Implementing Dynamic Pacing Controls in Genesys Cloud Outbound with Python

What You Will Build

• The script continuously monitors real-time queue performance, applies a PID controller to calculate optimal dial rates, and updates outbound campaign pacing parameters to maintain target service levels.
• This uses the Genesys Cloud CX Analytics API for real-time metrics and the Outbound Campaign REST API for parameter updates.
• The implementation is written in Python using the requests library and standard mathematical libraries.

Prerequisites

• OAuth client type: Confidential client (client credentials grant)
• Required scopes: analytics:query:read, outbound:campaign:read, outbound:campaign:write
• API version: Genesys Cloud CX v2 REST API
• Language/runtime: Python 3.9+
• External dependencies: requests, typing, time, math, os

Authentication Setup

Genesys Cloud uses OAuth 2.0 for all API access. The client credentials grant is required for server-to-server integrations. Token expiration must be tracked to avoid 401 errors during long-running control loops.

import os
import time
import requests
from typing import Optional, Dict, Any

GENESYS_BASE_URL = os.environ.get("GENESYS_BASE_URL", "https://api.mypurecloud.com")
AUTH_URL = os.environ.get("GENESYS_AUTH_URL", "https://login.mypurecloud.com/oauth/token")
CLIENT_ID = os.environ["GENESYS_CLIENT_ID"]
CLIENT_SECRET = os.environ["GENESYS_CLIENT_SECRET"]

class GenesysAuth:
    def __init__(self) -> None:
        self._token: str = ""
        self._expires_at: float = 0.0

    def get_token(self) -> str:
        if time.time() < self._expires_at:
            return self._token
        
        payload = {
            "grant_type": "client_credentials",
            "client_id": CLIENT_ID,
            "client_secret": CLIENT_SECRET
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        
        response = requests.post(AUTH_URL, data=payload, headers=headers)
        response.raise_for_status()
        
        data = response.json()
        self._token = data["access_token"]
        self._expires_at = time.time() + (data["expires_in"] * 0.9)
        return self._token

The 0.9 multiplier on expires_in creates a safety buffer to prevent mid-request token expiration. The token is cached in memory and refreshed automatically when the loop runs.

Implementation

Step 1: Fetch Real-Time Queue Metrics

The real-time queue statistics endpoint returns current conversation states. You must specify the queue identifiers and the exact metric names required for pacing calculations. The required scope is analytics:query:read.

import requests
from typing import Dict, Any

def fetch_queue_metrics(auth: GenesysAuth, queue_id: str) -> Dict[str, Any]:
    url = f"{GENESYS_BASE_URL}/api/v2/queues/stats/realtime"
    params = {
        "queueIds": queue_id,
        "metricNames": "conversation.answered,conversation.abandoned,conversation.active"
    }
    headers = {"Authorization": f"Bearer {auth.get_token()}"}
    
    response = requests.get(url, params=params, headers=headers)
    
    if response.status_code == 429:
        retry_after = int(response.headers.get("Retry-After", 1))
        time.sleep(retry_after)
        return fetch_queue_metrics(auth, queue_id)
        
    response.raise_for_status()
    data = response.json()
    
    if not data.get("entities"):
        raise ValueError("No queue entities returned. Verify queueId exists.")
        
    entity = data["entities"][0]
    metrics = entity.get("metrics", {})
    
    answered = metrics.get("conversation.answered", {}).get("count", 0)
    abandoned = metrics.get("conversation.abandoned", {}).get("count", 0)
    active = metrics.get("conversation.active", {}).get("count", 0)
    
    return {
        "answered": answered,
        "abandoned": abandoned,
        "active": active,
        "total": answered + abandoned
    }

Expected response structure from Genesys Cloud:

{
  "entities": [
    {
      "id": "queue-uuid-here",
      "metrics": {
        "conversation.answered": { "count": 142 },
        "conversation.abandoned": { "count": 8 },
        "conversation.active": { "count": 12 }
      }
    }
  ],
  "pageSize": 20,
  "pageNumber": 1,
  "pageCount": 1,
  "total": 1
}

The 429 retry logic handles rate limit cascades. The function extracts the three core metrics required for service level approximation and pacing decisions.

