Provisioning Genesys Cloud Predictive Engagement Campaign Schedules via REST API with Python
What You Will Build
A production-ready Python module that constructs, validates, and provisions Genesys Cloud outbound campaign schedules using the REST API. The module implements asynchronous job orchestration, overlap detection, priority ranking, external webhook synchronization, and operational telemetry. This tutorial uses the Genesys Cloud Outbound API and standard HTTP clients. The code is written in Python using httpx, pydantic, and asyncio.
Prerequisites
- OAuth 2.0 Client Credentials flow with scopes:
outbound:campaign:read,outbound:campaign:write,outbound:segment:read,outbound:contactlist:read,job:read,job:write - Genesys Cloud REST API v2
- Python 3.9+
- External dependencies:
pip install httpx pydantic python-dateutil
Authentication Setup
Genesys Cloud requires OAuth 2.0 bearer tokens for all API calls. The client credentials flow exchanges a client ID and secret for an access token. The token expires after one hour and requires rotation before expiration.
import httpx
import asyncio
from typing import Optional
import time
class GenesysAuth:
def __init__(self, client_id: str, client_secret: str, base_url: str):
self.client_id = client_id
self.client_secret = client_secret
self.base_url = base_url.rstrip("/")
self.token: Optional[str] = None
self.token_expiry: float = 0
async def get_token(self) -> str:
if self.token and time.time() < self.token_expiry - 300:
return self.token
async with httpx.AsyncClient(timeout=10) as client:
response = await client.post(
f"{self.base_url}/oauth/token",
data={
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret
}
)
response.raise_for_status()
payload = response.json()
self.token = payload["access_token"]
self.token_expiry = time.time() + payload["expires_in"]
return self.token
async def get_headers(self) -> dict:
token = await self.get_token()
return {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
}
Expected response body from /oauth/token:
{
"access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
"expires_in": 3600,
"scope": "outbound:campaign:read outbound:campaign:write outbound:segment:read",
"token_type": "Bearer"
}
Implementation
Step 1: Schedule Payload Construction & Schema Validation
The schedule payload must reference a valid audience segment, define a delivery window matrix, and specify throttle rate directives. Pydantic enforces strict schema validation before transmission.
Required OAuth scope: outbound:campaign:write
from pydantic import BaseModel, Field, field_validator
from datetime import datetime
from typing import Optional
class DeliveryWindow(BaseModel):
start_time: str = Field(pattern=r"^\d{2}:\d{2}:\d{2}$")
end_time: str = Field(pattern=r"^\d{2}:\d{2}:\d{2}$")
time_zone: str = Field(pattern=r"^[A-Za-z_]+$")
class SchedulePayload(BaseModel):
segment_id: str
delivery_window: DeliveryWindow
throttle_rate: int = Field(ge=1, le=10000)
schedule_type: str = "PREDICTIVE"
start_date_time: datetime
end_date_time: datetime
priority: int = Field(ge=1, le=10)
@field_validator("start_date_time", "end_date_time")
@classmethod
def validate_dates(cls, v: datetime) -> datetime:
if v.tzinfo is None:
raise ValueError("Datetime must include timezone information.")
return v
@field_validator("end_date_time")
@classmethod
def validate_duration(cls, v: datetime, info) -> datetime:
start = info.data.get("start_date_time")
if start and v <= start:
raise ValueError("End date must be after start date.")
return v
Step 2: Overlap Detection & Priority Ranking Algorithm
Predictive campaigns require strict schedule isolation to prevent audience fatigue and contact capacity violations. This step fetches existing schedules for the campaign, detects temporal overlaps, and ranks incoming schedules by priority and segment size.
Required OAuth scope: outbound:campaign:read
import logging
from dateutil import parser as date_parser
logger = logging.getLogger(__name__)
class ScheduleValidator:
def __init__(self, client: httpx.AsyncClient, headers: dict, base_url: str):
self.client = client
self.headers = headers
self.base_url = base_url
async def fetch_existing_schedules(self, campaign_id: str) -> list:
schedules = []
page_size = 20
cursor = None
while True:
params = {"pageSize": page_size}
if cursor:
params["cursor"] = cursor
response = await self.client.get(
f"{self.base_url}/api/v2/outbound/campaigns/{campaign_id}/schedules",
headers=self.headers,
params=params
)
response.raise_for_status()
data = response.json()
schedules.extend(data.get("entities", []))
next_page = data.get("nextPageCursor")
if not next_page:
break
cursor = next_page
return schedules
def detect_overlap(self, new_schedule: SchedulePayload, existing: list) -> bool:
new_start = new_schedule.start_date_time
new_end = new_schedule.end_date_time
for schedule in existing:
if schedule.get("segmentId") != new_schedule.segment_id:
continue
existing_start = date_parser.parse(schedule["startDateTime"])
existing_end = date_parser.parse(schedule["endDateTime"])
if new_start < existing_end and new_end > existing_start:
logger.warning("Schedule overlap detected with ID: %s", schedule["id"])
return True
return False
def calculate_priority_score(self, schedule: SchedulePayload, segment_size: int) -> float:
base_score = schedule.priority * 10
capacity_factor = min(segment_size / 10000, 1.0)
return base_score + (capacity_factor * 5)
Step 3: Asynchronous Job Orchestration & Schedule Registration
Schedule registration requires format verification, conflict resolution, and asynchronous execution to prevent blocking the event loop. The orchestrator implements exponential backoff for rate limits and registers the operation with the Genesys Cloud Job API for platform visibility.
