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Assign Work Items with AI Agents

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Wrike Documentation Team

Wrike Documentation Team

TL;DR 

Wrike's Custom AI agents can automatically rename and assign work items by analyzing context and task counts. You must define a clear user pool (by name, group, attributes, or workload) and include fallback rules. Agents can't access capacity/workload data beyond task count - use calculated fields as a workaround. Test prompts in the Playground before going live.

Table 10. Availability

Free Team Business Pinnacle Apex
Availability: Business, Pinnacle, Apex. ; Unavailability: Free, Team;

Overview

AI agents can automatically change work item names and assign work items to users by analyzing context, availability, and workload. This action is available for Custom Agents and helps your team allocate work dynamically.

How Automatic Assignment Works

When you set up a Custom AI agent and configure the details, choose the action to update the Assignee field. The agent analyzes available user information and follows the rules you set in your prompt.

The AI agent can count the tasks it sees assigned to each user, but it does not have access to Wrike's workload view, capacity planning, or scheduling data. For more accurate workload balancing, use a calculated field that references capacity or workload system data - agents can read calculated field values.

Configuration Requirements

You must specify a clear pool of users for the AI agent to choose from. The agent won't assign tasks to just anyone.

Valid ways to define the user pool:

  • Specify exact users by name: "Assign to Lisa or Alex"
  • Combine criteria: "Assign to Morgan or Casey if the task mentions 'design' or 'UI'. Among them, prefer whoever has fewer assigned tasks."

Invalid ways:

  • "Assign to a random user" (no pool defined).
  • "Assign to the best person" (no pool defined).

Important

You must always define a user pool. The agent won't assign tasks if you don't provide clear guidance.

Note

My Team is a built-in group containing all regular users in your account. It does not include Collaborators or External users.

Supported Assignment Methods

  • By full name or user UID.
  • By a unique partial name.
  • By user profile attributes (department, job title, timezone, country).
  • By user group membership ("assign to a member of the Design group").
  • To the same assignees as another task (reads assignees from sibling items).
  • To parent task assignees.
  • To "me" (the person who created the agent).
  • To members or admins of a user group.
  • To users without specific permissions.
  • To the task creator.
  • To multiple users at once.
  • Across different groups.
  • By location, time zone, job role, department, or country.
  • By workload (such as the least busy person in a group).
  • Using rotation strategies (e.g., a different user each week).
  • With fallback options if no one matches your main criteria.

Current Limitations

  • Assign to space admins (admin info isn't passed to the AI agent yet).
  • Assign based on account roles or permissions.
  • Assign using workload or capacity data beyond task count. AI agents can see assignedTaskCount per user but cannot access effort, scheduled hours, or capacity planning.
    • Workaround: Use a calculated field that references capacity data - agents can read calculated field values.

Example Prompts

Basic assignment:

When a new task is created in this folder, assign it to Lisa or Alex. If the task mentions "design", prefer Lisa. Otherwise, prefer Alex.

Workload-aware assignment:

Assign new tasks to the user from this list who currently has the fewest tasks assigned: Morgan, Casey, Alex. If multiple users are tied, choose randomly.

Rotation strategy:

Rotate task assignment among Design Team members weekly. On Mondays, assign to User A; Tuesdays to User B; and so on.

Multi-criteria assignment:

Assign tasks to users who meet these criteria: members of the Engineering user group; located in Europe (GMT to GMT+3); fewer than 10 active tasks. If no one matches, assign to the Engineering user group admin.

Best Practices

  • Always define your user pool clearly.
  • Include fallback logic for cases where no one meets your criteria.
  • Test your prompts in the Playground before going live.
  • Start with simple rules and add complexity as needed.
  • Document your assignment logic for future reference.

Troubleshooting

If the AI agent fails to assign anyone:

  • Check that your prompt defines a user pool.
  • Make sure users have the right permissions for the folder or project.

If the wrong person is assigned:

  • Review your prompt for ambiguous wording.
  • Ensure user profiles are up to date.
  • Test in the Playground.

If assignment seems random:

  • Make sure your criteria are specific and clear.
  • Keep in mind AI agents may occasionally make different choices with similar options.

Note

Regularly review and update your assignment prompts as your team changes or processes evolve.

What’s Next?

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