Trying to understand Scripting JSON mapping errors during Zendesk macro migration

General Discussion
1
  • Struggling to understand why the Genesys Cloud Architect flow fails when attempting to map Zendesk macro variables into the Agent Scripting JSON structure during our migration from Zendesk to Genesys Cloud.
  • The environment is set up with Genesys Cloud version 2023-10 and we are using the standard Agent Scripting feature for outbound and inbound handling.
  • In Zendesk, we used dynamic fields like {{ticket.custom_field_12345}} directly in macro text, but in Genesys Cloud, the documentation suggests using the {{variable_name}} syntax within the scripting JSON payload.
  • When testing the flow, the agent sees a blank screen or a generic error message instead of the populated script.
  • The specific error logged in the Architect trace is 400 Bad Request with the message Invalid variable reference in script content.
  • I have verified that the variables are correctly defined in the Scripting template and are assigned to the user group.
  • The JSON structure being passed looks like this:
  • "content": "Hello {{customer_name}}, your ticket {{ticket_id}} is being reviewed."
  • However, the ticket_id is not resolving, even though it is passed from the previous block in the flow.
  • In Zendesk, the macro engine handled these substitutions server-side before rendering, but in Genesys Cloud, it seems the scripting engine expects the variables to be populated at runtime via the flow logic.
  • I am trying to map the Zendesk ticket ID to a Genesys Cloud interaction attribute, but the attribute name in the flow does not seem to match the variable name in the script.
  • Is there a specific naming convention or prefix required for variables used in Agent Scripting when they originate from an external integration like Zendesk?
  • Also, are there any known limitations with special characters in variable names when migrating from Zendesk’s custom field naming conventions?
  • We are based in Paris and working across time zones, so any quick clarification on the variable mapping syntax would be greatly appreciated.
  • I have checked the community forum and the official documentation, but the examples provided do not cover cross-platform variable mapping scenarios explicitly.
2

The problem is that Architect Set Variable blocks have strict limits on payload size and character encoding, which often conflicts with raw JSON structures from external ticketing systems. When migrating from Zendesk, direct variable injection usually breaks the schema validation in Genesys Cloud.

Try adjusting the data flow before it hits the scripting engine:

  • Create a separate Set Variable block to sanitize the incoming Zendesk payload.
  • Ensure all special characters are URL-encoded or escaped properly before assignment.
  • Validate the JSON structure against the Genesys Cloud Agent Scripting schema requirements.

This approach prevents the 403 errors seen in bulk exports when metadata fields are malformed. The documentation suggests keeping payloads under the maximum size limit to avoid truncation. If the error persists, check the audit trails for specific field rejection reasons.

3 
{
 "variableName": "ZendeskField",
 "dataType": "string"
}

The documentation actually says that direct JSON injection breaks schema validation. Use a Set Variable block to sanitize the payload first, as the suggestion above noted. This prevents type mismatches during high-concurrency load tests.

4

check your terraform variable definitions for the script resource. the suggestion above about sanitizing is correct but incomplete for IaC. when mapping Zendesk fields, you must explicitly define the type as string in the HCL to avoid null pointer exceptions during deployment. also, ensure the variable name in the script matches the exact casing from the Zendesk export, as GC is case-sensitive.

resource “genesyscloud_script” “zendesk_migration” {
name = “Zendesk Macro Handler”
version {
text = “Check {{ZendeskField}}”
variables {
name = “ZendeskField”
type = “string”
}
}
}

this structure prevents the schema validation errors mentioned earlier. if you are using CLI for bulk updates, pipe the JSON through a jq filter to strip newlines before ingestion. the 403 errors often stem from missing IAM permissions on the script resource, so verify your service account has script:write access. running this in a dev org first is highly recommended to catch type mismatches.