Step 2: Calculate Optimal Dial Rate with PID Controller

A Proportional-Integral-Derivative controller adjusts the dial rate based on the error between the target service level and the measured service level. The controller requires anti-windup clamping and derivative filtering to prevent oscillation during peak hours.

import math
from typing import Optional

class PIDDialController:
    def __init__(self, kp: float, ki: float, kd: float, min_rate: float = 1.0, max_rate: float = 50.0) -> None:
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.min_rate = min_rate
        self.max_rate = max_rate
        self.previous_error: float = 0.0
        self.integral: float = 0.0
        self.last_time: Optional[float] = None

    def calculate(self, current_service_level: float, target_service_level: float, dt: float) -> float:
        error = target_service_level - current_service_level
        
        if self.last_time is None:
            self.last_time = time.time()
            return self.min_rate
            
        self.last_time = time.time()
        
        # Proportional term
        p_term = self.kp * error
        
        # Integral term with anti-windup
        self.integral += error * dt
        self.integral = max(-1000.0, min(1000.0, self.integral))
        i_term = self.ki * self.integral
        
        # Derivative term
        d_term = self.kd * ((error - self.previous_error) / dt)
        self.previous_error = error
        
        output = p_term + i_term + d_term
        return max(self.min_rate, min(self.max_rate, output))

The dt parameter represents the time delta between control loop iterations. Anti-windup clamps the integral term to prevent controller saturation when the campaign is temporarily paused or blocked by carrier restrictions. The output is strictly bounded between min_rate and max_rate to match Genesys Cloud pacing constraints.

Step 3: Update Campaign Pacing Parameters

The outbound campaign API accepts a PATCH request to modify pacing configuration. You must supply the campaign identifier and the calculated dial rate. The required scope is outbound:campaign:write. Pagination is required when discovering campaign identifiers via list endpoints.

import requests
from typing import List, Dict, Any

def list_campaigns(auth: GenesysAuth, search_term: str = "") -> List[str]:
    url = f"{GENESYS_BASE_URL}/api/v2/outbound/campaigns"
    headers = {"Authorization": f"Bearer {auth.get_token()}"}
    params = {"search": search_term, "pageSize": 25}
    campaign_ids: List[str] = []
    
    while True:
        response = requests.get(url, headers=headers, params=params)
        if response.status_code == 429:
            time.sleep(int(response.headers.get("Retry-After", 1)))
            continue
        response.raise_for_status()
        
        data = response.json()
        campaign_ids.extend([c["id"] for c in data.get("entities", [])])
        
        if data.get("nextPage"):
            params = {"pageSize": 25}
            url = data["nextPage"]
        else:
            break
            
    return campaign_ids

def update_campaign_pacing(auth: GenesysAuth, campaign_id: str, pacing_rate: float) -> Dict[str, Any]:
    url = f"{GENESYS_BASE_URL}/api/v2/outbound/campaigns/{campaign_id}"
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    payload = {"pacing": pacing_rate}
    
    response = requests.patch(url, json=payload, headers=headers)
    
    if response.status_code == 429:
        time.sleep(int(response.headers.get("Retry-After", 1)))
        return update_campaign_pacing(auth, campaign_id, pacing_rate)
        
    if response.status_code == 400:
        raise ValueError(f"Invalid pacing value: {pacing_rate}. Response: {response.text}")
        
    response.raise_for_status()
    return response.json()

The list_campaigns function demonstrates proper pagination handling using the nextPage URL returned by Genesys Cloud. The update_campaign_pacing function applies the PID output directly to the campaign pacing parameter. A 400 error indicates the calculated rate falls outside the platform validation range, which requires immediate clamping in the control loop.

Step 4: Orchestrate the Control Loop

The control loop ties authentication, metric collection, PID calculation, and parameter updates into a continuous execution cycle. You must handle token refresh, metric calculation, and pacing updates within a fixed interval.

