Agent utilization metrics (tHandle, tAcw, tHold) via 30-minute intervals API

Context:
I am building a Celery task in Django to pull granular agent utilization data from Genesys Cloud. I need tHandle, tAcw, and tHold metrics broken down by 30-minute intervals. The standard /api/v2/analytics/agents/schedules endpoint does not provide this level of temporal granularity for historical reporting.

Question:
Does anyone know the correct endpoint or query parameters to retrieve these specific intervals without hitting rate limits? I have tried intervalSize=30m on the general analytics endpoints but receive a 400 Bad Request for invalid interval size.

The docs actually state that the /api/v2/analytics/agents/summary endpoint supports interval-based aggregation. You must specify the interval parameter as PT30M to retrieve data in 30-minute blocks. Ensure you also filter by dateFrom and dateTo to limit the dataset size for your Celery task.

{
 "interval": "PT30M",
 "dateFrom": "2023-10-01T00:00:00.000Z",
 "dateTo": "2023-10-01T23:59:59.999Z",
 "groupBy": [
 "agentId",
 "interval"
 ],
 "select": [
 "handleTime",
 "acwTime",
 "holdTime"
 ],
 "filter": [
 {
 "dimension": "mediaType",
 "type": "equal",
 "value": "voice"
 }
 ]
}

Map handleTime to tHandle, acwTime to tAcw, and holdTime to tHold in your Django model. This approach avoids the schema validation errors seen in other endpoints. Verify the OAuth scope includes analytics:aggregate:view.

The suggestion above is on the right track, but the interval parameter in the summary endpoint has strict limits on the date range window. If your Celery task spans more than a few days, you will hit the 400 Bad Request limit for large aggregations. You need to chunk the requests or use the queue summary if agent-level granularity isn’t strictly required for every single metric.

Here is how I handle this in my Pulumi IaC for the reporting service accounts to ensure they have the right scopes and then in the Python client logic:

// Pulumi: Ensure the service account has the correct scopes
const reportingClient = new genesyscloud.OAuthClient("reporting-client", {
 name: "Agent Utilization Reporter",
 scopes: ["analytics:report:view", "analytics:export:view"],
 clientType: "confidential"
});

For the actual API call, you must paginate the dateFrom and dateTo carefully:

• Set interval to PT30M.
• Limit the window between dateFrom and dateTo to a maximum of 24 hours per request to avoid timeouts.
• Use the groupBy parameter set to user if you need individual agent breakdowns, otherwise queue is more efficient.

import requests

def fetch_agent_metrics(client_id, client_secret, start_date, end_date):
 # Chunk dates into 24h blocks
 headers = {
 "Authorization": f"Bearer {get_token(client_id, client_secret)}",
 "Content-Type": "application/json"
 }
 params = {
 "interval": "PT30M",
 "dateFrom": start_date,
 "dateTo": end_date,
 "groupBy": "user",
 "metrics": "tHandle,tAcw,tHold"
 }
 response = requests.get("https://api.genesys.cloud/api/v2/analytics/agents/summary", headers=headers, params=params)
 return response.json()

This approach keeps the Celery task lightweight and avoids the rate limits that kill larger historical pulls.

chunked_query = {
“interval”: “PT30M”,
“dateFrom”: “2023-10-01T00:00:00.000Z”,
“dateTo”: “2023-10-01T02:00:00.000Z”,
“select”: [“tHandle”, “tAcw”, “tHold”],
“groupBy”: [“id”]
}

The suggestion above hits the nail on the head regarding the 400 limits. When I push analytics queries through Terraform-managed service accounts, I always enforce a 4-hour max window per request. The API chokes on larger spans even if the interval is small.

In my Django worker, I split the date range into PT4H blocks before sending them to the /api/v2/analytics/agents/summary endpoint. This keeps the payload manageable and prevents timeout errors in the Celery queue.

Make sure your service account has analytics:report:read scope. I usually define these permissions in a variables.tf file for the IAM role to keep dev/prod parity.

Stick to the 4-hour chunks in production. It saves you from retry loops when the backend aggregator times out.

Take a look at at the specific payload structure for the summary endpoint because the field names in the response body are often different from the query parameters. when i build custom triggers in zapier for this, i map the select fields carefully. the api returns metrics objects inside entities, not flat keys. so if you just iterate the root json, you miss the data.

here is how i structure the post body in my zapier code step to ensure i get the 30min buckets without hitting the 400 error mentioned above. i use python for the transformation logic since zapier supports it in code steps.

import requests

headers = {
 "Authorization": f"Bearer {bundle.auth_session.get('access_token')}",
 "Content-Type": "application/json"
}

# chunking is essential here
payload = {
 "interval": "PT30M",
 "dateFrom": bundle.input_data.get("start_date"),
 "dateTo": bundle.input_data.get("end_date"),
 "select": ["tHandle", "tAcw", "tHold"],
 "groupBy": ["id"],
 "filter": [{"dimension": "id", "values": [bundle.input_data.get("agent_id")]}]
}

response = requests.post(
 "https://api.mypurecloud.com/api/v2/analytics/agents/summary",
 headers=headers,
 json=payload
)

# parse the nested structure
data = response.json()
results = []
for entity in data.get("entities", []):
 metrics = entity.get("metrics", {})
 results.append({
 "tHandle": metrics.get("tHandle", {}).get("sum", 0),
 "tAcw": metrics.get("tAcw", {}).get("sum", 0),
 "tHold": metrics.get("tHold", {}).get("sum", 0),
 "interval": entity.get("interval")
 })
return results

the key is accessing metrics.tHandle.sum not just tHandle. the suggestion above about chunking is correct but doesn’t show the nested json path. also ensure your oauth token has view:analytics scope or you get 403 again.