Agentic AI prototypes are being deployed across domains with increasing speed, yet no methodology for their structured design, governance, and prospective evaluation has been established. Existing AI documentation practices and guidelines - Model Cards, Datasheets, or NIST AI RMF - are either retrospective or lack machine-readability and interoperability. We present the Agentic Automation Canvas (AAC), a structured framework for the prospective design of agentic systems and a tool to facilitate communication between their users and developers. The AAC captures six dimensions of an automation project: definition and scope; user expectations with quantified benefit metrics; developer feasibility assessments; governance staging; data access and sensitivity; and outcomes. The framework is implemented as a semantic web-compatible metadata schema with controlled vocabulary and mappings to established ontologies such as Schema.org and W3C DCAT. It is made accessible through a privacy-preserving, fully client-side web application with real-time validation. Completed canvases export as FAIR-compliant RO-Crates, yielding versioned, shareable, and machine-interoperable project contracts between users and developers. We describe the schema design, benefit quantification model, and prospective application to diverse use cases from research, clinical, and institutional settings. The AAC and its web application are available as open-source code and interactive web form at https://aac.slolab.ai

翻译：智能体AI原型正以日益增长的速度在各领域部署，然而其结构化设计、治理和前瞻性评估的方法论尚未建立。现有的AI文档实践与指南——如模型卡片、数据表或NIST AI风险管理框架——要么属于回顾性工具，要么缺乏机器可读性与互操作性。本文提出智能自动化画布，这是一种用于智能体系统前瞻性设计的结构化框架，也是促进用户与开发者间沟通的工具。该画布涵盖自动化项目的六个维度：定义与范围；附带量化效益指标的用户期望；开发者可行性评估；治理阶段划分；数据访问与敏感性；以及成果产出。该框架以实现为语义网兼容的元数据模式，包含受控词汇表并与Schema.org、W3C DCAT等现有本体建立映射。通过具备实时验证功能的隐私保护型全客户端Web应用程序提供访问。完成的画布可导出为符合FAIR原则的RO-Crate数据包，形成用户与开发者之间具备版本控制、可共享且机器可互操作的项目契约。本文详细阐述了模式设计、效益量化模型，以及在科研、临床和机构场景中多样化用例的前瞻性应用。AAC及其Web应用程序以开源代码和交互式网络表单形式发布于https://aac.slolab.ai。