import time
import logging

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)

def run_pacing_controller(
    auth: GenesysAuth,
    queue_id: str,
    campaign_id: str,
    target_sl: float = 0.85,
    interval_seconds: int = 30
) -> None:
    controller = PIDDialController(kp=0.5, ki=0.1, kd=0.2, min_rate=5.0, max_rate=40.0)
    
    while True:
        try:
            metrics = fetch_queue_metrics(auth, queue_id)
            total = metrics["total"]
            
            if total == 0:
                logger.warning("No conversations recorded. Maintaining minimum pacing.")
                current_sl = 1.0
            else:
                current_sl = metrics["answered"] / total
                
            pacing_rate = controller.calculate(current_sl, target_sl, interval_seconds)
            pacing_rate = round(pacing_rate, 2)
            
            logger.info(f"SL: {current_sl:.3f} | Target: {target_sl:.3f} | Setting pacing: {pacing_rate}")
            
            update_campaign_pacing(auth, campaign_id, pacing_rate)
            
        except requests.exceptions.HTTPError as e:
            logger.error(f"HTTP Error: {e.response.status_code} - {e.response.text}")
            if e.response.status_code == 403:
                raise PermissionError("Missing required OAuth scopes.") from e
        except Exception as e:
            logger.error(f"Control loop error: {e}")
            
        time.sleep(interval_seconds)

The loop calculates service level as answered divided by total answered plus abandoned. This proxy metric aligns with standard outbound pacing logic. The controller output is rounded to two decimal places to match Genesys Cloud pacing precision requirements.

Complete Working Example

The following script combines all components into a single executable module. Replace the environment variables with your Genesys Cloud credentials before execution.

import os
import time
import requests
import logging
from typing import Optional, Dict, Any, List

GENESYS_BASE_URL = os.environ.get("GENESYS_BASE_URL", "https://api.mypurecloud.com")
AUTH_URL = os.environ.get("GENESYS_AUTH_URL", "https://login.mypurecloud.com/oauth/token")
CLIENT_ID = os.environ["GENESYS_CLIENT_ID"]
CLIENT_SECRET = os.environ["GENESYS_CLIENT_SECRET"]

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)

class GenesysAuth:
    def __init__(self) -> None:
        self._token: str = ""
        self._expires_at: float = 0.0

    def get_token(self) -> str:
        if time.time() < self._expires_at:
            return self._token
        
        payload = {
            "grant_type": "client_credentials",
            "client_id": CLIENT_ID,
            "client_secret": CLIENT_SECRET
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        
        response = requests.post(AUTH_URL, data=payload, headers=headers)
        response.raise_for_status()
        
        data = response.json()
        self._token = data["access_token"]
        self._expires_at = time.time() + (data["expires_in"] * 0.9)
        return self._token

class PIDDialController:
    def __init__(self, kp: float, ki: float, kd: float, min_rate: float = 1.0, max_rate: float = 50.0) -> None:
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.min_rate = min_rate
        self.max_rate = max_rate
        self.previous_error: float = 0.0
        self.integral: float = 0.0
        self.last_time: Optional[float] = None

    def calculate(self, current_service_level: float, target_service_level: float, dt: float) -> float:
        error = target_service_level - current_service_level
        
        if self.last_time is None:
            self.last_time = time.time()
            return self.min_rate
            
        self.last_time = time.time()
        
        p_term = self.kp * error
        
        self.integral += error * dt
        self.integral = max(-1000.0, min(1000.0, self.integral))
        i_term = self.ki * self.integral
        
        d_term = self.kd * ((error - self.previous_error) / dt)
        self.previous_error = error
        
        output = p_term + i_term + d_term
        return max(self.min_rate, min(self.max_rate, output))

def fetch_queue_metrics(auth: GenesysAuth, queue_id: str) -> Dict[str, Any]:
    url = f"{GENESYS_BASE_URL}/api/v2/queues/stats/realtime"
    params = {
        "queueIds": queue_id,
        "metricNames": "conversation.answered,conversation.abandoned,conversation.active"
    }
    headers = {"Authorization": f"Bearer {auth.get_token()}"}
    
    response = requests.get(url, params=params, headers=headers)
    
    if response.status_code == 429:
        retry_after = int(response.headers.get("Retry-After", 1))
        time.sleep(retry_after)
        return fetch_queue_metrics(auth, queue_id)
        
    response.raise_for_status()
    data = response.json()
    
    if not data.get("entities"):
        raise ValueError("No queue entities returned. Verify queueId exists.")
        
