Distributing Genesys Cloud Quality Evaluation Assignments via Python SDK

What You Will Build

• A Python distributor script that programmatically assigns Quality evaluations to raters while enforcing load limits and bias mitigation rules.
• The implementation uses the Genesys Cloud Quality API (/api/v2/quality/evaluations), User API, and Webhook API.
• The tutorial covers Python 3.9+ with requests, pydantic, and standard library modules for production-grade execution.

Prerequisites

• OAuth 2.0 confidential client with scopes: quality:evaluation:write, user:read:all, webhook:write, quality:evaluation:read
• Genesys Cloud environment URL (e.g., usw2.mygen.com)
• Python 3.9+ runtime
• External dependencies: requests>=2.31.0, pydantic>=2.5.0, uuid, time, json, logging

Authentication Setup

Genesys Cloud uses standard OAuth 2.0 client credentials flow. The authentication manager caches tokens and refreshes them sixty seconds before expiration to prevent mid-request 401 failures.

import requests
import time
from typing import Dict, Optional

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

    def get_access_token(self) -> str:
        if self.access_token and time.time() < self.expires_at - 60:
            return self.access_token
        
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        
        response = requests.post(self.token_url, data=payload, headers=headers)
        response.raise_for_status()
        
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.expires_at = time.time() + token_data["expires_in"]
        return self.access_token

The token manager returns a valid bearer string. You pass this string in the Authorization header for all subsequent API calls. The sixty-second buffer prevents edge-case expiration during long-running distribution batches.

Implementation

Step 1: Rater Availability Checking and Load Calculation

The Quality engine enforces maximum assignment limits per rater. You must query the current assignment count before distributing new evaluations. The User API returns raters in paginated batches. You filter by division and role, then calculate available capacity.

import requests
from typing import List, Dict, Any

class RaterLoadCalculator:
    def __init__(self, base_url: str, auth_manager: GenesysAuthManager):
        self.base_url = base_url
        self.auth = auth_manager
        self.max_assignments_per_rater = 15

    def fetch_raters(self, division_id: str, role_id: str) -> List[Dict[str, Any]]:
        raters = []
        page_size = 50
        cursor = None
        
        while True:
            params = {
                "pageSize": page_size,
                "division.id": division_id,
                "roles": role_id
            }
            if cursor:
                params["cursorId"] = cursor
                
            headers = {"Authorization": f"Bearer {self.auth.get_access_token()}"}
            response = requests.get(
                f"{self.base_url}/api/v2/users", 
                headers=headers, 
                params=params
            )
            response.raise_for_status()
            
            data = response.json()
            raters.extend(data["entities"])
            cursor = data.get("nextPageCursor")
            if not cursor:
                break
        return raters

    def get_current_load(self, user_id: str) -> int:
        headers = {"Authorization": f"Bearer {self.auth.get_access_token()}"}
        params = {
            "assigneeId": user_id,
            "status": "in-progress",
            "pageSize": 1
        }
        response = requests.get(
            f"{self.base_url}/api/v2/quality/evaluations",
            headers=headers,
            params=params
        )
        response.raise_for_status()
        return response.json().get("totalCount", 0)

    def calculate_available_capacity(self, raters: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        available_raters = []
        for rater in raters:
            current_load = self.get_current_load(rater["id"])
            remaining_capacity = self.max_assignments_per_rater - current_load
            if remaining_capacity > 0:
                available_raters.append({
                    "id": rater["id"],
                    "name": rater["name"],
                    "remaining_capacity": remaining_capacity,
                    "bias_weight": 1.0  # Default weight for round-robin distribution
                })
        return available_raters

The pagination loop respects the nextPageCursor field. The load calculation queries only in-progress evaluations to avoid counting completed or expired assignments. You adjust max_assignments_per_rater based on your quality program throughput targets.

