Statistics API returning stale waiting counts for real-time queue observation

Dealing with a very strange bug here with the Genesys Cloud Statistics API when trying to fetch real-time queue metrics. I am polling /api/v2/analytics/queues/realtime for waiting_count and agents_available but the values lag by ~15 seconds compared to the UI. My Slack Bolt handler expects sub-second updates. Am I missing a cache-bust header or is this a known limitation?

200 OK: { “data”: [{ “summary”: { “waiting_count”: 0, “agents_available”: 5 } }] }

The best way to fix this is to switch to WebSockets. The REST API caches for 15 seconds. Use the /v2/analytics/queues/realtime stream endpoint instead.

In Go, use gorilla/websocket to maintain the persistent connection and parse the JSON stream directly.

WebSocket approach is correct, but be careful with the connection lifecycle in Azure Functions. If your function instance sleeps, the WSS connection drops silently. You need a dedicated host, not a standard HTTP trigger.

Use an Azure WebJob or a always-on Function App with Durable Entities to manage the state. Here is the minimal .NET setup to keep the stream alive:

// Install-Package GenesysCloudPlatformClientV2
var config = PlatformConfiguration.Builder()
 .WithClientId("id")
 .WithClientSecret("secret")
 .Build();

var platformClient = PlatformClientFactory.CreatePlatformClient(config);
var analyticsClient = platformClient.Analytics;

// Keep this running continuously in a background task
await analyticsClient.RealtimeQueuesGetAsync(); 

See the warning here: https://support.genesys.cloud/articles/wss-timeout-behavior. Do not poll REST API for sub-second needs; the cache is hard-coded.

Yep, this is a known issue. The suggestion above regarding WebSockets is valid, but if you are stuck with REST, you are hitting the documented cache layer. The documentation states: “Real-time analytics endpoints may return cached data with a latency of up to 15 seconds.” This is not a bug; it is a design constraint.

For a FastAPI backend, I usually bypass the REST cache by implementing a simple polling loop with exponential backoff for retries, though it still suffers from the initial lag. If you need true sub-second updates, you must use the WebSocket stream.

Here is how I handle the connection in Python using websockets:

import websockets
import json

async def connect_ws():
 uri = "wss://api.euc1.genesyscloud.com/v2/analytics/queues/realtime"
 headers = {"Authorization": f"Bearer {access_token}"}
 async with websockets.connect(uri, extra_headers=headers) as ws:
 async for message in ws:
 data = json.loads(message)
 process_queue_stats(data)

Check the official WebSocket docs for scope requirements.

To fix this easily, this is to leverage the websocket stream for sub-second updates, as the rest cache is immutable. in architect, use a data action to trigger the flow on interaction.created instead of polling. this pushes state changes directly to your backend without latency.

{
 "method": "post",
 "url": "https://your-api.com/webhook",
 "headers": { "authorization": "bearer {{oauth.token}}" }
}