The Agentic Automation Canvas: a structured framework for agentic AI project design

TL;DR

The paper introduces the Agentic Automation Canvas (AAC), a structured, interoperable framework for designing, governing, and evaluating agentic AI systems proactively, enhancing communication and transparency between users and developers.

Contribution

It presents the AAC framework and a web tool that captures key project dimensions, enabling structured, FAIR-compliant, and machine-readable documentation for agentic AI projects.

Findings

The AAC schema supports diverse use cases across domains.

It facilitates transparent communication between stakeholders.

The web application ensures privacy-preserving, real-time validation.

Abstract

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$\mathrm{.}$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$\mathrm{.}$