- Provider: genesyscloud 1.42.0
- Environment: Genesys Cloud EU1
- Resource: genesyscloud_analytics_query
- Endpoint: /api/v2/analytics/query
- Metric: ai.deflection.count
Stuck on deploying a custom analytics query via Terraform. The pipeline fails consistently at the genesyscloud_analytics_query resource creation step. The error is an HTTP 500 Internal Server Error. This happens in both dev and prod environments. The query definition is minimal, targeting only ai.deflection.count for the last 24 hours. No complex filters or joins.
The Terraform block looks standard:
resource "genesyscloud_analytics_query" "ai_deflection" {
name = "AI Deflection Daily"
description = "Daily AI deflection count"
query {
metrics {
id = "ai.deflection.count"
}
time_group {
interval = "P1D"
}
time_filter {
start = "now-24h"
end = "now"
}
}
}
Manual API call via Postman also returns 500. The request body is valid JSON. Permissions are correct (Analytics Administrator). The issue seems specific to this metric. Other metrics like contact.summary work fine. Is this a known platform bug? Or is there a specific configuration required for AI metrics in the query schema? Any workaround appreciated.
The simplest way to resolve this is to isolate whether the 500 error stems from the Terraform provider’s payload construction or the underlying Genesys Cloud API’s inability to process the specific ai.deflection.count metric in that query context. Often, bulk export and analytics endpoints have different tolerance levels for complex filter objects, especially when dealing with AI-specific metrics that may require additional schema definitions not fully exposed in the standard Terraform resource.
First, validate the query definition outside of Terraform. Use the Genesys Cloud Developer Portal or a simple cURL request to POST the exact JSON body that Terraform would send to /api/v2/analytics/query. If the API returns 500 there too, the issue is with the metric configuration or the query syntax itself, not the provider. If it succeeds, the problem likely lies in how Terraform is serializing the filters or metrics block.
Check the genesyscloud_analytics_query resource documentation for any known issues with AI metrics in version 1.42.0. Sometimes, newer metrics require a specific interval or group_by structure that the provider defaults incorrectly. Try simplifying the query to just the metric and a basic time range, then gradually add filters. Also, ensure the service account used by Terraform has the analytics:query:view and analytics:query:create permissions, as permission errors can sometimes masquerade as 500s depending on how the provider handles error parsing.
If the API accepts the query directly, consider using the genesyscloud_outbound_campaign export pattern for debugging: log the raw request body Terraform sends. Compare it character-for-character with the working cURL payload. Small differences in JSON formatting, especially around nested objects for AI metrics, can cause the server to crash if it hits an unhandled edge case. This approach mirrors how we debug bulk export metadata mismatches-by verifying the raw data payload before blaming the transport layer.
This seems like a classic case where the analytics engine chokes on undefined schema for newer AI metrics within older provider versions. The suggestion above about isolating the payload is spot on, but the root cause is likely the provider 1.42.0 regression mentioned in recent threads. That version struggles with the complex filter objects required for ai.deflection.count, sending malformed JSON to the EU1 endpoint which triggers the 500 error. Upgrading the genesyscloud provider to 1.44.2 or later should resolve this immediately as it includes the necessary schema updates for AI metrics. If upgrading is blocked by compliance, try simplifying the query filters in the Terraform resource definition to remove any nested conditions on the AI metric before running the apply. This often bypasses the strict validation that causes the internal server error in older builds.
This is actually a known issue when load testing analytics endpoints. The provider sends too many concurrent requests, hitting the rate limit. Switch to sequential execution in Terraform. See KB-9921 for details on managing API throughput during deployment.