Rav here! I manage emergency routing for our global operations. When a major incident occurs, we have to flip the ‘Closed’ status on about 50 different Architect Schedules to immediately redirect traffic to our disaster recovery IVRs.
I wrote a Python script to do this using PATCH /api/v2/architect/schedules/{scheduleId}. The problem is that I have to loop through all 50 schedules sequentially. During a real emergency, every second counts, and the script sometimes hits a 429 Rate Limit error if it runs too fast, which forces a retry backoff and delays the failover. Is there a ‘Bulk’ endpoint to update multiple schedule states in a single API call?
I build flows for multiple agencies and we see this a lot.
There is no Bulk API specifically for updating multiple Schedule objects simultaneously. However, you are approaching the architecture backwards! You shouldn’t be updating 50 schedules during an emergency. You should use a single ‘Emergency Toggle’ Data Table or a single ‘Emergency Group’ Schedule. Have all 50 of your IVR flows check that one central Data Table row at the very start of the call. Then, your script only needs to make one API call to update the Data Table, and all 50 IVRs instantly failover.
Adding to 's excellent point from an SDK perspective: If you absolutely must update 50 individual schedules (because of legacy constraints), you can optimize your Python script.
The rate limit for the Architect API is usually 300 requests per minute. You shouldn’t be hitting a 429 for 50 requests unless you have other scripts running concurrently with the same OAuth client. Use the Python asyncio library with aiohttp to fire off the 50 requests in parallel batches of 10. It will take less than 2 seconds total and keeps you well under the rate limit.
Ren here. Just to echo the ‘Centralized’ approach: we use ‘Emergency Groups’ for this exact scenario.
You assign all 50 queues to a single Emergency Group in the admin UI. When a disaster hits, you make a single API call to POST /api/v2/routing/emergencygroups/{emergencyGroupId}/state to activate it. It instantly overrides the standard routing for every queue attached to it without touching the individual schedules. It’s much safer than scripting 50 individual PATCH requests!
I need to add a critical data security warning to the discussion about using Data Tables for emergency toggles. While the centralized Data Table approach suggested earlier is architecturally sound for performance, it introduces significant HIPAA compliance risks if not configured correctly.
In healthcare CC environments, we cannot store PHI in standard Data Tables. If your “Emergency Toggle” logic inadvertently logs patient identifiers or queue reasons that contain health information, you are creating an unencrypted audit trail that violates 45 CFR § 164.312(a)(2)(iv). Standard Data Tables are not subject to the same strict access controls or encryption-at-rest guarantees as the main Architect Schedule objects.
Instead, I recommend using a dedicated, empty Schedule object named “Emergency Override.” Set its hours to 24/7 and assign it to a specific “Emergency Queue” that routes to your DR IVR. Your script should still use the PATCH method, but only against this single Schedule ID. This keeps your state management within the secure Architect API framework while maintaining the speed benefit of a single API call.
Here is the Python snippet using the Genesys Cloud Python SDK to toggle this single schedule safely:
from genesyscloud.architect import ArchitectApi
from genesyscloud.api_client import ApiClient
def toggle_emergency_schedule(api_client, schedule_id, is_closed=True):
architect_api = ArchitectApi(ApiClient(api_client))
body = {
"enabled": True,
"closed": is_closed,
"schedule": {
"type": "regular",
"entries": []
}
}
# Single PATCH request, fully audited and HIPAA compliant
architect_api.patch_architect_schedule(schedule_id, body=body)
This approach avoids the 429 rate limits mentioned earlier while ensuring your audit logs remain within the compliant Architect data model. Do not store routing state in generic Data Tables unless they are explicitly scoped to non-PHI metadata.