How it works

Follow these 8 simple steps to start sending closed Zendesk ticket summaries to Asana automatically

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Ticket Status Updated

App connector: Zendesk • Time to complete: 0 minutes (Auto-configured)
Why this matters: This trigger monitors your Zendesk tickets and kicks off the workflow whenever a ticket's status changes, catching closed tickets in real-time so you can capture insights immediately.

This trigger watches for any status change on Zendesk tickets and passes the ticket ID to the next step when a change occurs. It runs continuously in real-time, so there's no delay between a ticket closing and the workflow starting. The trigger captures the basic ticket ID which gets used to retrieve full details in the following step.

Retrieve Ticket

App connector: Zendesk • Time to complete: 0 minutes (Auto-configured)
Why this matters: The trigger only provides the ticket ID, so this step fetches all the detailed information about the ticket including its current status, recipient email, and timestamps needed for the summary.

This step sends a request to Zendesk using the ticket ID from the trigger and retrieves the complete ticket record including status, recipient email, creation date, and update timestamp. All of this information gets passed forward to later steps—the status determines whether to continue processing, while the recipient and timestamps appear in the final Asana task for context.

Filter: Check if ticket status is closed

App connector: Filter • Time to complete: 0 minutes (Auto-configured)
Why this matters: Not every status change means a ticket closed—this filter makes sure you only summarize and document tickets that have actually been resolved and closed, preventing premature summaries of open conversations.

This filter examines the ticket status from the previous step and only allows the workflow to continue if the status exactly equals "closed". If the ticket is in any other state like open, pending, or on-hold, the workflow stops here and doesn't create an Asana task. The comparison happens automatically using the status field retrieved in the previous step.

Get List of Comments

App connector: Zendesk • Time to complete: 0 minutes (Auto-configured)
Why this matters: The AI needs the full conversation history to generate an accurate summary—this step retrieves all comments and messages exchanged between your support team and the customer throughout the ticket's lifecycle.

This step queries Zendesk for all comments associated with the ticket ID and returns them as an array of comment objects. Each comment includes the message body, author, and timestamp. This complete conversation thread gets passed to the Loop step which prepares it for the AI to analyze and summarize.

Loop: Get comments

App connector: Loop • Time to complete: 0 minutes (Auto-configured)
Why this matters: The AI needs the comments formatted as a single text stream rather than separate objects—this loop extracts just the comment bodies and combines them into comma-separated text that's easy for AI to process.

This Map step iterates through all the comments retrieved in the previous step and extracts only the body text from each comment. It automatically creates a comma-separated version of all comment bodies which gets used in both AI prompt steps. This formatting makes it easier for the AI to read the conversation flow and generate accurate summaries.

Generate Title

App connector: AI • Time to complete: 1 minute
Why this matters: A descriptive title helps you quickly identify what each ticket was about when reviewing your Asana board—the AI creates this by reading the conversation and extracting the core issue in plain language.

This AI step analyzes the comma-separated conversation thread and generates a short, clear title that captures the customer's main concern or situation. You don't need to configure the prompt unless you want to adjust how titles get formatted—the default prompt instructs the AI to avoid technical jargon and focus on the essence of the customer's experience. The generated title becomes the Asana task name in the final step.

Based on the customer support thread provided {{loop_2.comma_separated}}, generate a short, descriptive title that captures the essence of the customer's concern or situation. Avoid technical jargon.

Example Input:
"Customer: I can't access my dashboard, Support: Can you try clearing your cache?, Customer: That worked, thanks!"

Example Output:
Login Issue Resolved After Basic Troubleshooting

Generate Summary

App connector: AI • Time to complete: 1 minute
Why this matters: While the title provides quick context, the summary gives you the full picture including customer sentiment, resolution status, and the solution provided—this helps you identify patterns and training opportunities across multiple tickets.

This AI step creates a detailed summary by analyzing the same conversation thread with specific instructions to capture the problem nature, customer sentiment, resolution status, and solution provided. The prompt focuses the AI on extracting actionable insights rather than technical details. You can customize the prompt if you want summaries to emphasize different aspects like feature requests or bug reports.

Summary Writing Instructions for Support Threads:
When reviewing a customer support thread (messages separated by commas), write a short, clear summary that includes only:

-General nature of the customer's problem (no technical detail)
-Their experience or sentiment (e.g., frustration, confusion, satisfaction)
-Resolution status (resolved, pending, unclear)
-The solution provided

Example
Input:
Customer: I can't access my dashboard, Support: Can you try clearing your cache?, Customer: That worked, thanks!

Output:
The customer was unable to access their dashboard but regained access after clearing the cache. They appeared satisfied with the outcome. {{loop_2.comma_separated}}
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Create Task

App connector: Asana • Time to complete: 2 minutes
Why this matters: This step delivers the AI-generated insights to your team in Asana where you can review, discuss, and act on patterns emerging from customer support conversations.

This step creates a new task in your specified Asana workspace and project using the AI-generated title as the task name. The task notes include the AI summary, original ticket number, customer email, and the timestamp when the ticket was last updated. You need to configure two fields: the workspace ID and project ID where tasks should be created—you can find these in your Asana URL when viewing the project. The task appears immediately in Asana once created.

Make it your own

Customize this workflow even further:

Add priority levels based on customer sentiment
Use a Transform or Custom Code step after the AI Summary to analyze the sentiment keywords and automatically set Asana task priority—mark tasks as high priority if the summary mentions frustration or dissatisfaction.
Filter by ticket tags or categories
Add a Filter step after retrieving the ticket to only process tickets with specific tags like "bug" or "feature-request", helping you build separate insight collections for different types of customer feedback.
Store summaries in MESA Tables for trend analysis
Add a Table step after the AI Summary to log each ticket summary with a timestamp and category, creating a searchable database you can query to identify recurring issues over time.
Send weekly digest of closed tickets to Slack
Set up a separate workflow using Schedule trigger that queries your Asana project weekly and compiles all the ticket summaries into a single Slack message, giving your team a regular overview of support trends.

Frequently asked questions

What happens if a ticket gets reopened and closed again later?
The workflow will run again and create a new Asana task with an updated summary that includes the additional conversation. You'll end up with two tasks for the same ticket number, which actually helps you see when issues resurface and require multiple rounds of support.
Can the AI summary include specific data like order numbers or account IDs?
Yes, the AI reads the entire conversation thread so if order numbers or account details appear in the comments, they'll be included in the summary. However, the default prompt instructs the AI to focus on general insights rather than technical details—you can modify the summary prompt if you need more specific data captured.
How accurate are the AI-generated titles and summaries?
The AI performs best with complete conversations that have clear resolution, typically achieving high accuracy for standard support interactions. Very short tickets or those with heavy technical jargon may produce less descriptive summaries—you can always edit the Asana task after creation to add context or clarification if needed.
What is a template?
Templates are pre-made workflows by our team of experts. Instead of building a workflow from scratch, these have all the steps needed to complete the task.
Can I personalize a template?
Yes! Every step can be customized to meet your exact requirements. Additionally, you can even add more steps and make it more sophisticated.
Are templates free?
Yes! Our entire library containing hundreds of templates are free to use and customize to your exact needs.

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