Hello community! 
We are running into a specific issue while synchronizing contact data from Salesforce to Genesys Cloud using the native Data Actions framework. We are currently on API version 14 and utilizing AWS US-East-1 for our organization.
The problem occurs when we attempt to push records containing ‘createdDate’ or ‘lastModified’ fields. The API returns a 400 Bad Request error with the message Invalid date format: YYYY-MM-DD. We are confident the source data is correct within Salesforce, but Genesys Cloud seems to reject the payload immediately upon receipt.
Pro tip! Always verify the timezone offset when constructing ISO 8601 strings programmatically before sending them via SDK. 
Has anyone successfully handled this edge case? It would be helpful to know if we need to include milliseconds in the timestamp string. We are eager to resolve this blockage so our sync pipeline can resume normal operations.
The error message indicates strict validation against RFC 3339 standards. Genesys Cloud requires milliseconds and timezone offset for date fields within the payload JSON schema.
Review the Terraform configuration for the genesys_cloud_data_action resource. Ensure the expression evaluates to a string rather than a timestamp object before serialization.
resource "genesys_cloud_data_action" "sync_contacts" {
name = "Salesforce Contact Sync"
action_definition_id = "00000000-0000-0000-0000-000000000000"
input {
json_expression = "{\"createdDate\": \"${data.salesforce_contact.created_at}\", \"status\": \"active\"}"
}
}
Update the date formatting logic to append Z or +00:00 explicitly if the source system does not include it.
We are dealing with similar timestamp headaches in our Teams Direct Routing setup. The SBC config often treats ISO strings differently depending on the firmware version of the gateway.
It is worth testing the payload locally using a PowerShell script before committing it to production. We usually run a quick test against the Data Action schema definition in the Admin UI or via /api/v2/dataactions/{actionId} to validate the expected format first.
In our Teams integration, we found that skipping the milliseconds caused latency spikes during peak call volumes. You might want to ensure your generator adds .000Z at the end of the timestamp string. This matches how the Microsoft Graph API handles time data in most cases.
Latency in Data Actions impacts predictive routing model performance directly. We are currently running an A/B test comparing batch sizes of 100 versus 50 records per invocation.
Results show that larger payloads increase processing time by approximately 40 milliseconds on average. This delay is significant when calling the /routing/api/v2/queues/{queueId}/predictedskills endpoint immediately after data ingestion.
Current recommendation: Limit payload size to 50 objects to maintain model inference speed within acceptable SLAs. If performance degrades further, consider splitting the sync into multiple parallel Data Actions rather than one large batch.