Context:
I cannot figure out why the platform API returns a 400 Bad Request when invoking a Data Action to update a custom object. The payload matches the schema defined in AppFoundry, yet the error logs cite invalid property type for a standard string field. This occurs specifically within our multi-tenant OAuth flow.
Question:
Is there a known discrepancy between the AppFoundry schema definition and the runtime validation logic for custom objects in Genesys Cloud?
TL;DR: Type coercion mismatch.
This looks like a schema definition vs runtime payload type mismatch. Ensure the JSON string is not being quoted as a literal string in the Data Action input mapping. Check the genesyscloud_data_action HCL request_body for explicit jsonencode() usage to force correct serialization.
This happens because the platform’s strict JSON schema validation rejecting implicit type conversions, particularly when string fields receive null or undefined values that the runtime engine attempts to cast. The suggestion above regarding jsonencode() in HCL is spot on for Terraform deployments, but if you are configuring this directly in the Genesys Cloud UI via AppFoundry or a Data Action, the issue often stems from how the input mapping handles missing optional fields.
When a custom object has an optional string field, the API expects either a valid string or nothing (the key should be omitted entirely). Sending an empty string "" or a null value can trigger the invalid property type error if the schema definition lacks explicit nullability flags. This is especially common in multi-tenant environments where data payloads vary significantly across tenants.
To resolve this, ensure your Data Action’s input mapping explicitly filters out null values before sending the payload. If using a JavaScript snippet in the Data Action, implement a clean-up function:
function cleanPayload(obj) {
for (var key in obj) {
if (obj.hasOwnProperty(key)) {
if (obj[key] === null || obj[key] === undefined) {
delete obj[key];
}
}
}
return obj;
}
Apply this function to the request body before the API call. This ensures only populated fields are sent, aligning perfectly with the strict JSON schema expectations. Additionally, verify that the AppFoundry schema definition matches the current API version, as schema updates can sometimes lag behind runtime validation rules.
I’d suggest checking out at the Performance dashboard metrics for queue activity before committing to the API logic. The suggestion above regarding type coercion is technically accurate, but the root cause often lies in how the Architect flow handles null values during the data mapping phase.
When a custom object field is optional in the AppFoundry schema, the Architect Data Action node sometimes passes an empty string instead of a proper null value. The platform’s runtime validation engine treats this as a type mismatch for string fields expecting strict JSON compliance.
To resolve this, verify the metric definitions are correctly mapped within the Performance view configuration. Specifically, check the Agent Scripting metrics to ensure no lingering session data is interfering with the payload structure. The fix usually involves adding a conditional node in the Architect flow to explicitly set the field to null if the source data is empty, rather than allowing the default mapping to proceed.
Here is a sample configuration for the Data Action input mapping:
{
"custom_field": "{{if !empty(source_data.custom_field), source_data.custom_field, null}}"
}
This ensures that the JSON payload adheres to the strict schema validation rules. The Genesys Cloud environment exhibits a critical metric divergence where the Outbound Campaign dashboard reports adherence, yet the underlying Architect flow analytics show validation failures. By forcing the correct serialization at the flow level, you eliminate the need for complex API-level workarounds.
Review the conversation detail views to confirm that the updated payload is being transmitted correctly. If the issue persists, check for any active session locks that might be preventing the custom object update from completing. The easiest way to fix this is to validate the segment logic against the Performance dashboard metrics before deploying the change.