AI Agents in Wrike
TL;DR:
AI agents in Wrike are smart assistants that help your team work more efficiently by monitoring projects, analyzing context, and taking action. You can use prebuilt agents or create custom ones. Only Space admins can set them up. Enable them in Wrike Labs under Work Intelligence Preview. Agents work best with clear, descriptive text and can only access data within their assigned scope.
Important
This is an experimental feature, and as a result, it may have bugs, is subject to change, and may be discontinued at any time. Please feel free to use the feedback links in Wrike Labs to share your thoughts on this beta feature.
AI agents in Wrike are intelligent assistants designed to help your team work more efficiently. They monitor your projects, analyze context, and perform actions such as detecting risks, categorizing requests, or validating details. You can choose from prebuilt agents or create custom ones to suit your needs.
Note
AI agents are currently available only in spaces and can be configured by Space admins.
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Visit Wrike Labs.
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Log in with your Wrike account.
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Navigate to Work Intelligence preview.
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Click Enable.
Note
AI agents require the AI Addendum to be signed, as it’s powered by Large Language Models, which require your acceptance of the Terms and Conditions.
If you don’t see the AI agents in space settings, make sure Generative AI is enabled in your settings to access all AI features.
AI agents bring intelligence and automation to your workflows:
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Intelligent analysis: Read task descriptions, comments, and context to make informed decisions.
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Proactive monitoring: Watch for risks, bottlenecks, and opportunities.
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Smart classification: Organize and route work based on content understanding.
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Quality assurance: Validate incoming requests for completeness.
Agents can post comments, update fields usually within seconds of detecting a change.
AI Agents operate in two phases:
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Watcher: Monitors spaces, projects, or folders for triggers (like new tasks or field changes).
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Do-er: Analyzes the situation and takes action - posting comments, updating fields, or notifying teammates.
Note
Response time is typically 2–5 seconds as the agent “thinks” through the best next step. Processing may take longer for larger items or when actions are applied in bulk, such as mass assignee changes.
You can start with three built-in agents or design your own custom agent:
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Risk Status Reporter
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Purpose: Identify potential project risks early.
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What it does: Scans projects, tasks for overdue or blocked items and posts a summary comment.
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Ideal for: Project managers who want a quick project health check.
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Setup tip: Schedule daily or weekly runs; the agent posts its risk report as a comment automatically.
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Triaging Agent
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Purpose: Classify and route incoming work based on content.
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What it does: Reads new task descriptions, identifies intent, and sets custom field values like priority or category.
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Ideal for: Teams with high volumes of incoming requests.
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Setup tip: Define which custom fields to populate and the classification options.
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Intake Agent
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Purpose: Validate that new requests contain all required details before work begins.
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What it does: Reviews task descriptions for missing info and posts validation comments.
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Ideal for: Teams that rely on structured request intake.
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Setup tip: Define what “complete” means for your team (e.g., deadline, design link, assignee).
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Custom Agent
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Purpose: Build your own intelligent automation tailored to your workflow.
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What it does: You define the role, logic, and triggers; choose actions like posting comments or updating fields.
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Ideal for: Advanced teams that want to automate unique workflows.
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Setup tip: Write a clear prompt describing what the agent should look for, how to decide, and what to do in each case. Test it in the Playground before deploying.
You’ll need Space admin permissions to set up AI agents. Agents operate at the space level, and each one must be appointed to a specific folder, project, or task.
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Navigate to the space where you want to create an agent, then click the Settings icon 1 next to the space overview in the sidebar or below the space title in the overview.
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Select AI agents tab 2 in the space settings overview and click Get started 3.
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Choose an agent type from the drop-down 4 (Triaging, Intake, or Risk) or click + Custom AI agent 5 to create your own custom agent.
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Configure details:
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Name your agent.
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Write a clear prompt that outlines the agent’s role, logic, expected actions, and fallback behaviors and add example inputs/outputs if needed.
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Set the scope of the agent:
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Item where the agent was added:
For aggregate actions like Risk Reporter. The agent is triggered by changes in this item and acts on it.
Example: A custom agent is added to a Product Launch project to notify followers about the changes to the project. If any of the project’s custom fields change, the agent posts a comment to a project about this change and @mentions all the followers.
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Any subitem of the item where the agent was added:
For task-level actions like Intake or Triaging. The agent is triggered by changes in subitems and acts on those subitems.
Example: The Intake agent is added to an Incoming Requests folder. When a new subitem is added to this folder, it triggers the agent to check if all required information (like assignee or due date) is provided. The agent then posts a comment to that subitem to inform you if anything is missing.
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Choose triggers:
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For scheduled agents (like Risk Status Reporter), set how often the agent runs (daily, weekly, or custom).
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For event-based agents, select triggers such as “New item created” or “Item moved to folder.”