Step 2: Distribute Payload Construction and Schema Validation

Quality evaluation payloads require strict schema compliance. You validate the payload using Pydantic before transmission. The validator enforces UUID formats, weight boundaries, and mandatory field presence. Bias mitigation occurs here by rotating rater selection based on remaining capacity and weight directives.

from pydantic import BaseModel, Field, field_validator
from typing import Optional
import uuid
from datetime import datetime, timedelta

class EvaluationAssignmentPayload(BaseModel):
    evaluation_form_id: str = Field(alias="evaluationFormId")
    conversation_id: str = Field(alias="conversationId")
    assignee_id: str = Field(alias="assigneeId")
    weight: float = Field(default=1.0, ge=0.1, le=1.0)
    due_date: Optional[str] = Field(default=None, alias="dueDate")

    @field_validator("evaluation_form_id", "conversation_id", "assignee_id")
    @classmethod
    def validate_uuid(cls, v: str) -> str:
        try:
            uuid.UUID(v)
        except ValueError:
            raise ValueError("Field must be a valid UUID string")
        return v

    @field_validator("due_date")
    @classmethod
    def validate_due_date(cls, v: Optional[str]) -> Optional[str]:
        if v:
            try:
                datetime.fromisoformat(v.replace("Z", "+00:00"))
            except ValueError:
                raise ValueError("dueDate must be ISO 8601 format")
        return v

    def model_dump_genesis(self) -> dict:
        return self.model_dump(by_alias=True, exclude_none=True)

The weight directive scales between 0.1 and 1.0. Values outside this range trigger a validation error before the HTTP request reaches the Quality engine. The model_dump_genesis method produces the exact JSON structure the API expects.

Step 3: Atomic POST Operations with Conflict Avoidance

The Quality API returns 429 when rate limits are exceeded and 409 when duplicate assignments exist. You implement exponential backoff for 429 responses and idempotency checks for 409 responses. Latency tracking captures request duration for performance auditing.

import time
import logging
from typing import Dict, Any, Tuple

logger = logging.getLogger("quality_distributor")

class EvaluationDistributor:
    def __init__(self, base_url: str, auth_manager: GenesysAuthManager):
        self.base_url = base_url
        self.auth = auth_manager
        self.success_count = 0
        self.failure_count = 0
        self.total_latency = 0.0

    def distribute(self, payload: EvaluationAssignmentPayload) -> Tuple[Dict[str, Any], float]:
        headers = {
            "Authorization": f"Bearer {self.auth.get_access_token()}",
            "Content-Type": "application/json"
        }
        body = payload.model_dump_genesis()
        
        start_time = time.perf_counter()
        max_retries = 4
        
        for attempt in range(max_retries):
            response = requests.post(
                f"{self.base_url}/api/v2/quality/evaluations",
                headers=headers,
                json=body
            )
            
            latency = time.perf_counter() - start_time
            self.total_latency += latency
            
            if response.status_code == 201:
                self.success_count += 1
                logger.info("Assignment created successfully. Assignee: %s", payload.assignee_id)
                return response.json(), latency
            elif response.status_code == 429:
                wait_time = (2 ** attempt) + 0.5
                logger.warning("Rate limited (429). Retrying in %.2f seconds.", wait_time)
                time.sleep(wait_time)
                start_time = time.perf_counter()  # Reset latency timer for next attempt
                continue
            elif response.status_code == 409:
                logger.warning("Conflict (409). Assignment already exists for conversation %s.", payload.conversation_id)
                self.failure_count += 1
                return {"error": "DUPLICATE_ASSIGNMENT"}, latency
            elif response.status_code == 400:
                logger.error("Bad Request (400). Payload validation failed: %s", response.text)
                self.failure_count += 1
                return {"error": "SCHEMA_VIOLATION", "details": response.text}, latency
            else:
                response.raise_for_status()
                
        logger.error("Max retries exceeded for evaluation distribution.")
        self.failure_count += 1
        return {"error": "MAX_RETRIES_EXCEEDED"}, latency

The retry loop resets the latency timer on each 429 attempt to measure actual network cost. The 409 handler treats duplicates as soft failures to prevent pipeline halts. You track success and failure counts for downstream reporting.

