Mapping Genesys Cloud Outbound Campaign Disposition Codes via Python SDK

What You Will Build

A Python utility that validates, binds, and synchronizes outbound campaign disposition codes using atomic PUT operations, tracks bind success rates, and generates governance audit logs. This tutorial uses the Genesys Cloud Outbound Campaign API (/api/v2/outbound/campaigns/{campaignId}/dispositioncodes) with the official genesyscloud Python SDK. The code is written in Python 3.10+.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in Genesys Cloud
• Required scopes: outbound:campaign:write, outbound:campaign:read, webhook:write
• genesyscloud SDK v1.0.0 or higher
• Python 3.10+ runtime
• External dependencies: httpx, pydantic, rich, python-dotenv
• Install dependencies: pip install genesyscloud httpx pydantic rich python-dotenv

Authentication Setup

Genesys Cloud APIs require OAuth 2.0 bearer tokens. The Python SDK handles token caching and automatic refresh when configured with client credentials. You must set environment variables for your organization domain, client ID, and client secret.

import os
from genesyscloud.platform.client import PlatformClient
from genesyscloud.outbound.api import OutboundApi
from genesyscloud.outbound.model import Dispositions, DispositionCode

def initialize_genesys_client() -> OutboundApi:
    """Initialize the Genesys Cloud SDK with platform authentication."""
    platform_client = PlatformClient(
        environment=os.getenv("GENESYS_ENV", "mypurecloud.com"),
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET")
    )
    platform_client.login()
    return OutboundApi(platform_client)

The SDK maintains an in-memory token cache. Token expiration triggers automatic silent refresh via the /oauth/token endpoint. You do not need to implement manual refresh logic unless you run distributed workers that require cross-process token sharing.

Implementation

Step 1: Validate Mapping Schema Against Taxonomy Constraints

Disposition codes must comply with Genesys Cloud taxonomy constraints before submission. The API enforces a maximum of 100 codes per campaign, alphanumeric patterns, and uniqueness rules. You must also verify that new codes do not conflict with existing analytics dimensions or create orphan references.

import re
from pydantic import BaseModel, field_validator, ValidationError
from typing import List

TAXONOMY_PATTERN = re.compile(r"^[A-Z0-9_]{3,20}$")
MAX_CODE_LIMIT = 100

class DispositionCodeRef(BaseModel):
    code: str
    name: str
    color: str
    enabled: bool = True
    category_prefix: str = ""

    @field_validator("code")
    @classmethod
    def validate_taxonomy_constraints(cls, v: str) -> str:
        if not TAXONOMY_PATTERN.match(v):
            raise ValueError("Code must be 3-20 uppercase alphanumeric characters or underscores")
        return v

    @field_validator("color")
    @classmethod
    def validate_hex_color(cls, v: str) -> str:
        if not re.match(r"^#[0-9A-F]{6}$", v):
            raise ValueError("Color must be a valid 6-digit hex code")
        return v

class DispositionCodeMatrix(BaseModel):
    codes: List[DispositionCodeRef]

    @field_validator("codes")
    @classmethod
    def validate_maximum_code_limits(cls, v: List[DispositionCodeRef]) -> List[DispositionCodeRef]:
        if len(v) > MAX_CODE_LIMIT:
            raise ValueError(f"Exceeds maximum-code-limits. Maximum allowed is {MAX_CODE_LIMIT}")
        return v

    @field_validator("codes")
    @classmethod
    def validate_orphan_code_checking(cls, v: List[DispositionCodeRef]) -> List[DispositionCodeRef]:
        existing_codes = [c.code for c in v]
        if len(existing_codes) != len(set(existing_codes)):
            raise ValueError("Duplicate codes detected. Orphan-code checking failed.")
        return v

def build_code_matrix(raw_codes: list[dict]) -> DispositionCodeMatrix:
    """Construct and validate the code-matrix payload."""
    try:
        matrix = DispositionCodeMatrix(codes=[DispositionCodeRef(**c) for c in raw_codes])
        return matrix
    except ValidationError as e:
        raise ValueError(f"Standard-compliance verification pipeline failed: {e}")

This validation pipeline enforces taxonomy constraints, checks the maximum code limit, and prevents orphan code collisions. The category_prefix field supports category-hierarchy calculation by grouping codes under shared prefixes (e.g., SALES_, SUPPORT_).

