Generating Genesys Cloud Routing Strategy Simulations via Python SDK

What You Will Build

• This tutorial builds a production-grade Python module that submits routing strategy simulations to Genesys Cloud, validates input constraints, polls for completion, and caches results.
• The code uses the official Genesys Cloud Python SDK v2.x and the /api/v2/routing/simulations endpoint surface.
• All examples are written in Python 3.9+ with strict type hints, explicit error handling, and deterministic retry logic.

Prerequisites

• OAuth confidential client registered in Genesys Cloud with grant type client_credentials
• Required OAuth scopes: routing:simulation:write, routing:simulation:read, routing:queue:read, routing:strategy:read
• Python 3.9 or higher
• Dependencies: pip install genesyscloud==2.10.0 python-dateutil pytz
• A valid routing strategy ID and queue ID from your Genesys Cloud organization

Authentication Setup

The Genesys Cloud Python SDK handles OAuth 2.0 client credentials flow internally. You must initialize the platform client with your environment, client ID, and client secret. The SDK caches tokens and refreshes them automatically before expiration.

from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.environment import Environment
from typing import Optional

def init_genesys_client(
    client_id: str,
    client_secret: str,
    environment: str = "mypurecloud.com"
) -> PureCloudPlatformClientV2:
    """Initialize and return an authenticated Genesys Cloud platform client."""
    platform_client = PureCloudPlatformClientV2(
        environment=Environment(environment),
        client_id=client_id,
        client_secret=client_secret
    )
    
    # Verify authentication by fetching a lightweight endpoint
    try:
        platform_client.platform_api.get_platform_version()
    except Exception as e:
        raise RuntimeError(f"Authentication failed: {str(e)}") from e
        
    return platform_client

Implementation

Step 1: Validate Simulation Constraints and Resource Conflicts

Genesys Cloud enforces strict boundaries on simulation duration, agent capacity, and load matrix structure. Invalid payloads return 400 Bad Request. You must validate inputs before submission to prevent wasted API calls and rate limit consumption.

import datetime
import re
from typing import List, Dict, Any
from pydantic import BaseModel, Field, validator

class LoadMatrixEntry(BaseModel):
    start_time: str = Field(..., pattern=r"^([01]\d|2[0-3]):[0-5]\d$")
    end_time: str = Field(..., pattern=r"^([01]\d|2[0-3]):[0-5]\d$")
    call_volume: int = Field(..., ge=0, le=100000)
    agent_count: int = Field(..., ge=1, le=5000)
    avg_handle_time_seconds: float = Field(..., gt=0, le=3600)

class SimulationRequest(BaseModel):
    strategy_id: str
    queue_id: str
    duration_hours: int = Field(..., ge=1, le=24)
    start_date: datetime.date
    load_matrix: List[LoadMatrixEntry]
    
    @validator("duration_hours")
    def validate_max_duration(cls, v):
        if v > 24:
            raise ValueError("Genesys Cloud routing simulations are capped at 24 hours.")
        return v

    @validator("load_matrix")
    def validate_load_matrix(cls, v):
        if not v:
            raise ValueError("Load matrix must contain at least one entry.")
        for entry in v:
            if entry.start_time >= entry.end_time:
                raise ValueError("Start time must precede end time in load matrix.")
        return v

def verify_resource_conflicts(
    platform_client: PureCloudPlatformClientV2,
    queue_id: str,
    strategy_id: str
) -> bool:
    """Check that the queue and strategy exist and are accessible."""
    from genesyscloud.routing.api.routing_api import RoutingApi
    
    routing_api = RoutingApi(platform_client)
    
    try:
        routing_api.get_routing_queues_queue_id(queue_id=queue_id)
    except Exception as e:
        raise RuntimeError(f"Queue {queue_id} not found or inaccessible: {str(e)}") from e
        
    try:
        routing_api.get_routing_strategies_strategy_id(strategy_id=strategy_id)
    except Exception as e:
        raise RuntimeError(f"Strategy {strategy_id} not found or inaccessible: {str(e)}") from e
        
    return True

Step 2: Construct Simulation Payload and Execute Atomic POST

The simulation endpoint accepts a structured request body. You must map your validated model to the SDK’s PostRoutingSimulationsRequest. The SDK serializes the object to JSON and handles Content-Type negotiation. You must implement retry logic for 429 Too Many Requests to avoid cascading failures.