    entity = data["entities"][0]
    metrics = entity.get("metrics", {})
    
    answered = metrics.get("conversation.answered", {}).get("count", 0)
    abandoned = metrics.get("conversation.abandoned", {}).get("count", 0)
    
    return {
        "answered": answered,
        "abandoned": abandoned,
        "total": answered + abandoned
    }

def update_campaign_pacing(auth: GenesysAuth, campaign_id: str, pacing_rate: float) -> Dict[str, Any]:
    url = f"{GENESYS_BASE_URL}/api/v2/outbound/campaigns/{campaign_id}"
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    payload = {"pacing": pacing_rate}
    
    response = requests.patch(url, json=payload, headers=headers)
    
    if response.status_code == 429:
        time.sleep(int(response.headers.get("Retry-After", 1)))
        return update_campaign_pacing(auth, campaign_id, pacing_rate)
        
    if response.status_code == 400:
        raise ValueError(f"Invalid pacing value: {pacing_rate}. Response: {response.text}")
        
    response.raise_for_status()
    return response.json()

def run_pacing_controller(
    auth: GenesysAuth,
    queue_id: str,
    campaign_id: str,
    target_sl: float = 0.85,
    interval_seconds: int = 30
) -> None:
    controller = PIDDialController(kp=0.5, ki=0.1, kd=0.2, min_rate=5.0, max_rate=40.0)
    
    while True:
        try:
            metrics = fetch_queue_metrics(auth, queue_id)
            total = metrics["total"]
            
            if total == 0:
                logger.warning("No conversations recorded. Maintaining minimum pacing.")
                current_sl = 1.0
            else:
                current_sl = metrics["answered"] / total
                
            pacing_rate = controller.calculate(current_sl, target_sl, interval_seconds)
            pacing_rate = round(pacing_rate, 2)
            
            logger.info(f"SL: {current_sl:.3f} | Target: {target_sl:.3f} | Setting pacing: {pacing_rate}")
            
            update_campaign_pacing(auth, campaign_id, pacing_rate)
            
        except requests.exceptions.HTTPError as e:
            logger.error(f"HTTP Error: {e.response.status_code} - {e.response.text}")
            if e.response.status_code == 403:
                raise PermissionError("Missing required OAuth scopes.") from e
        except Exception as e:
            logger.error(f"Control loop error: {e}")
            
        time.sleep(interval_seconds)

if __name__ == "__main__":
    auth = GenesysAuth()
    target_queue = os.environ["GENESYS_QUEUE_ID"]
    target_campaign = os.environ["GENESYS_CAMPAIGN_ID"]
    
    logger.info("Starting dynamic pacing controller...")
    run_pacing_controller(auth, target_queue, target_campaign, target_sl=0.85, interval_seconds=30)

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The access token has expired or the client credentials are invalid.
• How to fix it: Verify CLIENT_ID and CLIENT_SECRET match a confidential client in the Genesys Cloud admin console. Ensure the GenesysAuth class expiration buffer is active. Restart the script if credentials were rotated.
• Code showing the fix: The get_token method automatically refreshes when time.time() >= self._expires_at. No manual intervention is required during execution.

Error: 403 Forbidden

• What causes it: The OAuth client lacks required scopes for the requested endpoint.
• How to fix it: Navigate to the Genesys Cloud admin console, locate the OAuth client, and add analytics:query:read, outbound:campaign:read, and outbound:campaign:write. Regenerate the client secret if the client was modified.
• Code showing the fix: The run_pacing_controller function explicitly catches 403 responses and raises a descriptive PermissionError to halt execution safely.

Error: 429 Too Many Requests

• What causes it: The control loop exceeds Genesys Cloud rate limits for the tenant or API endpoint.
• How to fix it: Implement exponential backoff or respect the Retry-After header. Increase the interval_seconds parameter in the control loop.
• Code showing the fix: Both fetch_queue_metrics and update_campaign_pacing check for 429 status codes, extract the Retry-After header, sleep, and recursively retry the request.

Error: 400 Bad Request on Pacing Update

• What causes it: The calculated pacing rate falls outside the valid range for the campaign or violates platform constraints.
• How to fix it: Adjust the min_rate and max_rate parameters in the PIDDialController constructor. Ensure the output is rounded to two decimal places.
• Code showing the fix: The controller clamps output between min_rate and max_rate. The update_campaign_pacing function catches 400 responses and logs the exact validation failure from the response body.

Official References

• Genesys Cloud OAuth 2.0 Authentication
• Real-Time Queue Statistics API
• Outbound Campaigns API
• Analytics Conversations Query API