Step 2: Bind Directive and Atomic HTTP PUT Operation

The bind directive refers to the atomic PUT operation that replaces the entire disposition code set for a campaign. Genesys Cloud processes this as a single transaction. You must format the payload using SDK models and handle rate limits explicitly.

Raw HTTP cycle for transparency:

PUT /api/v2/outbound/campaigns/{campaignId}/dispositioncodes HTTP/1.1
Host: mydomain.mypurecloud.com
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Content-Type: application/json
Accept: application/json

{
  "dispositionCodes": [
    {
      "id": null,
      "code": "SALES_CONVERTED",
      "name": "Sales Converted",
      "color": "#00FF00",
      "enabled": true
    },
    {
      "id": null,
      "code": "SALES_NO_ANSWER",
      "name": "Sales No Answer",
      "color": "#FF0000",
      "enabled": true
    }
  ]
}

Expected response:

{
  "dispositionCodes": [
    {
      "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
      "code": "SALES_CONVERTED",
      "name": "Sales Converted",
      "color": "#00FF00",
      "enabled": true
    },
    {
      "id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
      "code": "SALES_NO_ANSWER",
      "name": "Sales No Answer",
      "color": "#FF0000",
      "enabled": true
    }
  ]
}

SDK implementation with retry logic for 429 responses:

import time
import httpx
from genesyscloud.outbound.model import Dispositions

def execute_bind_directive(api: OutboundApi, campaign_id: str, matrix: DispositionCodeMatrix, max_retries: int = 3) -> Dispositions:
    """Execute atomic HTTP PUT operation with automatic index triggers and format verification."""
    sdk_codes = [
        DispositionCode(
            code=c.code,
            name=c.name,
            color=c.color,
            enabled=c.enabled
        )
        for c in matrix.codes
    ]
    payload = Dispositions(disposition_codes=sdk_codes)

    attempt = 0
    while attempt < max_retries:
        try:
            response = api.put_outbound_campaign_disposition_codes(
                campaign_id=campaign_id,
                body=payload
            )
            return response
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 429 and attempt < max_retries - 1:
                retry_after = int(e.response.headers.get("Retry-After", 2 ** attempt))
                time.sleep(retry_after)
                attempt += 1
                continue
            raise
        except Exception as e:
            raise RuntimeError(f"Bind directive failed: {e}")

The PUT operation replaces the existing code set atomically. Genesys Cloud automatically triggers index updates for analytics aggregation evaluation logic. You do not need to manually refresh report caches.

Step 3: Category Hierarchy Calculation and Analytics Aggregation Evaluation

After successful binding, you must calculate the category hierarchy and verify that the new codes align with analytics aggregation rules. Disposition codes feed directly into campaign performance reports. Codes sharing a prefix group into logical categories.

from collections import defaultdict
import time
from typing import Dict, List, Tuple

def calculate_category_hierarchy(codes: List[DispositionCode]) -> Dict[str, List[str]]:
    """Calculate category-hierarchy based on code prefixes for analytics-aggregation evaluation."""
    hierarchy: Dict[str, List[str]] = defaultdict(list)
    for code in codes:
        prefix = code.code.split("_")[0] if "_" in code.code else code.code
        hierarchy[prefix].append(code.code)
    return dict(hierarchy)

def evaluate_analytics_aggregation(hierarchy: Dict[str, List[str]]) -> List[Dict[str, str]]:
    """Verify that category groups meet reporting dimension requirements."""
    evaluation_results = []
    for category, members in hierarchy.items():
        evaluation_results.append({
            "category": category,
            "member_count": len(members),
            "aggregation_status": "COMPLIANT" if len(members) >= 2 else "SINGLETON",
            "reporting_dimension": f"DISPOSITION_{category}"
        })
    return evaluation_results

This logic ensures that disposition codes map correctly to report dimensions. Singletons are flagged for review because they lack comparative analytics value. The hierarchy calculation runs synchronously after the bind directive completes.

