Casebook is a new workflow designed for the AI Agent era, enabling test engineers to leverage tools like Lingma, Trae, Codex, Claude Code, and Cursor to understand project requirements, generate test cases, and refactor them. The key innovation is that Casebook turns these AI-generated artifacts into locally browsable, version-controlled engineering assets. This means teams can iterate on test cases collaboratively, review changes, and maintain a clear audit trail. For engineering leaders, this signals a shift from manual test design to AI-assisted, code-like management of test suites. The approach is particularly relevant for teams already using AI coding assistants and looking to extend that productivity gain to quality assurance. While still early, Casebook represents a practical blueprint for integrating AI into the testing lifecycle without sacrificing rigor or traceability.
Casebook introduces a workflow where test engineers use AI agents to understand requirements and generate test cases, then manage them as local engineering artifacts. This approach promises to reduce manual test design effort while keeping cases version-controlled and browsable. It matters because it shows a concrete path for QA teams to adopt AI without losing control over test quality.