Tutorial: building multi-step AI workflows with openJiuwen Agent Core

On 2026-10-28, DEV Community's AI channel published a tutorial on building a support-ticket workflow with the open-source Python SDK openJiuwen Agent Core. The tutorial argues that a multi-step LLM pipeline usually fails at a handoff between steps. For example, a triage step may return the string high while the routing step expects the integer 4. No error is raised in that situation, so the bad value keeps travelling downstream until a customer receives an incorrect reply or a dashboard starts showing misleading figures. By that point, it is no longer clear which step mangled the data, which makes a quick fix difficult. To keep the demonstration grounded, the author ran every scenario three times using the real SDK together with real models rather than mocks. The workflow graph is described as having five parts, but the available excerpt does not enumerate those five components. The author also reports that Agent Core checks two boundaries at runtime while missing a third, though the excerpt does not say which boundaries are covered and which one is not, nor what each check actually does. This stands as the only available coverage of the tutorial so far, and no follow-up reports have appeared since publication.