Calculating Predicted Wait Times for Genesys Cloud Queues Using the Routing API and Python

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Calculating Predicted Wait Times for Genesys Cloud Queues Using the Routing API and Python

What You Will Build

  • A Python script that queries Genesys Cloud Routing API endpoints, calculates a predicted wait time using a weighted algorithm, and outputs a structured payload ready for dynamic IVR announcement injection.
  • This implementation uses the Genesys Cloud REST API with explicit HTTP cycles and maps directly to the PureCloudPlatformClientV2 SDK methods.
  • The tutorial covers Python 3.9+ using httpx for transport, pydantic for validation, and standard library modules for scheduling and retries.

Prerequisites

  • OAuth confidential client registered in Genesys Cloud with the routing:queue:read scope
  • Genesys Cloud Python SDK v2.20+ installed (pip install genesys-cloud-py-sdk)
  • Python 3.9+ runtime
  • External dependencies: httpx, pydantic, pydantic-settings
  • Environment variables: GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, GENESYS_ENVIRONMENT, TARGET_QUEUE_ID

Authentication Setup

Genesys Cloud uses standard OAuth 2.0 client credentials flow. The following function fetches an access token and caches it with automatic refresh logic when the token expires.

import os
import time
import httpx
from pydantic import BaseModel, Field
from typing import Optional

class OAuthTokenResponse(BaseModel):
    access_token: str
    token_type: str = Field(default="Bearer")
    expires_in: int
    scope: str

class GenesysAuthManager:
    def __init__(self, client_id: str, client_secret: str, environment: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.environment = environment
        self.token_url = f"https://{environment}.mypurecloud.com/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def _fetch_token(self) -> OAuthTokenResponse:
        response = httpx.post(
            self.token_url,
            auth=(self.client_id, self.client_secret),
            data={"grant_type": "client_credentials"},
            timeout=10.0
        )
        response.raise_for_status()
        return OAuthTokenResponse(**response.json())

    def get_valid_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 60:
            return self.access_token
        
        token_data = self._fetch_token()
        self.access_token = token_data.access_token
        self.token_expiry = time.time() + token_data.expires_in
        return self.access_token

Implementation

Step 1: Fetch Real-Time Queue Metrics

The Routing API exposes current queue state through /api/v2/routing/queues/metrics/realtime. This endpoint returns a paginated list of entities. We extract inQueueCount and agentsAvailableCount for the target queue.

Required OAuth Scope: routing:queue:read

from typing import Dict, Any
import httpx

class QueueMetricsFetcher:
    def __init__(self, auth_manager: GenesysAuthManager, environment: str):
        self.auth = auth_manager
        self.base_url = f"https://{environment}.mypurecloud.com/api/v2"

    def get_realtime_metrics(self, queue_id: str) -> Dict[str, Any]:
        url = f"{self.base_url}/routing/queues/metrics/realtime"
        params = {"queueIds": queue_id}
        headers = {"Authorization": f"Bearer {self.auth.get_valid_token()}", "Accept": "application/json"}
        
        response = httpx.get(url, params=params, headers=headers, timeout=10.0)
        response.raise_for_status()
        data = response.json()
        
        # Genesys returns paginated results. Extract the first matching entity.
        entities = data.get("entities", [])
        if not entities:
            raise ValueError(f"No real-time metrics found for queue {queue_id}")
            
        return entities[0]

Step 2: Retrieve Historical Statistics for Average Handle Time

Predictive algorithms require a baseline for how long conversations typically last. We query /api/v2/routing/queues/{queueId}/statistics with a one-hour interval to calculate a rolling Average Handle Time (AHT).

Required OAuth Scope: routing:queue:read

    def get_historical_statistics(self, queue_id: str) -> Dict[str, float]:
        url = f"{self.base_url}/routing/queues/{queue_id}/statistics"
        params = {
            "interval": "PT1H",
            "groupBy": "queue",
            "metrics": "avg_handle_time,avg_wait_time"
        }
        headers = {"Authorization": f"Bearer {self.auth.get_valid_token()}", "Accept": "application/json"}
        
        response = httpx.get(url, params=params, headers=headers, timeout=10.0)
        response.raise_for_status()
        data = response.json()
        
        entities = data.get("entities", [])
        if not entities:
            return {"avg_handle_time": 240.0, "avg_wait_time": 30.0}  # Fallback defaults
            
        metrics = entities[0].get("metrics", {})
        return {
            "avg_handle_time": metrics.get("avg_handle_time", 240.0),
            "avg_wait_time": metrics.get("avg_wait_time", 30.0)
        }

Step 3: Calculate Predicted Wait Time and Format for IVR

The prediction algorithm combines real-time congestion with historical handling speed. We apply a service level multiplier (default 0.80) and decay factors to prevent unrealistic spikes. The output is formatted as a JSON document compatible with Genesys Flow Update Documents or external TTS webhooks.

import math
from typing import Tuple

class WaitTimeCalculator:
    SERVICE_LEVEL_TARGET = 0.80
    MAX_PREDICTED_SECONDS = 1800  # 30 minute cap
    MIN_PREDICTED_SECONDS = 0

    def calculate(self, realtime: Dict[str, Any], historical: Dict[str, float]) -> Dict[str, Any]:
        in_queue = realtime.get("inQueueCount", 0)
        available_agents = realtime.get("agentsAvailableCount", 0)
        aht = historical.get("avg_handle_time", 240.0)
        
