> For the complete documentation index, see [llms.txt](https://academy.pentaho.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.pentaho.com/pentaho-data-integration/data-integration/enterprise-solution/jobs/backward-chaining.md). # Backward Chaining Rules when executing in multiple threads .. {% hint style="warning" %} **Workshop - Backward Chaining** There are rules on the sequence in which Job Entries are executed. In this workshop, the Job Entries were placed on the canvas in the following order: * START * Transformation A * Transformation B * Transformation C * Success START > Transformation A is executed first. Even though Transformation C and Success were placed on the Canvas after Transformation B .. they will be executed before Transformation B as there's a hop from Transformation A. Once the Transformation A pipeline has been completed with a success then Transformation B will be executed. {% endhint %}

#### RUN the Transformation 1. Click the Run button in the Canvas Toolbar. 2. Click on the Job Metrics tab.

{% hint style="info" %} From the Job metrics: START > Transformation A > Transformation C > Success is executed before Transformation B. Check the dependencies ... {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://academy.pentaho.com/pentaho-data-integration/data-integration/enterprise-solution/jobs/backward-chaining.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.