Looking for advice on handling state drift for genesyscloud_script resources when using Terraform in a GitHub Actions workflow. The setup involves a standard IaC pipeline (Terraform 1.8.3, GC Provider 1.12.0) targeting a US-East tenant.
The specific issue is that the genesyscloud_script resource appears to update successfully during terraform apply, returning a 200 OK and updating the state file. However, on the subsequent plan, Terraform detects a diff in the content block, specifically around the blocks structure for certain script elements like say or play. The provider seems to be normalizing the JSON payload differently than the API returns it in the GET response, causing perpetual drift.
This is problematic for our CI/CD strategy as we enforce a “plan must be empty” gate. The script itself works fine in Architect and Agent Desktop, so it is purely a state synchronization issue. The error in the plan output highlights changes in the id of nested blocks and slight variations in whitespace handling within the text fields of say blocks.
- Attempted to use
ignore_changes = [content] on the resource block, but this prevents legitimate updates to the script text from being deployed via code, which defeats the purpose of CX-as-Code.
- Tried pinning the provider version to 1.11.5, but the drift behavior persists, suggesting it is a fundamental schema mapping issue in how the provider serializes the script JSON.
Here is a snippet of the relevant HCL:
resource "genesyscloud_script" "onboarding_script" {
name = "Onboarding Flow v2"
description = "Automated onboarding script"
enabled = true
content {
blocks {
# Simplified for brevity
}
}
}
Is there a recommended pattern for stabilizing script content in Terraform state? Or should we be using a different approach for version-controlling scripts that frequently change?
lifecycle {
ignore_changes = [
created_by,
last_updated_by,
version
]
}
Ah, this is a known issue… The provider picks up metadata changes from the API during load spikes. Adding that lifecycle block to the script resource should stop the drift detection in your CI/CD pipeline.
This is caused by…
Hi. The lifecycle block helps, but for recording exports, metadata like chain_of_custody_ref often updates asynchronously. Consider using genesyscloud_script data sources to read current state instead of managing full CRUD. This avoids drift when legal hold tags are injected dynamically via API. Check the audit trail for unexpected updates.
As far as I remember, the lifecycle block is the standard fix, but it often misses the root cause when dealing with high-concurrency environments. The genesyscloud_script resource in the provider sometimes struggles with timestamp precision during rapid successive calls. If your CI/CD pipeline is running multiple plans in parallel or if there are background processes updating script metadata (like version increments from automated testing), the state file gets out of sync before the drift check even runs. I have seen this happen frequently when load testing with JMeter scripts that trigger API updates concurrently. The provider might return a 200 OK for the apply, but the underlying database transaction hasn’t fully committed the metadata fields like version or last_updated_date before the next plan reads the state. A more robust approach than just ignoring changes is to add a small sleep or wait condition in your GitHub Actions workflow after the apply step, ensuring the Genesys Cloud backend has fully propagated the changes. Alternatively, check if your script has any associated data actions or outbound campaigns that might be auto-updating the script’s usage metrics, which can sometimes trigger unexpected diffs. If the drift persists despite the lifecycle block, try running terraform refresh before the plan to force a state update from the live API, rather than relying on the cached state from the previous apply. This ensures you are comparing against the most recent actual configuration, not a stale snapshot. Also, verify that your tenant isn’t hitting API rate limits during the apply phase, which can cause partial updates that look successful but leave the state inconsistent.