Required OAuth scopes: outbound:campaign:write, job:write
import json
import random
class AsyncOrchestrator:
def __init__(self, client: httpx.AsyncClient, headers: dict, base_url: str):
self.client = client
self.headers = headers
self.base_url = base_url
async def retry_on_rate_limit(self, func, *args, max_retries: int = 3, **kwargs):
for attempt in range(max_retries):
try:
return await func(*args, **kwargs)
except httpx.HTTPStatusError as e:
if e.response.status_code == 429:
retry_after = int(e.response.headers.get("Retry-After", 2 ** attempt))
logger.info("Rate limited. Retrying in %s seconds.", retry_after)
await asyncio.sleep(retry_after)
continue
raise
async def register_job(self, job_name: str, job_type: str) -> str:
payload = {
"name": job_name,
"type": job_type,
"script": {
"name": "ScheduleProvisioningHandler",
"description": "Automated predictive schedule registration"
}
}
response = await self.client.post(
f"{self.base_url}/api/v2/jobs",
headers=self.headers,
json=payload
)
response.raise_for_status()
return response.json()["id"]
async def provision_schedule(self, campaign_id: str, schedule: SchedulePayload) -> dict:
endpoint = f"{self.base_url}/api/v2/outbound/campaigns/{campaign_id}/schedules"
async def _post_schedule():
response = await self.client.post(
endpoint,
headers=self.headers,
json=schedule.model_dump(by_alias=True)
)
return response
response = await self.retry_on_rate_limit(_post_schedule)
response.raise_for_status()
return response.json()
Step 4: External Webhook Synchronization & Telemetry
Deployment events must synchronize with external marketing automation platforms. The provisioner emits webhook callbacks and tracks provisioning latency, validation success rates, and audit trails for governance compliance.
import time
from enum import Enum
class ProvisioningStatus(Enum):
SUCCESS = "SUCCESS"
FAILED = "FAILED"
CONFLICT = "CONFLICT"
class TelemetryCollector:
def __init__(self):
self.metrics = {
"total_attempts": 0,
"successful_provisions": 0,
"validation_failures": 0,
"average_latency_ms": 0.0,
"latencies": []
}
def record_attempt(self, latency_ms: float, status: ProvisioningStatus):
self.metrics["total_attempts"] += 1
self.metrics["latencies"].append(latency_ms)
self.metrics["average_latency_ms"] = sum(self.metrics["latencies"]) / len(self.metrics["latencies"])
if status == ProvisioningStatus.SUCCESS:
self.metrics["successful_provisions"] += 1
elif status == ProvisioningStatus.FAILED:
self.metrics["validation_failures"] += 1
def get_success_rate(self) -> float:
if self.metrics["total_attempts"] == 0:
return 0.0
return (self.metrics["successful_provisions"] / self.metrics["total_attempts"]) * 100
async def send_webhook(url: str, payload: dict):
async with httpx.AsyncClient(timeout=10) as client:
await client.post(url, json=payload)
def generate_audit_log(entry: dict) -> str:
return json.dumps({
"timestamp": datetime.utcnow().isoformat(),
"action": "SCHEDULE_PROVISIONING",
"details": entry
})
Complete Working Example
The following module combines authentication, validation, orchestration, telemetry, and webhook synchronization into a single provisioner class. Replace the placeholder credentials before execution.
import asyncio
import logging
import httpx
from datetime import datetime, timezone
import pydantic
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
class ScheduleProvisioner:
def __init__(self, client_id: str, client_secret: str, base_url: str, webhook_url: str):
self.auth = GenesysAuth(client_id, client_secret, base_url)
self.base_url = base_url
self.webhook_url = webhook_url
self.telemetry = TelemetryCollector()
self.client = httpx.AsyncClient(timeout=30, limits=httpx.Limits(max_connections=20))
async def execute(self, campaign_id: str, schedule_config: dict) -> dict:
start_time = time.time()
headers = await self.auth.get_headers()
try:
schedule = SchedulePayload(**schedule_config)
except pydantic.ValidationError as e:
logger.error("Schema validation failed: %s", e)
return self._finalize(start_time, ProvisioningStatus.FAILED, {"error": str(e)})
validator = ScheduleValidator(self.client, headers, self.base_url)
orchestrator = AsyncOrchestrator(self.client, headers, self.base_url)
existing = await validator.fetch_existing_schedules(campaign_id)
if validator.detect_overlap(schedule, existing):
logger.warning("Automatic conflict resolution triggered. Schedule rejected due to overlap.")