Step 4: Webhook Synchronization and Latency Tracking

You must synchronize mapping events with external CRM systems via code indexed webhooks. Genesys Cloud supports outbound campaign event webhooks. You will also track mapping latency and bind success rates for map efficiency monitoring.

import json
from datetime import datetime, timezone
from genesyscloud.webhook.api import WebhookApi
from genesyscloud.webhook.model import Webhook, WebhookConfig

def register_code_indexed_webhook(api: WebhookApi, webhook_url: str, campaign_id: str) -> str:
    """Synchronize mapping events with external-crm via code indexed webhooks."""
    config = WebhookConfig(
        event_type="outbound:campaign:disposition:updated",
        filter=f"campaign.id:{campaign_id}",
        endpoint_url=webhook_url,
        http_method="POST",
        content_type="application/json"
    )
    webhook = Webhook(
        name=f"DispositionSync_{campaign_id}",
        description="Code indexed webhook for external CRM alignment",
        enabled=True,
        config=config
    )
    response = api.post_webhooks(body=webhook)
    return response.id

def track_bind_metrics(start_time: float, success: bool, campaign_id: str) -> Dict:
    """Track mapping latency and bind success rates for map efficiency."""
    latency_ms = (time.perf_counter() - start_time) * 1000
    return {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "campaign_id": campaign_id,
        "bind_success": success,
        "latency_ms": round(latency_ms, 2),
        "status": "COMPLETED" if success else "FAILED"
    }

Webhook registration uses the webhook:write scope. The filter ensures only disposition updates for the target campaign trigger the payload. Latency tracking uses time.perf_counter() for sub-millisecond precision.

Complete Working Example

This script combines validation, binding, hierarchy calculation, webhook synchronization, and audit logging into a single executable module.

import os
import time
import json
import logging
from datetime import datetime, timezone

from genesyscloud.platform.client import PlatformClient
from genesyscloud.outbound.api import OutboundApi
from genesyscloud.webhook.api import WebhookApi
from genesyscloud.outbound.model import Dispositions, DispositionCode

# Import local modules defined in previous steps
# from validation import build_code_matrix, DispositionCodeMatrix
# from hierarchy import calculate_category_hierarchy, evaluate_analytics_aggregation
# from webhook import register_code_indexed_webhook
# from metrics import track_bind_metrics

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

class DispositionCodeMapper:
    def __init__(self, environment: str, client_id: str, client_secret: str):
        self.platform = PlatformClient(
            environment=environment,
            client_id=client_id,
            client_secret=client_secret
        )
        self.platform.login()
        self.outbound_api = OutboundApi(self.platform)
        self.webhook_api = WebhookApi(self.platform)

    def apply_disposition_mapping(
        self,
        campaign_id: str,
        raw_codes: list[dict],
        webhook_url: str = None
    ) -> dict:
        start_time = time.perf_counter()
        audit_log = {
            "operation": "DISPOSITION_BIND",
            "campaign_id": campaign_id,
            "start_time": datetime.now(timezone.utc).isoformat(),
            "steps": []
        }

        try:
            # Step 1: Validate code-matrix
            logger.info("Validating code-matrix against taxonomy-constraints")
            matrix = build_code_matrix(raw_codes)
            audit_log["steps"].append({"phase": "validation", "status": "PASSED"})

            # Step 2: Execute bind directive
            logger.info("Executing atomic HTTP PUT bind directive")
            response = execute_bind_directive(self.outbound_api, campaign_id, matrix)
            audit_log["steps"].append({"phase": "bind", "status": "SUCCESS", "code_count": len(response.disposition_codes)})

            # Step 3: Hierarchy and analytics evaluation
            logger.info("Calculating category-hierarchy and analytics-aggregation")
            hierarchy = calculate_category_hierarchy(response.disposition_codes)
            aggregation_eval = evaluate_analytics_aggregation(hierarchy)
            audit_log["steps"].append({"phase": "analytics_evaluation", "status": "COMPLETED", "evaluations": aggregation_eval})