Step 4: Webhook Synchronization and Audit Logging

External performance systems require real-time synchronization. You register a webhook on evaluation assignment events. The audit logger records distribution metrics, rater selection decisions, and payload hashes for governance compliance.

import hashlib
import json
from datetime import datetime

class WebhookSyncManager:
    def __init__(self, base_url: str, auth_manager: GenesysAuthManager):
        self.base_url = base_url
        self.auth = auth_manager

    def register_assignment_webhook(self, target_url: str, webhook_id: str) -> Dict[str, Any]:
        headers = {
            "Authorization": f"Bearer {self.auth.get_access_token()}",
            "Content-Type": "application/json"
        }
        payload = {
            "id": webhook_id,
            "name": "Quality Assignment Sync",
            "targetUrl": target_url,
            "apiVersion": "v2",
            "event": "quality.evaluations.assigned",
            "authScheme": "basic",
            "authUsername": "webhook_user",
            "authPassword": "secure_password_placeholder",
            "retryIntervalSeconds": 30,
            "maxRetryCount": 5,
            "divisionId": "default"
        }
        response = requests.post(
            f"{self.base_url}/api/v2/webhooks",
            headers=headers,
            json=payload
        )
        response.raise_for_status()
        return response.json()

class AuditLogger:
    def __init__(self, log_file: str = "quality_distribution_audit.log"):
        self.log_file = log_file

    def log_distribution_event(self, payload_hash: str, assignee_id: str, latency: float, status: str) -> None:
        timestamp = datetime.utcnow().isoformat() + "Z"
        log_entry = {
            "timestamp": timestamp,
            "payload_hash": payload_hash,
            "assignee_id": assignee_id,
            "latency_seconds": round(latency, 4),
            "status": status
        }
        with open(self.log_file, "a") as f:
            f.write(json.dumps(log_entry) + "\n")
        logger.info("Audit log written: %s", json.dumps(log_entry))

    def record_metrics(self, success_count: int, failure_count: int, total_latency: float) -> Dict[str, Any]:
        total_attempts = success_count + failure_count
        success_rate = (success_count / total_attempts * 100) if total_attempts > 0 else 0.0
        avg_latency = (total_latency / total_attempts) if total_attempts > 0 else 0.0
        metrics = {
            "success_count": success_count,
            "failure_count": failure_count,
            "success_rate_percent": round(success_rate, 2),
            "average_latency_seconds": round(avg_latency, 4)
        }
        logger.info("Distribution metrics: %s", json.dumps(metrics))
        return metrics

The webhook targets external systems with basic authentication and exponential retry. The audit logger writes JSON lines to a file for SIEM ingestion. You calculate success rates and average latency after batch execution.

Complete Working Example

The following script combines all components into a runnable distribution pipeline. Replace placeholder credentials and IDs before execution.

import os
import uuid
import logging
from typing import List, Dict, Any

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

def main():
    # Configuration
    ENVIRONMENT = os.getenv("GENESYS_ENV", "usw2.mygen.com")
    CLIENT_ID = os.getenv("GENESYS_CLIENT_ID")
    CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET")
    DIVISION_ID = "default"
    RATER_ROLE_ID = "QualityRater"
    WEBHOOK_TARGET = "https://your-performance-system.example.com/api/v1/webhooks/quality"
    EVALUATION_FORM_ID = "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    CONVERSATION_IDS = [
        "conv-001-uuid-placeholder-1111",
        "conv-002-uuid-placeholder-2222",
        "conv-003-uuid-placeholder-3333"
    ]

    if not CLIENT_ID or not CLIENT_SECRET:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set")

    # Initialize managers
    auth = GenesysAuthManager(ENVIRONMENT, CLIENT_ID, CLIENT_SECRET)
    calculator = RaterLoadCalculator(f"https://{ENVIRONMENT}", auth)
    distributor = EvaluationDistributor(f"https://{ENVIRONMENT}", auth)
    webhook_sync = WebhookSyncManager(f"https://{ENVIRONMENT}", auth)
    auditor = AuditLogger()

    # Step 1: Fetch and validate raters
    logger.info("Fetching rater matrix...")
    raters = calculator.fetch_raters(DIVISION_ID, RATER_ROLE_ID)
    available_raters = calculator.calculate_available_capacity(raters)
    
    if not available_raters:
        raise RuntimeError("No raters available with remaining capacity")
    
    logger.info("Available raters: %d", len(available_raters))