return self._finalize(start_time, ProvisioningStatus.CONFLICT, {"reason": "overlap_detected"})
segment_response = await self.client.get(
f"{self.base_url}/api/v2/outbound/segments/{schedule.segment_id}",
headers=headers
)
segment_response.raise_for_status()
segment_data = segment_response.json()
segment_size = segment_data.get("contactCount", 0)
priority_score = validator.calculate_priority_score(schedule, segment_size)
logger.info("Priority score calculated: %.2f", priority_score)
job_id = await orchestrator.register_job(f"schedule_{campaign_id}", "OUTBOUND_SCHEDULE_CREATE")
try:
result = await orchestrator.provision_schedule(campaign_id, schedule)
status = ProvisioningStatus.SUCCESS
except httpx.HTTPError as e:
logger.error("Provisioning failed: %s", e)
status = ProvisioningStatus.FAILED
result = {"error": str(e)}
latency_ms = (time.time() - start_time) * 1000
self.telemetry.record_attempt(latency_ms, status)
audit_entry = {
"campaign_id": campaign_id,
"segment_id": schedule.segment_id,
"priority_score": priority_score,
"job_id": job_id,
"status": status.value,
"latency_ms": latency_ms
}
await send_webhook(self.webhook_url, audit_entry)
logger.info("Audit log generated: %s", generate_audit_log(audit_entry))
return self._finalize(start_time, status, result)
def _finalize(self, start_time: float, status: ProvisioningStatus, payload: dict) -> dict:
latency_ms = (time.time() - start_time) * 1000
return {
"status": status.value,
"latency_ms": latency_ms,
"success_rate": self.telemetry.get_success_rate(),
"payload": payload
}
if __name__ == "__main__":
async def main():
provisioner = ScheduleProvisioner(
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_CLIENT_SECRET",
base_url="https://api.mypurecloud.com",
webhook_url="https://your-marketing-platform.example.com/webhooks/genesys"
)
config = {
"segment_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"delivery_window": {
"start_time": "09:00:00",
"end_time": "17:00:00",
"time_zone": "America/Chicago"
},
"throttle_rate": 150,
"start_date_time": datetime(2024, 11, 15, 9, 0, tzinfo=timezone.utc),
"end_date_time": datetime(2024, 11, 15, 17, 0, tzinfo=timezone.utc),
"priority": 5
}
result = await provisioner.execute("campaign-uuid-123", config)
print(json.dumps(result, indent=2))
asyncio.run(main())
Common Errors & Debugging
Error: 401 Unauthorized
What causes it: The OAuth token has expired or the client credentials are invalid. The token caching logic in GenesysAuth checks token_expiry before requesting a new token.
How to fix it: Ensure the client_id and client_secret match the application in the Genesys Cloud admin console. Verify the token rotation logic subtracts a buffer period before expiry.
Code showing the fix:
if self.token and time.time() < self.token_expiry - 300:
return self.token
Error: 403 Forbidden
What causes it: The OAuth token lacks the required outbound:campaign:write scope, or the application does not have license tier permissions for predictive engagement.
How to fix it: Update the OAuth application scopes in the Genesys Cloud admin console. Confirm the organization license includes Outbound Predictive dialer capabilities.
Code showing the fix:
Add scope verification during initialization:
if "outbound:campaign:write" not in payload["scope"]:
raise PermissionError("Missing required outbound:campaign:write scope")
Error: 409 Conflict
What causes it: A schedule already exists for the same segment and time window. The overlap detection algorithm catches this before transmission.
How to fix it: The provisioner automatically rejects overlapping schedules. Adjust the start_date_time and end_date_time or select a different audience segment.
Code showing the fix:
if validator.detect_overlap(schedule, existing):
return self._finalize(start_time, ProvisioningStatus.CONFLICT, {"reason": "overlap_detected"})
Error: 429 Too Many Requests
What causes it: The API rate limit threshold has been exceeded. Genesys Cloud returns a Retry-After header.
How to fix it: The retry_on_rate_limit method implements exponential backoff. Ensure the backoff respects the Retry-After header value.
Code showing the fix:
retry_after = int(e.response.headers.get("Retry-After", 2 ** attempt))
await asyncio.sleep(retry_after)