            # Step 4: Webhook synchronization
            if webhook_url:
                logger.info("Registering code indexed webhook for external-crm sync")
                webhook_id = register_code_indexed_webhook(self.webhook_api, webhook_url, campaign_id)
                audit_log["steps"].append({"phase": "webhook_sync", "status": "REGISTERED", "webhook_id": webhook_id})

            success = True
        except Exception as e:
            logger.error(f"Mapping pipeline failed: {e}")
            success = False
            audit_log["steps"].append({"phase": "error", "status": "FAILED", "message": str(e)})

        metrics = track_bind_metrics(start_time, success, campaign_id)
        audit_log["metrics"] = metrics
        audit_log["end_time"] = datetime.now(timezone.utc).isoformat()

        # Persist audit log
        log_filename = f"disposition_audit_{campaign_id}_{int(time.time())}.json"
        with open(log_filename, "w") as f:
            json.dump(audit_log, f, indent=2)
        logger.info(f"Audit log written to {log_filename}")

        return audit_log

if __name__ == "__main__":
    mapper = DispositionCodeMapper(
        environment=os.getenv("GENESYS_ENV", "mypurecloud.com"),
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET")
    )

    sample_codes = [
        {"code": "SALES_CONVERTED", "name": "Sales Converted", "color": "#00FF00"},
        {"code": "SALES_CALLBACK", "name": "Sales Callback", "color": "#FFA500"},
        {"code": "SUPPORT_RESOLVED", "name": "Support Resolved", "color": "#0000FF"},
        {"code": "SUPPORT_ESCALATED", "name": "Support Escalated", "color": "#FF0000"}
    ]

    result = mapper.apply_disposition_mapping(
        campaign_id=os.getenv("GENESYS_CAMPAIGN_ID"),
        raw_codes=sample_codes,
        webhook_url=os.getenv("EXTERNAL_CRM_WEBHOOK_URL")
    )
    print(json.dumps(result, indent=2))

Common Errors & Debugging

Error: 400 Bad Request (Invalid Code Format)

What causes it: The disposition code violates taxonomy constraints. Genesys Cloud rejects codes containing lowercase letters, spaces, or special characters outside the allowed set.
How to fix it: Ensure all codes match the regex ^[A-Z0-9_]{3,20}$. Remove spaces and convert to uppercase before submission.
Code showing the fix:

clean_code = raw_code.upper().replace(" ", "_")
clean_code = re.sub(r"[^A-Z0-9_]", "", clean_code)

Error: 409 Conflict (Duplicate or Orphan Codes)

What causes it: The code-matrix contains duplicate entries or references codes that already exist in a different campaign without proper scoping.
How to fix it: Run orphan-code checking before binding. Fetch existing codes via get_outbound_campaign_disposition_codes and diff against the new matrix.
Code showing the fix:

existing = api.get_outbound_campaign_disposition_codes(campaign_id=campaign_id)
existing_codes = {c.code for c in existing.disposition_codes}
new_codes = {c.code for c in matrix.codes}
if existing_codes.intersection(new_codes):
    raise ValueError("Duplicate codes detected during orphan-code checking")

Error: 429 Too Many Requests

What causes it: Rate limit cascade across microservices. Genesys Cloud enforces per-tenant and per-endpoint throttling.
How to fix it: Implement exponential backoff. The execute_bind_directive function already includes retry logic with Retry-After header parsing.
Code showing the fix:

if response.status_code == 429:
    delay = max(1, int(response.headers.get("Retry-After", 2 ** attempt)))
    time.sleep(delay)

Error: 503 Service Unavailable

What causes it: Platform maintenance or automatic index trigger backlog during high-volume bind operations.
How to fix it: Wait for platform recovery. Do not retry immediately. Log the event and schedule a delayed retry job.
Code showing the fix:

if response.status_code == 503:
    logger.warning("Platform index trigger backlog detected. Scheduling delayed retry.")
    raise RuntimeError("Service unavailable. Manual intervention required.")

Official References

• Outbound Campaign Disposition Codes API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow
• Webhook Configuration API
• Outbound Campaign Analytics Aggregation Rules