import time
import logging
from genesyscloud.routing.model.post_routing_simulations_request import PostRoutingSimulationsRequest
from genesyscloud.routing.model.simulation_load_matrix import SimulationLoadMatrix
from typing import Dict, Any

logger = logging.getLogger(__name__)

def build_simulation_request(
    strategy_id: str,
    queue_id: str,
    duration_hours: int,
    start_date: datetime.date,
    load_matrix: List[LoadMatrixEntry]
) -> PostRoutingSimulationsRequest:
    """Map validated inputs to the SDK simulation request model."""
    iso_start = start_date.isoformat()
    iso_end = (start_date + datetime.timedelta(hours=duration_hours)).isoformat()
    
    sdk_matrix = [
        SimulationLoadMatrix(
            start_time=entry.start_time,
            end_time=entry.end_time,
            call_volume=entry.call_volume,
            agent_count=entry.agent_count,
            avg_handle_time=entry.avg_handle_time_seconds
        )
        for entry in load_matrix
    ]
    
    return PostRoutingSimulationsRequest(
        strategy_id=strategy_id,
        queue_id=queue_id,
        start_time=iso_start,
        end_time=iso_end,
        load_matrix=sdk_matrix
    )

def post_simulation_with_retry(
    platform_client: PureCloudPlatformClientV2,
    request: PostRoutingSimulationsRequest,
    max_retries: int = 3,
    base_delay: float = 2.0
) -> Dict[str, Any]:
    """Submit simulation with exponential backoff for 429 responses."""
    from genesyscloud.platform.client import ApiClientException
    
    routing_api = platform_client.routing_api
    attempt = 0
    
    while attempt < max_retries:
        try:
            response = routing_api.post_routing_simulations(body=request)
            logger.info("Simulation submitted successfully. ID: %s", response.id)
            return response.to_dict()
        except ApiClientException as e:
            if e.status == 429:
                delay = base_delay * (2 ** attempt)
                logger.warning("Rate limited (429). Retrying in %.1fs...", delay)
                time.sleep(delay)
                attempt += 1
            elif e.status in (401, 403):
                raise RuntimeError(f"Authentication/Authorization failed: {e.status} {e.reason}") from e
            else:
                raise RuntimeError(f"API error {e.status}: {e.reason}") from e
                
    raise RuntimeError("Max retries exceeded for simulation submission.")

Step 3: Poll for Completion and Process Results

Simulations run asynchronously. You must poll the simulation status endpoint until it transitions to COMPLETED or FAILED. Each poll must respect rate limits and track latency. You will also extract queue wait time projections and agent capacity utilization from the result payload.

def poll_simulation_results(
    platform_client: PureCloudPlatformClientV2,
    simulation_id: str,
    poll_interval: float = 15.0,
    max_wait_seconds: float = 3600.0
) -> Dict[str, Any]:
    """Poll simulation status until completion or timeout."""
    import time
    from datetime import datetime, timezone
    
    routing_api = platform_client.routing_api
    start_time = time.time()
    
    while (time.time() - start_time) < max_wait_seconds:
        try:
            sim = routing_api.get_routing_simulations_simulation_id(
                simulation_id=simulation_id
            )
        except Exception as e:
            raise RuntimeError(f"Failed to fetch simulation status: {str(e)}") from e
            
        status = sim.status
        logger.debug("Simulation status: %s", status)
        
        if status == "COMPLETED":
            latency = time.time() - start_time
            logger.info("Simulation completed in %.2f seconds.", latency)
            return {
                "result": sim.result.to_dict() if sim.result else {},
                "latency_seconds": latency,
                "status": status,
                "completed_at": datetime.now(timezone.utc).isoformat()
            }
        elif status == "FAILED":
            raise RuntimeError(f"Simulation failed. Reason: {sim.failure_reason}")
            
        time.sleep(poll_interval)
        
    raise TimeoutError("Simulation exceeded maximum wait time.")

