> For the complete documentation index, see [llms.txt](https://docs.lleverage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lleverage.ai/build-and-improve/automate/build-a-workflow/canvas-reference/action-connections.md). # Action connections Connections between action cards define the execution path through a Workflow. The current canvas shows connection points at the edges of cards and lines between connected steps. For a simple linear Workflow, following those connections across the canvas should make the main route easy to understand. ## Connect the path, not just the picture A line is meaningful because it determines which step can follow another. Moving cards for readability does not change execution; changing the connection can. When extending a Workflow, either add a new action and connect it into the path, or use a connection point where the editor offers a direct way to add the next action. ## Branching and flow control Not every Workflow is linear. Flow-control actions can create alternative routes. In those cases, make the branch condition explicit and keep each outgoing path visually distinct enough to review. Do not infer branch semantics from screen position alone. The action configuration and its outgoing connections determine the actual route. ## Data flow and execution flow are related Connections establish execution order, while variables and bindings determine what information a later action receives. A visually connected Workflow can still be wrong if a downstream action is bound to the wrong value. Review both: * **path** — did the right actions execute in the right route? * **data** — did each action receive the right inputs and produce the expected outputs? ## Before publishing Run the draft with representative inputs and inspect the trace. A neat connected canvas is not proof that the Workflow behaves correctly in external systems. For editor navigation see Canvas Controls. For the individual steps see Action Cards. --- # 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://docs.lleverage.ai/build-and-improve/automate/build-a-workflow/canvas-reference/action-connections.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.