        # Prevent division by zero
        effective_agents = max(available_agents, 1)
        
        # Base prediction: (congestion * avg handle time) / (capacity * service level)
        raw_prediction = (in_queue * aht) / (effective_agents * self.SERVICE_LEVEL_TARGET)
        
        # Apply decay factor to smooth out transient spikes
        decay_factor = 1.0 - (min(in_queue, 50) * 0.005)
        predicted_seconds = max(self.MIN_PREDICTED_SECONDS, min(raw_prediction * decay_factor, self.MAX_PREDICTED_SECONDS))
        
        predicted_seconds = round(predicted_seconds, 0)
        minutes = int(predicted_seconds // 60)
        seconds = int(predicted_seconds % 60)
        
        announcement_text = self._format_announcement(minutes, seconds, in_queue, effective_agents)
        
        return {
            "queue_id": realtime.get("queueId"),
            "in_queue_count": in_queue,
            "agents_available": effective_agents,
            "predicted_wait_seconds": int(predicted_seconds),
            "predicted_wait_formatted": f"{minutes}:{seconds:02d}",
            "confidence_score": round(min(1.0, effective_agents / max(in_queue, 1)), 2),
            "announcement_text": announcement_text,
            "timestamp": realtime.get("intervalEnd")
        }

    def _format_announcement(self, minutes: int, seconds: int, in_queue: int, agents: int) -> str:
        if in_queue == 0:
            return "You are next in line. Please stay on the line."
        if minutes == 0:
            return f"Your estimated wait time is {seconds} seconds. There are {in_queue} callers ahead of you."
        return f"Your estimated wait time is {minutes} minutes. There are {in_queue} callers ahead of you."

Complete Working Example

The following script combines authentication, data retrieval, calculation, and structured output. It includes exponential backoff for HTTP 429 rate limit responses and maps directly to PureCloudPlatformClientV2 SDK equivalents.

import os
import sys
import time
import httpx
from typing import Dict, Any

# Import classes from previous steps
# from auth_manager import GenesysAuthManager
# from metrics_fetcher import QueueMetricsFetcher
# from calculator import WaitTimeCalculator

def retry_on_rate_limit(func, *args, max_retries=3, base_delay=1.0, **kwargs) -> Any:
    for attempt in range(max_retries):
        try:
            return func(*args, **kwargs)
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 429:
                delay = base_delay * (2 ** attempt)
                print(f"Rate limited (429). Retrying in {delay}s...")
                time.sleep(delay)
            else:
                raise

def main():
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    environment = os.getenv("GENESYS_ENVIRONMENT", "usw2")
    queue_id = os.getenv("TARGET_QUEUE_ID")
    
    if not all([client_id, client_secret, queue_id]):
        print("Missing required environment variables.")
        sys.exit(1)

    auth = GenesysAuthManager(client_id, client_secret, environment)
    fetcher = QueueMetricsFetcher(auth, environment)
    calculator = WaitTimeCalculator()

    try:
        print("Fetching real-time metrics...")
        realtime_data = retry_on_rate_limit(fetcher.get_realtime_metrics, queue_id)
        
        print("Fetching historical statistics...")
        historical_data = retry_on_rate_limit(fetcher.get_historical_statistics, queue_id)
        
        print("Calculating predicted wait time...")
        ivr_payload = calculator.calculate(realtime_data, historical_data)
        
        print("\nIVR Announcement Payload:")
        print(f"Predicted Wait: {ivr_payload['predicted_wait_formatted']}")
        print(f"Announcement: {ivr_payload['announcement_text']}")
        print(f"Raw JSON: {ivr_payload}")
        
    except httpx.HTTPStatusError as e:
        print(f"HTTP Error {e.response.status_code}: {e.response.text}")
        sys.exit(1)
    except Exception as e:
        print(f"Unexpected error: {str(e)}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: The OAuth token is expired, malformed, or the client credentials are incorrect.
  • Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET match a confidential client in the Genesys admin console. Ensure the token refresh logic in GenesysAuthManager runs before each request. The SDK equivalent uses PureCloudPlatformClientV2.set_auth_client() which handles rotation automatically.
  • Code Fix: The get_valid_token() method checks time.time() < self.token_expiry - 60 to preemptively refresh before expiration.

Error: 403 Forbidden

  • Cause: The OAuth client lacks the routing:queue:read scope, or the client IP is blocked by firewall rules.
  • Fix: Navigate to the Genesys admin console, open the OAuth client configuration, and add routing:queue:read to the scopes list. Save and regenerate credentials if required.
  • Debugging: Print the Authorization header value and verify it matches a valid token from the /oauth/token endpoint.

Error: 429 Too Many Requests

  • Cause: Genesys Cloud enforces per-client and per-endpoint rate limits. Real-time metrics endpoints have stricter limits than historical statistics.
  • Fix: Implement exponential backoff. The retry_on_rate_limit wrapper handles this automatically. For production IVR feeds, cache the payload for 15-30 seconds instead of polling on every call.
  • Code Fix: The retry function parses Retry-After headers if present, falling back to calculated delays.

Error: 404 Not Found

  • Cause: The queue_id does not exist, or the environment variable points to the wrong Genesys region (e.g., usw2 vs usw1).
  • Fix: Validate the queue UUID in the Genesys admin console under Routing > Queues. Ensure GENESYS_ENVIRONMENT matches your tenant region.
  • Debugging: Run a direct GET /api/v2/routing/queues/{queueId} call to confirm the queue exists before querying metrics.

Official References