Step 4: Cache Results, Track Metrics, and Generate Audit Logs

You will store simulation results in a local JSON cache to prevent redundant API calls. You will also append structured audit logs for routing governance and track success rates for efficiency monitoring.

import json
import os
from pathlib import Path
from datetime import datetime, timezone

CACHE_DIR = Path("simulation_cache")
AUDIT_LOG = Path("simulation_audit.log")

def ensure_dirs():
    CACHE_DIR.mkdir(exist_ok=True)

def cache_simulation_result(simulation_id: str, result_data: Dict[str, Any]) -> None:
    """Persist simulation output to disk for safe iteration."""
    cache_file = CACHE_DIR / f"{simulation_id}.json"
    with open(cache_file, "w") as f:
        json.dump(result_data, f, indent=2)
    logger.info("Cached simulation result for %s", simulation_id)

def write_audit_log(
    simulation_id: str,
    strategy_id: str,
    queue_id: str,
    status: str,
    latency: float,
    error: Optional[str] = None
) -> None:
    """Append structured audit entry for routing governance."""
    timestamp = datetime.now(timezone.utc).isoformat()
    log_entry = {
        "timestamp": timestamp,
        "simulation_id": simulation_id,
        "strategy_id": strategy_id,
        "queue_id": queue_id,
        "status": status,
        "latency_seconds": round(latency, 3),
        "error": error
    }
    with open(AUDIT_LOG, "a") as f:
        f.write(json.dumps(log_entry) + "\n")
    logger.info("Audit log written for %s", simulation_id)

def calculate_success_rate(log_path: Path) -> float:
    """Compute historical simulation success rate from audit logs."""
    if not log_path.exists():
        return 0.0
        
    total = 0
    successes = 0
    
    with open(log_path, "r") as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                entry = json.loads(line)
                total += 1
                if entry.get("status") == "COMPLETED":
                    successes += 1
            except json.JSONDecodeError:
                continue
                
    return (successes / total) * 100 if total > 0 else 0.0

Complete Working Example

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

import logging
import sys
import os
from datetime import date, timedelta
from typing import Optional

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    datefmt="%Y-%m-%dT%H:%M:%S%z"
)
logger = logging.getLogger(__name__)

# Import components from previous steps
from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.environment import Environment
from genesyscloud.routing.model.post_routing_simulations_request import PostRoutingSimulationsRequest
from genesyscloud.routing.model.simulation_load_matrix import SimulationLoadMatrix
from pydantic import BaseModel, Field, validator
import datetime
import time
import json
from pathlib import Path

# Reuse models and functions defined in Steps 1-4
# (In production, import from separate modules)

def run_simulation_workflow(
    client_id: str,
    client_secret: str,
    strategy_id: str,
    queue_id: str,
    duration_hours: int,
    start_date: date,
    load_matrix: list
) -> dict:
    """End-to-end routing simulation execution pipeline."""
    ensure_dirs()
    
    # 1. Authentication
    platform_client = init_genesys_client(client_id, client_secret)
    
    # 2. Validation
    validated_request = SimulationRequest(
        strategy_id=strategy_id,
        queue_id=queue_id,
        duration_hours=duration_hours,
        start_date=start_date,
        load_matrix=load_matrix
    )
    
    verify_resource_conflicts(platform_client, queue_id, strategy_id)
    
    # 3. Build and Submit
    sdk_request = build_simulation_request(
        strategy_id, queue_id, duration_hours, start_date, load_matrix
    )
    
    sim_response = post_simulation_with_retry(platform_client, sdk_request)
    simulation_id = sim_response["id"]
    
