Common issues

Most problems in Spark come from one of a few predictable causes: you are in the wrong workspace, a workflow has not been activated, or something is waiting on a human. This page walks through the situations users hit most, with the symptom, the reason behind it, and the fix.

I can't see my data, or a colleague's data

Each workspace is an isolated tenant. Data, tasks, workflows, and agents live inside one workspace and are never visible from another. If a table or record you expect is missing, you are almost always looking at the wrong workspace.

Check the workspace switcher at the top of the sidebar and confirm the name matches where the data was created. If a colleague can see something you cannot, you may not be a member of that workspace yet. See Workspaces and organizations for how isolation works, and ask an admin to add you if needed (Members).

My workflow isn't running

A newly created workflow is saved as a Draft. Drafts do not execute, and their triggers stay dormant until you turn them on.

Open the workflow and use the Activate action to set its status to Active. After activating, confirm the trigger matches what you expect (for example a schedule or a file event). Only Active workflows respond to triggers or run on a schedule.

My workflow is stuck partway through

If a run started but never finished, it may be paused on a Human Review step, waiting for someone to approve or reject before it can continue.

Open the Interventions tab under Workflow to see runs awaiting a decision, then approve or reject the pending step. See Human in the loop for how these pauses work, and Runs and debugging if the run failed for another reason.

The agent doesn't know about my data

An agent answers from the context of the workspace it runs in. If it cannot find something, that data usually does not exist in this workspace, or you are chatting from a different one.

Confirm the data object and records exist in the current workspace, then start a fresh message. See Chat with an agent.

A CSV import mapped columns wrong

During import, Spark infers an attribute type for each column and matches it to a header. A misread date or number usually means the source was inconsistent.

Review the column mapping and attribute types before confirming the import. If it already landed, correct the attribute types on the data object, then re-import a clean file with one row per record and consistent formats.

I can't create a data object

Creating and editing the structure of data lives in the admin view, not the everyday browsing view. Open the Manage action in the Data area to reach it.