Create and update entities from Spark

Jake Sawyer

Updated June 16, 2026 15:06

Spark can create and update Productboard entities directly from a conversation. Type a plain-language request, review the proposed changes in a preview card, and confirm to commit them. You can create or update a single entity, or handle up to 25 in one turn.

Supported entity types

Spark can create and update the following entity types:

Feature	Subfeature	Product
Component	Initiative	Objective
Key result	Release	Release group

Supported fields

During both entity creation and editing, Spark can manipulate default and custom fields, including:

Name (required for all entity types)
Status
Owner
Description
Start date and end date
Custom fields (including text, number, date, single-select, multi-select, and member fields)

During entity creation, if you don't specify a field, Spark leaves it blank (except for Name, you need one of those). You can set blank fields later from the entity's sidebar or by asking Spark to update it.

When asking Spark to work with fields, you should refer to them using their display names.

Example: "Set the owner of the Mobile redesign initiative to Alex."

How the confirmation preview works

Before committing to any changes, Spark shows a preview card listing every proposed operation. Each row shows one of two states:

Ready: the operation is valid and will proceed on confirmation.
Blocked: the operation can't proceed. Spark explains the reason, such as a missing required field, an unrecognized field name, or insufficient access.

To accept the changes, reply to confirm in the conversation. Spark commits all ready operations. To reject, tell Spark to cancel or adjust your request.

Note: Spark always shows the preview before committing. It cannot skip the confirmation step.

Creating or updating multiple entities at once

You can create or update up to 25 entities in a single turn. List them in your request, or ask Spark to find and update a group based on criteria.

Each entity appears as a separate row in the preview card. Blocked rows don't commit, but ready rows in the same batch do.

Example: "Create three key results for the Q3 growth objective: increase trial conversion to 30%, reduce churn below 5%, and grow NPS to 45."

Running chained workflows

Spark can combine a read step with a write step in a single request. This lets you find entities that match a condition and update them in one turn.

Spark first queries your workspace for matching entities, then presents a preview of the proposed updates. You confirm before anything is committed.

Example: "Find all features without an owner and assign them to me."

Permissions and access

You must have editor access to an entity to create or update it. Spark checks your permissions before presenting the preview.

If you don't have editor access, the affected row appears as Blocked in the preview with the message: "You need editor access to this entity. Check with your workspace admin."

Blocked rows don't commit. Other rows in the same batch that you do have access to will still proceed when you confirm.

Example prompts

Here are some prompts to get you started.

Creating entities:

"Create a feature called Dark mode under the Settings component."
"Create an initiative called Mobile redesign with status In progress and owner Sarah."
"Create three key results for the Q3 growth objective."
"Create a release called 2.4 with a start date of July 1."

Updating entities:

"Update the status of the Onboarding flow feature to In review."
"Set the owner of the Mobile redesign initiative to Alex."
"Add a description to the Dark mode feature: Allow users to switch between light and dark themes."
"Set the end date of the Q3 growth objective to September 30."

Multi-entity and chained operations:

"Find all features without an owner and assign them to me."
"Update the status of all features in the Onboarding component to In review."
"Create five subfeatures under the Authentication feature: password reset, SSO, MFA, session management, and audit log."