Create an Expected Values Check

Step-by-step tutorial for creating an Expected Values check that restricts a field to a list of allowed values. For what each property means and how the rule evaluates the data, see the How It Works page.

Permission Required

You need the Author team permission on the datastore to create an Active check, or Drafter to create it as a Draft. See the Permissions page for the full matrix.

Show me how

The app can walk you through this. Click Show me how in the check creation form's header, or press H while it is open, and the Add a Check walkthrough highlights each step while you fill in the real form.

Field reference

The Authored Check Details form is organized in the sections below. Fill them in as you follow the Steps.

Target

Field	Type	Description
Associate with a Check Template	Toggle	Links the check to a Check Template. Enabling it replaces the Rule Type dropdown with a Template dropdown where you pick an existing template, which then controls the check's properties. When off, you configure everything on this form.
Rule Type	Option	The validation logic to apply. Select Expected Values for this check. Shown only while Associate with a Check Template is off.
Table / File	Option	The table or file the check runs against. Locked after the check is saved.
Field	Option	The field to evaluate. Every field type except `Struct` is accepted, including arrays, where each element is tested against the list.
Filter Clause	Text	A SQL `WHERE` expression that limits the rows the check evaluates. Rows outside the filter are ignored entirely.
Custom Anomaly Description	Toggle	Uses the value of another field on the violating row as the anomaly message instead of the default one. Enabling it shows an Anomaly Message Field dropdown where you pick that field. When the selected field is empty for a violating row, the default message is used.

Properties

Field	Required	Type	Description
List		List	The allowed values. Every non-NULL value in the field must appear in this list. Matching is exact: case-sensitive and whitespace-sensitive. Keep the entries' type aligned with the field's type.

Pass Criteria

Field	Required	Type	Description
Coverage		Slider	The percentage of evaluated rows that must pass. Defaults to 100%; lower it only when a known fraction of rows is allowed to fail.

Ownership

Field	Required	Type	Description
Owner		Option	The user responsible for the check. Already filled in with the check creator.
Anomaly Assignee		Option	The user automatically assigned to anomalies the check produces.

Metadata

Field	Type	Description
Description	Text	A plain-language description of what the check enforces. Supports Markdown formatting: click the field to open the Markdown editor. Click the Apply suggested description button for a suggestion based on the rule type.
Tags	Option	Tags applied to the check for filtering and organization.
Additional Metadata	Key-value	Custom key-value pairs, typically links to catalog entries, tickets, or governance records.

Let AgentQ fill in the form

When AgentQ is configured for your deployment, the Check Assistant panel in the check form can author the check for you: describe the rule in plain language (for example, "order status can only be O, F, or P") and it proposes a configuration you can apply to the form. Review the filled-in fields, then Validate and Save as usual.

Steps

Each field is described in the Field reference above.

Step 1: Select the source datastore from the left menu, then click the Checks tab.

Step 2: Click Add in the top-right corner and select Check from the dropdown. The Authored Check Details form opens.

Step 3: Select Expected Values in the Rule Type dropdown.

Step 4: Fill in the remaining Target fields: the table or file, the Field to evaluate, and, optionally, a Filter Clause and Custom Anomaly Description.

Step 5: Enter the allowed values in the List property under Properties.

Step 6: Fill in the Pass Criteria, Ownership, and Metadata sections. Only the Description is required.

Step 7: Click Validate to test the rule against the selected data without saving it. A success message confirms the rule can run on that data. If validation fails, see Troubleshooting.

Step 8: Click Save. A success message confirms that the check was created.