    # 4. Poll and Process
    try:
        result_data = poll_simulation_results(platform_client, simulation_id)
        cache_simulation_result(simulation_id, result_data)
        write_audit_log(
            simulation_id=simulation_id,
            strategy_id=strategy_id,
            queue_id=queue_id,
            status="COMPLETED",
            latency=result_data["latency_seconds"]
        )
        return result_data
    except Exception as e:
        latency = time.time() - time.time()  # Fallback tracking
        write_audit_log(
            simulation_id=simulation_id,
            strategy_id=strategy_id,
            queue_id=queue_id,
            status="FAILED",
            latency=0.0,
            error=str(e)
        )
        raise

if __name__ == "__main__":
    # Replace with actual credentials
    CLIENT_ID = os.getenv("GENESYS_CLIENT_ID", "")
    CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET", "")
    STRATEGY_ID = os.getenv("GENESYS_STRATEGY_ID", "")
    QUEUE_ID = os.getenv("GENESYS_QUEUE_ID", "")
    
    if not all([CLIENT_ID, CLIENT_SECRET, STRATEGY_ID, QUEUE_ID]):
        logger.error("Missing required environment variables.")
        sys.exit(1)
        
    sample_load = [
        {"start_time": "08:00", "end_time": "12:00", "call_volume": 150, "agent_count": 12, "avg_handle_time_seconds": 180},
        {"start_time": "12:00", "end_time": "17:00", "call_volume": 200, "agent_count": 15, "avg_handle_time_seconds": 150}
    ]
    
    try:
        output = run_simulation_workflow(
            client_id=CLIENT_ID,
            client_secret=CLIENT_SECRET,
            strategy_id=STRATEGY_ID,
            queue_id=QUEUE_ID,
            duration_hours=8,
            start_date=date.today() + timedelta(days=1),
            load_matrix=sample_load
        )
        logger.info("Workflow completed. Success rate: %.2f%%", calculate_success_rate(Path("simulation_audit.log")))
    except Exception as e:
        logger.error("Workflow failed: %s", str(e))
        sys.exit(1)

Common Errors & Debugging

Error: 400 Bad Request (Invalid Load Matrix or Duration)

• What causes it: The simulation engine rejects payloads where start_time exceeds end_time, avg_handle_time is zero or negative, or duration_hours exceeds the 24-hour platform limit.
• How to fix it: Run inputs through the SimulationRequest Pydantic model before submission. Validate time formats with regex and enforce numeric bounds.
• Code showing the fix: The SimulationRequest validator in Step 1 explicitly checks duration_hours <= 24 and enforces start_time < end_time.

Error: 409 Conflict (Simulation Already Running or Resource Locked)

• What causes it: Genesys Cloud prevents overlapping simulations for the same strategy/queue pair to preserve compute resources.
• How to fix it: Query existing simulations via get_routing_simulations before submission. Filter by strategy_id and status in QUEUED or RUNNING. Wait for completion or use a distinct strategy ID.
• Code showing the fix: Implement a pre-flight check that lists active simulations and blocks submission if a match exists.

Error: 429 Too Many Requests (Rate Limit Exceeded)

• What causes it: Routing simulation endpoints enforce strict per-minute request quotas. Rapid polling or concurrent submissions trigger throttling.
• How to fix it: Implement exponential backoff with jitter. The post_simulation_with_retry function handles this automatically. Increase poll_interval to 30 seconds for long-running jobs.
• Code showing the fix: The retry loop in Step 2 captures e.status == 429, calculates delay = base_delay * (2 ** attempt), and sleeps before retrying.

Error: 500 Internal Server Error (Simulation Engine Timeout)

• What causes it: Complex load matrices with high agent counts or extreme volume spikes can exceed the simulation engine’s memory allocation.
• How to fix it: Reduce agent_count and call_volume to realistic production baselines. Split multi-day simulations into separate 8-hour blocks. Verify queue routing rules do not contain circular dependencies.
• Code showing the fix: Adjust max_wait_seconds in poll_simulation_results and monitor sim.failure_reason for engine-specific diagnostics.

Official References

• Genesys Cloud Routing Simulations API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow
• Routing Strategy and Queue Management