Tool-using AI agents are increasingly used in coding, browser automation, research assistance, and support workflows. In practice, however, many teams still evaluate these systems through isolated prompts, one-off demos, or broad benchmark references that do not translate well into deployment judgment. This paper presents a lightweight workflow for evaluating agent behavior that begins with scenario design, continues through explicit expected behavior and failure-mode definition, and ends with an operational scorecard that helps teams judge rollout readiness. The workflow is instantiated through compact public artifacts, including small datasets, interactive demo apps, and public analytics surfaces. The aim is not to compete with large benchmarks on scale. It is to improve repeatability, interpretability, and operational usefulness for builders who need evaluation methods that are small enough to maintain and concrete enough to use. The manuscript is accompanied by public evaluation datasets, interactive demo apps, and portfolio artifacts that illustrate the workflow described in the paper.