> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/fabricate/data-agent-tools/workflows.md). # Creating and running workflows As you work with your database, you might want to run repeatable tasks such as: * Generating additional data * Posting data to another location * Generating unstructured files from the data * Starting tests * Validating data Instead of issuing a separate prompt every time, you can have the agent create a repeatable workflow. The workflow runs the exact same way every time.
Configure required connections

Make sure that your workflow has access to the connections that it needs to complete its task.		Configuring required connections for the workflow
Create, edit, or delete a workflow

Add a workflow, update the workflow configuration, or remove a workflow.		Creating, editing, and deleting a workflow
View workflow details

The workflow details include the overview, script, and API documentation.		Viewing workflow details
Select the freeform text model

Select the model that the workflow uses to generate freeform text.		Selecting the model to use for freeform text generation
Run a workflow

You can run a workflow from a prompt or from the workflow details.		Running a workflow
--- # 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.tonic.ai/fabricate/data-agent-tools/workflows.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.