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Define actions:
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Post comment: The agent will share its analysis as comments.
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Change custom field: The agent will update specific custom fields (choose which field to update).
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Change assignee: The agent will assign work items to users based on availability, workload, and criteria you define (see detailed configuration below).
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Change work item name: The agent will update the task or project title based on its analysis.
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Change status: The agent will update the task status based on conditions you define. Works with both standard and custom workflows.
When to Use Status Change:
Status change is ideal for automatic workflow progression:
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Intake validation: Change status to Ready when all required fields are completed.
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Overdue tracking: Mark tasks as Overdue when they pass their due date.
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Dependency detection: Set status to Blocked when dependencies are mentioned in comments.
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Project completion: Move tasks to Complete when all subtasks are finished.
Important notes about status changes:
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Agents respect your workflow transition rules and cannot skip required statuses.
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Status changes are logged in the task activity history with the agent's reasoning.
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If the status is already correct, the agent creates only a log entry (no duplicate comment).
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Works with both standard Wrike statuses and custom workflow statuses.
Example Status Change Prompts:
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Mark overdue tasks:
When a task passes its due date and status is not Completed, change status to Overdue.
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Validate intake completion:
When a task has all three required fields populated (Budget, Assignee, Deadline), change status from New to Ready for Work. Ignore tasks already marked Ready for Work or Completed.
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Detect blocked work:
When comments mention "blocked", "waiting on", or "dependency", and status is not already Blocked or Completed, change status to Blocked and post a comment summarizing what's blocking the task.
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Test your setup in the Testing Playground to see how the agent reasons and responds.
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Click Create to activate your agent.
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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.
When you set-up a Custom AI agent and configure the details as shown in Step 4 above, choose the action to update the Assignee field to assign tasks. The agent will analyze available user information and follow the rules you set in your prompt. The agent considers:
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User profile details: name, country, location, time zone, job role, department.
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User groups: group membership and admin status.
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Current Workload: number of tasks already assigned to each user.
To use Wrike’s AI agent for task assignments, you need to specify a clear pool of users for the agent to choose from. The agent won’t assign tasks to just anyone, you have to guide it by defining exactly who’s eligible. Assigning tasks to a random person without any constraints isn’t supported.
Valid ways to define the user pool:
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Specify exact users by name: “Assign to Lisa Simpson or Alex Jones”
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Reference a user group: “Assign to any member of the Marketing Team user group”
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Use built-in groups: “Assign to a random user from My Team”
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Combine criteria: “Assign to any designer in the EMEA region from My Team”
Invalid ways to define the user pool:
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“Assign to a random user” (no pool defined).
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“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 that contains all regular users in your account. It does not include Collaborators or External users. You can use My Team in your prompts to reference your entire team.
The AI agent can assign work items using these methods:
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By full name or user UID.
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By a unique partial name.
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To "me" (the person who created the agent).
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To members of My Team.
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To members or admins of a user group.
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To users without specific permissions.
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To the task creator.
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To multiple users at once.
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Across different groups.
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By location, time zone, job role, department, or country.
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By workload (such as the least busy person in a group).
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Using rotation strategies (for example, assigning to a different user each week).
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With fallback options if no one matches your main criteria.
Current Limitations:
Some assignment options aren’t available yet:
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Assign to space admins (admin info isn’t passed to the agent yet).
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Assign based on account roles or permissions.
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Assign to the same assignees as another task.
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Assign to parent task assignees.
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Assign using advanced workload metrics, such as capacity planning or scheduled hours.
Note
The agent can count tasks it sees, but doesn’t have full access to scheduling or capacity data.
Example Prompts
Here are some ways you can guide the AI agent to assign tasks:
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Basic assignment:
When a new task is created in this folder, assign it to a member of the Support Team user group who is based in North America.
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Workload-aware assignment:
Assign new tasks to the user from My Team who currently has the fewest tasks assigned. If multiple users are tied, choose randomly.
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Rotation strategy:
Rotate task assignment among Design Team members weekly. On Mondays, assign to User A; Tuesdays to User B; and so on.
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Multi-criteria assignment:
Assign tasks to users who meet these criteria:
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Member of the Engineering user group.
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Located in Europe (GMT to GMT+3).
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Fewer than 10 active tasks.
If no one matches, assign it to the Engineering user group admin.
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Best Practices
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Always define your user pool clearly.
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Include fallback logic for cases where no one meets your criteria.
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Test your prompts in the Playground before going live.
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Start with simple rules and add complexity as needed.
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Document your assignment logic for future reference.
Troubleshooting
If the agent fails to assign anyone:
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Check that your prompt defines a user pool.
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Make sure users have the right permissions for the folder or project.
If the wrong person is assigned:
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Review your prompt for any ambiguous wording.
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Ensure user profiles are up to date.
