team-level quantity that may include complementarities, substitution effects, bottlenecks, and nonlinear production; additive stage-level value is only a special case. Building on this workflow model, the paper formulates AI deployment as a coalition-formation problem and defines coalition value as the incremental net surplus generated relative to a benchmark human workflow. The Shapley value is then used to attribute economic surplus among participating AI agents, yielding a principled connection among valuation, accountability, and market pricing. The resulting Shapley pricing equilibrium provides a normative benchmark for assessing whether agent prices reflect expected marginal contribution. A security-operations case study illustrates how the framework accounts for productivity gains, deployment costs, reliability losses, and coalition-level complementarities in hybrid human--AI workflows.

翻译：代理式人工智能系统正越来越多地被部署为组织工作流中的生产性资源，然而现有的评估方法主要衡量其孤立的技术性能，而非经济贡献。本文提出"Agentomics"框架，这是一种基于工作流的评估方法，用于对人类和人工智能代理进行价值评估、归因与定价。该框架将工作流建模为异质代理的配置，其集体绩效决定了总价值、部署成本、可靠性和预期故障损失。工作流价值被视为团队层面的量，可能包含互补性、替代效应、瓶颈和非线性生产；加性阶段价值仅是特例。基于此工作流模型，本文将人工智能部署形式化为联盟形成问题，并将联盟价值定义为相对于基准人类工作流所产生的增量净盈余。随后利用沙普利值在参与的人工智能代理间分配经济盈余，从而在价值评估、问责制和市场定价之间建立原则性联系。由此产生的沙普利定价均衡为评估代理价格是否反映其预期边际贡献提供了规范基准。通过安全运营案例研究，展示了该框架如何衡量人机混合工作流中的生产力提升、部署成本、可靠性损失及联盟层面互补性。