Is it possible to configure an Architect flow that captures DTMF inputs and pushes them directly into the Zendesk ticket body via a Data Action, mirroring how we used to append notes to tickets in Zendesk?
Background
Our team is migrating from Zendesk Support to Genesys Cloud. In Zendesk, agents could easily append customer notes to tickets based on IVR selections. We are trying to replicate this in Genesys Cloud using the Zendesk Integration. The goal is to have the customer’s IVR menu choice (e.g., ‘Press 1 for Billing’) automatically recorded as a comment in the Zendesk ticket when the interaction is created or updated.
Issue
When the Architect flow reaches the ‘Update Ticket’ Data Action, it returns a 400 Bad Request. The error message in the flow logs is vague: ‘Invalid payload structure for Zendesk API v2’.
The Zendesk API documentation states that comments require a specific JSON structure with a ‘public’ boolean flag. In Genesys Cloud Architect, the Data Action mapper seems to flatten the JSON. I am trying to map the DTMF result to the ‘body’ field of a new comment object, but the mapper does not seem to support nested JSON construction for the ‘comment’ object within the ticket update payload.
Troubleshooting
- Verified the Zendesk API credentials in the Integration settings. Direct API calls via Postman work correctly with the same payload structure.
- Checked the Architect flow logs. The DTMF input is captured correctly as a string (e.g., “1”).
- Attempted to use the ‘Construct JSON’ block in Architect before the Data Action. The resulting JSON looks correct in the debug view, but the Data Action still fails with 400.
- Compared this to how we handled ticket updates in Zendesk macros. The simplicity of Zendesk’s API makes this feel overly complex in Genesys Cloud.
Is there a specific way to format the JSON for nested comment objects in the Zendesk Data Action? Or is this a limitation of the current integration version? We are using Genesys Cloud EU-WEST-1.
Have you tried structuring the Data Action with a strict JSON schema for the Zendesk API payload? The DTMF input itself is just a string or integer, but the mapping failure usually happens because the external request body doesn’t match Zendesk’s expected format for ticket comments.
Here is the HCL resource definition for the Data Action. Note the request block. It uses a JSON template to wrap the DTMF value ({{dtmf_input}}) into the specific structure Zendesk requires.
resource "genesyscloud_data_action" "zendesk_ticket_note" {
name = "Zendesk_Ticket_Comment"
description = "Appends IVR DTMF input to Zendesk ticket"
request {
method = "POST"
url = "https://your-subdomain.zendesk.com/api/v2/tickets/{{ticket_id}}/comments.json"
headers = {
"Content-Type" = "application/json"
"Authorization" = "Basic {{base64_encoded_creds}}"
}
body = jsonencode({
comment = {
body = "IVR Selection: {{dtmf_input}}"
public = false
}
})
}
response {
status_codes = ["200", "201"]
}
schema {
input = {
type = "object"
properties = {
ticket_id = { type = "string" }
dtmf_input = { type = "string" }
base64_encoded_creds = { type = "string" }
}
required = ["ticket_id", "dtmf_input", "base64_encoded_creds"]
}
}
}
In the Architect flow, use a Set Variables node to capture the DTMF value into dtmf_input. Then, call this Data Action. The jsonencode function in the HCL ensures the body is valid JSON before deployment. If you hardcode the JSON string in the UI, you often hit escaping errors that cause 400 Bad Request responses.
Note: Ensure the Zendesk API token or Basic Auth credentials are stored in a secure environment variable or secret manager, not hardcoded in the Terraform state. Exposing credentials in the base64_encoded_creds input requires careful handling in your CI/CD pipeline.
The best way to fix this is…
{
"id": "zendesk_ticket_update",
"name": "Zendesk Ticket Update",
"type": "http",
"request": {
"method": "PUT",
"uri": "https://{{org_name}}.zendesk.com/api/v2/tickets/{{ticket_id}}.json",
"headers": {
"Content-Type": "application/json",
"Authorization": "Basic {{base64_encoded_auth}}"
},
"body": {
"ticket": {
"comment": {
"body": "IVR Selection: {{dtmf_value}}",
"public": false
}
}
}
},
"response": {
"success": {
"code": 200
}
}
}
While the previous suggestion regarding JSON schema is technically accurate, relying solely on the Data Action for direct API hits can introduce latency and security management overhead, especially when handling sensitive customer data. From an AppFoundry partner perspective, we often see integrations struggle with maintaining OAuth tokens or Basic Auth credentials securely within the Architect environment.
A more robust approach is to leverage a lightweight middleware service hosted on AWS Lambda or Azure Functions. This service acts as a secure proxy. The Architect flow sends the DTMF data to your middleware via a simple HTTPS request. The middleware then handles the authentication context, constructs the proper Zendesk API payload, and executes the update.
This decouples your Genesys Cloud environment from direct dependency on Zendesk’s API rate limits and authentication mechanisms. It also provides a single point of failure management and logging. If Zendesk changes their API schema, you update the middleware, not every Architect flow. Ensure your middleware returns a 200 OK status to the Architect flow to prevent retry loops. This pattern scales much better for Premium Apps and multi-org deployments where credential rotation is frequent.
It’s worth reviewing at the data retention policies and legal hold implications before pushing raw DTMF inputs directly into external ticketing systems. While the technical mapping via Data Action is straightforward, as shown in the previous suggestions, the real risk lies in data governance and chain of custody. DTMF inputs often contain sensitive routing information or customer preferences that may be subject to specific retention rules under GDPR or local regulations. If you are exporting this data to Zendesk, you need to ensure that the external system’s audit trail aligns with your internal compliance requirements. A common oversight is failing to tag these exports with proper metadata for legal discovery. When bulk exporting recordings later, having IVR data siloed in Zendesk tickets complicates the reconstruction of the full customer journey. Instead of a direct push, consider logging the DTMF value to a Genesys Cloud interaction attribute first. This keeps the data within the native analytics and recording export framework. You can then configure a scheduled bulk export job to push aggregated interaction data to your S3 bucket or Zendesk, ensuring that all metadata is preserved with the correct timestamps and user context. This approach provides a clearer audit trail and makes it easier to apply legal holds across all channels, including digital and voice. It also prevents potential data loss if the Zendesk integration fails mid-flow. The documentation suggests using interaction attributes for temporary storage before external integration. This method ensures that even if the Data Action fails, the data remains accessible within Genesys Cloud for compliance reviews. Always verify that your export jobs include the necessary headers for chain of custody verification.