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Test your prompt in the Playground to confirm behavior.
If assignment seems random:
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Make sure your criteria are specific and clear.
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Keep in mind that AI agents may occasionally make different choices with similar options.
Note
Regularly review and update your assignment prompts as your team changes or your processes evolve.
Once your agents are deployed, you can monitor their performance in the Agent Activity Dashboard within the AI agents management interface.
Agent Overview Table
See all your appointed agents with details including:
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Agent name and monthly activity summary (e.g., “5 actions this month”).
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Recent action timestamps.
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Action types (such as changing custom fields or posting comments).
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Work items affected.
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Trigger events that activated the agent.
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Success or failure status.
Detailed Action Views
Click any action for more information, including:
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Complete agent details and timestamp.
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Specific work item and its location.
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Exact trigger that activated the agent.
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Precise action taken (for example, “Update Request Category field to creative asset”).
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AI reasoning with a full explanation of why the agent made its decision.
Monitoring Best Practices
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Regular review: Check agent activity logs weekly to make sure agents are working as expected.
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Reasoning analysis: Use the detailed reasoning display to understand how agents make decisions and spot opportunities to improve prompts.
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Success rate tracking: Monitor success and failure statuses to catch any ongoing issues.
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Performance adjustment: Refine agent prompts based on activity logs to boost accuracy and consistency.
Important
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Always test your agent setup in the built-in playground before deployment. You can select sample items and preview exactly how the agent will respond, including its reasoning process.
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Set fallback values for cases where the agent can’t make a determination.
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Agents respect existing permission settings and won’t access information users can’t see.
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Only one agent of each type can operate in the same location (folder, project, or task).
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Agents can monitor at the container level (folders or projects) or act on individual work items (tasks), depending on your scope selection.
Chatting with an agent in Wrike lets you ask follow-up questions about actions it takes on your projects and tasks. Use this feature to get insights into the agent’s decisions and reasoning — all in a private conversation.
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Navigate to the project or task's comment stream where the agent took action.
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In the comment box, @mention the agent you want to chat with.
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A private chat panel will open on the right side of your screen.
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Type your question and press Enter to chat with the agent.
Note
Your conversation with the agent is private. Other users cannot see what you discuss in the chat panel.
You can use the chat to understand why the agent made certain decisions. For example:
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Why it took an action:
“Why did you flag this task as at risk?”
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What information it used:
“What made you classify this as a bug?”
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How it reached its conclusion:
“Why did you think this was blocked?”
Tip
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Ask clear, direct questions to get the best answers from the agent.
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You can copy the answer to your clipboard and like or dislike the response provided by the chat.
Do:
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Be specific about what you want the agent to look for.
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Give clear examples of the classifications or actions you expect.
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Define fallback behaviours for any edge cases.
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Use natural language - write as if you’re explaining it to a helpful colleague.
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Test your prompt thoroughly in the Testing Playground before deploying.
Don't:
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Don’t create overly complex, multi-step logic.
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Don’t assume the agent understands your company’s terminology without explanation.
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Don’t forget to address situations with unclear or incomplete information.
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Don’t deploy your agent without testing it first.
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Start small: Test one agent type in a single folder before expanding to other areas.
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Test first: Always validate your agent’s behaviour in the Playground before deploying.
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Combine agents: Use the Intake Agent and Triaging Agent together for complete request handling.
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Review often: Regularly refine your prompts based on activity logs and team feedback.
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Communicate: Keep your team informed when agents are active and let them know what’s being automated.
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Non-deterministic behaviour: AI agents may give slightly different responses to the same situation - this is expected for AI.
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Context boundaries: Agents can only access and work with information within their assigned scope, such as a specific folder, project, or task.
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Agent Scope for Subitems: When you set an AI Agent’s scope to Any subitem of the item where the agent was added, the agent will only monitor up to two levels below the appointed item.
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Level 1 (Direct subitems): Monitored
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Level 2 (Sub-subitems): Monitored
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Level 3 and beyond: Not monitored
Example:
If you add an Intake agent to a folder called Incoming Requests:
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The agent will monitor tasks directly in the Incoming Requests folder (Level 1)
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The agent will monitor subtasks within those tasks (Level 2)
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The agent will not monitor subtasks nested three levels deep (Level 3)
Best practice:
Keep tasks that require agent context within the first two levels of your appointed item. For complex or deeply nested hierarchies, add the agent closer to the items you want it to monitor.
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Language processing: Agents perform best when you provide clear, descriptive text.
If your agent isn’t responding:
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Make sure the agent is active and properly configured.
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Check that the trigger conditions are set up correctly and are being met.
If your agent is making inaccurate classifications or actions:
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Review your prompt for clarity and specificity.
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Add examples or adjust the fallback behaviour.
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Ensure the task description provides enough context for the agent to make an informed decision.