    # Step 2: Register webhook for external sync
    webhook_id = str(uuid.uuid4())
    try:
        webhook_sync.register_assignment_webhook(WEBHOOK_TARGET, webhook_id)
        logger.info("Webhook registered: %s", webhook_id)
    except Exception as e:
        logger.warning("Webhook registration failed, continuing without sync: %s", str(e))

    # Step 3: Distribute evaluations with bias mitigation (round-robin)
    rater_index = 0
    for conversation_id in CONVERSATION_IDS:
        if rater_index >= len(available_raters):
            rater_index = 0
        
        selected_rater = available_raters[rater_index]
        
        # Construct payload
        assignment = EvaluationAssignmentPayload(
            evaluationFormId=EVALUATION_FORM_ID,
            conversationId=conversation_id,
            assigneeId=selected_rater["id"],
            weight=1.0,
            dueDate=(datetime.utcnow() + timedelta(days=7)).isoformat() + "Z"
        )
        
        # Calculate payload hash for audit
        payload_json = json.dumps(assignment.model_dump_genesis(), sort_keys=True)
        payload_hash = hashlib.sha256(payload_json.encode()).hexdigest()
        
        # Distribute
        result, latency = distributor.distribute(assignment)
        status = "SUCCESS" if "error" not in result else "FAILED"
        
        # Audit
        auditor.log_distribution_event(payload_hash, selected_rater["id"], latency, status)
        
        # Rotate rater for bias mitigation
        if status == "SUCCESS":
            selected_rater["remaining_capacity"] -= 1
            if selected_rater["remaining_capacity"] <= 0:
                available_raters.remove(selected_rater)
                if rater_index >= len(available_raters):
                    rater_index = 0
            else:
                rater_index += 1
        else:
            rater_index += 1

    # Step 4: Final metrics
    metrics = auditor.record_metrics(distributor.success_count, distributor.failure_count, distributor.total_latency)
    logger.info("Distribution complete. Metrics: %s", json.dumps(metrics))

if __name__ == "__main__":
    main()

The script fetches raters, calculates capacity, registers a webhook, distributes evaluations using round-robin rotation, and logs every action. You adjust CONVERSATION_IDS and EVALUATION_FORM_ID to match your environment. The rater rotation prevents bias concentration and respects load limits.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or invalid client credentials.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET. Ensure the token manager refreshes tokens before expiration. Check that the confidential client has quality:evaluation:write scope.
• Code Fix: The GenesysAuthManager already implements automatic refresh. Add explicit token validation before batch execution if running in long-lived processes.

Error: 403 Forbidden

• Cause: Missing OAuth scopes or insufficient user permissions.
• Fix: Grant quality:evaluation:write, user:read:all, and webhook:write to the OAuth client. Ensure the client credentials grant access to the target division.
• Code Fix: Log the exact response body on 403 to identify the missing scope. Cross-reference with the Genesys Cloud OAuth scope matrix.

Error: 429 Too Many Requests

• Cause: Exceeding Quality API rate limits (typically 100 requests per minute per client).
• Fix: The distributor implements exponential backoff. Reduce batch size or add a fixed delay between iterations if scaling to thousands of evaluations.
• Code Fix: Adjust max_retries and wait_time multiplier in EvaluationDistributor.distribute. Monitor Retry-After header if provided.

Error: 400 Bad Request

• Cause: Invalid UUID format, weight out of bounds, or missing mandatory fields.
• Fix: Pydantic validation catches schema violations before transmission. Verify evaluationFormId and conversationId exist in your environment. Ensure weight stays between 0.1 and 1.0.
• Code Fix: Check audit logs for SCHEMA_VIOLATION entries. Validate source data before constructing EvaluationAssignmentPayload.

Error: 409 Conflict

• Cause: Duplicate assignment for the same conversation and rater.
• Fix: The Quality engine prevents duplicate assignments. The distributor treats 409 as a soft failure and continues.
• Code Fix: Implement idempotency keys in upstream systems to prevent duplicate submission. The current handler logs the conflict and increments failure count without halting.

Official References

• Quality Evaluation API Reference
• User API Reference
• Webhook API Reference
• OAuth 2.0 Authentication Guide
• Python SDK Documentation