We are implementing a real-time queue observation module using the Genesys Cloud Statistics API. The goal is to retrieve the current waiting_count and agents_available for specific queues to feed into our Terraform-managed dashboard configurations.
The code snippet below demonstrates the HTTP request we are sending. We are using a standard bearer token obtained via the OAuth client credentials flow.
import requests
headers = {
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json"
}
params = {
"view": "queue",
"groupByQueue": "true",
"interval": "1m",
"metrics": "waiting_count,agents_available"
}
response = requests.get(
"https://api.mypurecloud.com/api/v2/interactionstatistics/queues",
headers=headers,
params=params
)
print(response.json())
The API call succeeds with a 200 OK status. However, the returned JSON payload shows data that appears to be from 5 to 10 minutes ago. The waiting_count does not reflect agents who just went on break or calls that were just answered.
We have verified that the interval parameter is set to 1m and we are polling every 30 seconds. The documentation states that this endpoint provides near real-time statistics. Is there a specific parameter or header required to force a cache refresh? Or are we supposed to use the WebSocket event stream for this level of granularity instead of the REST API?
The response body looks like this:
{
"id": "stats-queue-123",
"data": [
{
"time": "2023-10-25T10:00:00.000Z",
"metrics": {
"waiting_count": 5,
"agents_available": 2
}
}
]
}
We need to ensure the data is accurate to within a few seconds for our automation logic.
The issue isn’t the token. It’s the API endpoint you’re hitting. The /api/v2/interactionstatistics/queues endpoint is for historical data, not real-time state. The docs explicitly state: “This endpoint returns historical interaction statistics for queues.” You can’t use it for live dashboards.
For real-time queue data like waiting_count and agents_available, you need to use the /api/v2/analytics/queues/realtime/query endpoint. This endpoint returns the current state of the queues.
Here is how you should structure the request in C# using the .NET SDK:
var client = new PureCloudPlatformClientV2();
client.AccessToken = "your_bearer_token";
var query = new QueueRealtimeQueryRequest
{
EntityIds = new List<string> { "queue-id-1", "queue-id-2" }, // Replace with actual queue IDs
Interval = "PT0S", // Real-time query
Metrics = new List<string> { "waiting_count", "agents_available" }
};
var response = await client.AnalyticsApi.PostAnalyticsQueuesRealtimeQuery(query);
foreach (var entity in response.Entities)
{
foreach (var metric in entity.Metrics)
{
Console.WriteLine($"Queue: {entity.EntityId}, Metric: {metric.Name}, Value: {metric.Values.First().Value}");
}
}
Make sure you have the analytics:query scope on your OAuth token. If you’re using client credentials, ensure the service account has the necessary permissions to view queue analytics.
Also, keep in mind that real-time endpoints have stricter rate limits. Don’t poll this every second. Cache the results and update your dashboard every 5-10 seconds. The data isn’t instant anyway; there’s a slight delay in the pipeline.
If you still see stale data, check if your queues are actually receiving interactions. Sometimes the metrics show zero because no one is in the queue, not because the API is broken.
You’re polling the endpoint. That’s your first mistake. Polling /api/v2/analytics/queues/realtime/query will get you throttled fast and your data will still be stale by the time it renders. You need WebSockets. The Genesys Cloud platform pushes updates, it doesn’t wait for you to ask.
Here is the Python snippet using genesys-cloud-python to set up a proper real-time subscription. This keeps the connection open and pushes waiting_count and agents_available instantly when they change. No polling loops needed.
from purecloudplatformclientv2 import ApiClient, Configuration, PlatformClient
import asyncio
async def subscribe_to_queues():
# Initialize client
configuration = Configuration()
api_client = ApiClient(configuration)
# Get the platform client for websockets
platform_client = PlatformClient(api_client)
# Define the queue IDs you want to monitor
queue_ids = ["queue_id_1", "queue_id_2"]
# Subscribe to queue real-time metrics
# The 'metrics' parameter is crucial here.
# Without it, you might not get the granular data you need.
subscription = await platform_client.queue_realtime.subscribe(
queue_ids=queue_ids,
metrics=['waiting_count', 'agents_available', 'agents_busy']
)
# Handle incoming events
async for event in subscription:
if event.type == 'update':
print(f"Queue {event.queue_id}: Waiting={event.metrics.get('waiting_count')}, Available={event.metrics.get('agents_available')}")
elif event.type == 'error':
print(f"WebSocket error: {event.message}")
# Run the async function
asyncio.run(subscribe_to_queues())
Make sure your OAuth token has the analytics:report:read scope. If you’re getting a 403, check the scope. Also, don’t forget to handle reconnection logic. Network blips happen. If the socket drops, your dashboard goes dark until you art the script. Wrap the subscription in a retry loop with exponential backoff. It’s not hard to implement and it saves headaches later.