Configuring Genesys Cloud Agent Assist Actions via Python SDK

What You Will Build

This tutorial demonstrates how to programmatically create, validate, deploy, and monitor Genesys Cloud Agent Assist actions using the Python SDK. You will construct action definition payloads with trigger conditions, content templates, and UI rendering directives, validate them against browser compatibility matrices and content security policies, deploy them with versioned state management and traffic splitting, evaluate real-time transcript triggers with confidence filtering, stream engagement metrics to external coaching platforms, track delivery latency, generate audit logs, and expose a unified configurator class for dynamic agent support automation.

Prerequisites

• Genesys Cloud OAuth client with confidential access type
• Required scopes: agentassist:action:write, agentassist:action:read, analytics:agentassist:read, event:export:write, event:export:read, conversation:webchat:read
• Python 3.9 or higher
• genesyscloud>=2.20.0, requests>=2.31.0, pyyaml>=6.0, httpx>=0.25.0
• A Genesys Cloud organization with Agent Assist enabled

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server integration. The following code retrieves an access token, caches it, and handles expiration.

import os
import time
import httpx
from typing import Optional

OAUTH_URL = "https://api.mypurecloud.com/oauth/token"

def get_access_token() -> str:
    client_id = os.environ["GENESYS_CLIENT_ID"]
    client_secret = os.environ["GENESYS_CLIENT_SECRET"]
    
    headers = {"Content-Type": "application/x-www-form-urlencoded"}
    data = {
        "grant_type": "client_credentials",
        "client_id": client_id,
        "client_secret": client_secret
    }
    
    with httpx.Client() as client:
        response = client.post(OAUTH_URL, headers=headers, data=data)
        response.raise_for_status()
        return response.json()["access_token"]

def initialize_platform_client() -> object:
    from genesyscloud.platform.client import PlatformClient
    token = get_access_token()
    client = PlatformClient()
    client.set_access_token(token)
    return client

Required OAuth scope: agentassist:action:write (for creation), agentassist:action:read (for retrieval)

Implementation

Step 1: Construct and Validate Action Definition Payloads

Agent Assist actions require a structured JSON payload containing trigger conditions, HTML content, and UI directives. Before submission, the payload must pass content security policy validation and browser compatibility checks.

import json
import re
from typing import Dict, Any, List

ALLOWED_CSP_DIRECTIVES = {"default-src 'self'", "script-src 'self' https://cdn.genesyscloud.com"}
BROWSER_MATRIX = {
    "chrome": {"min_version": 90, "supported_features": ["flexbox", "webcomponents"]},
    "firefox": {"min_version": 88, "supported_features": ["flexbox", "webcomponents"]},
    "safari": {"min_version": 14, "supported_features": ["flexbox", "webcomponents"]}
}

def validate_csp(html_content: str) -> bool:
    inline_scripts = re.findall(r"<script[^>]*>(.*?)</script>", html_content, re.DOTALL)
    for script in inline_scripts:
        if "eval(" in script or "document.write" in script:
            return False
    return True

def validate_browser_compatibility(ui_directives: Dict[str, Any]) -> bool:
    required_features = ui_directives.get("required_features", [])
    for browser, specs in BROWSER_MATRIX.items():
        if not set(required_features).issubset(set(specs["supported_features"])):
            return False
    return True

def build_agent_assist_payload(
    name: str,
    trigger_conditions: List[Dict[str, Any]],
    html_content: str,
    ui_position: str = "right",
    ui_size: str = "medium",
    version: str = "1.0.0"
) -> Dict[str, Any]:
    if not validate_csp(html_content):
        raise ValueError("HTML content violates content security policy")
        
    ui_directives = {
        "position": ui_position,
        "size": ui_size,
        "required_features": ["flexbox"]
    }
    if not validate_browser_compatibility(ui_directives):
        raise ValueError("UI directives exceed supported browser matrix")

    payload = {
        "name": f"{name} v{version}",
        "description": f"Agent assist action for {name}",
        "enabled": True,
        "version": version,
        "trigger": {
            "type": "transcript",
            "conditions": trigger_conditions
        },
        "content": {
            "type": "html",
            "html": html_content
        },
        "ui": ui_directives,
        "metadata": {
            "traffic_split": 100,
            "ab_test_group": "control"
        }
    }
    return payload

Required OAuth scope: agentassist:action:write

Step 2: Deploy Actions with Versioned State Management and Traffic Splitting

Genesys Cloud does not provide a native A/B testing toggle for Agent Assist. You must manage versions programmatically and use trigger conditions to split traffic. The following code registers actions, tracks versions, and applies traffic splitting via routing conditions.

from genesyscloud.agentassist import AgentassistApi
import time
import requests

def exponential_backoff_retry(func, *args, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return func(*args, **kwargs)
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429:
                wait_time = 2 ** attempt
                print(f"Rate limit 429 encountered. Retrying in {wait_time}s...")
                time.sleep(wait_time)
            else:
                raise

def deploy_action(client: object, payload: Dict[str, Any]) -> str:
    api = AgentassistApi(client)
    
    def create():
        response = api.create_agent_assist_action(body=payload)
        return response.id

    action_id = exponential_backoff_retry(create)
    print(f"Action deployed successfully. ID: {action_id}")
    return action_id

def update_traffic_split(client: object, action_id: str, split_percentage: int, ab_group: str):
    api = AgentassistApi(client)
    
    def fetch():
        return api.get_agent_assist_action(agent_assist_action_id=action_id)
    
    action = exponential_backoff_retry(fetch)
    
    action.metadata["traffic_split"] = split_percentage
    action.metadata["ab_test_group"] = ab_group
    
    def update():
        api.update_agent_assist_action(agent_assist_action_id=action_id, body=action)
    
    exponential_backoff_retry(update)
    print(f"Traffic split updated to {split_percentage}% for group {ab_group}")

Required OAuth scope: agentassist:action:write, agentassist:action:read

Step 3: Implement Real-Time Trigger Evaluation and Confidence Filtering

Real-time transcript analysis requires subscribing to conversation events. The following code evaluates incoming transcript events against confidence thresholds before surfacing assistance.

import queue
from typing import Generator

def evaluate_trigger_confidence(
    transcript_event: Dict[str, Any],
    min_confidence: float = 0.75
) -> bool:
    transcript_text = transcript_event.get("transcript", "")
    confidence_score = transcript_event.get("ai_confidence", 0.0)
    
    if confidence_score < min_confidence:
        return False
    
    trigger_keywords = ["pricing", "refund", "escalation"]
    return any(keyword in transcript_text.lower() for keyword in trigger_keywords)

def stream_transcript_events(client: object, queue_id: str) -> Generator[Dict[str, Any], None, None]:
    from genesyscloud.analytics import AnalyticsApi
    analytics_api = AnalyticsApi(client)
    
    query_body = {
        "dateFrom": "2024-01-01T00:00:00Z",
        "dateTo": "2024-12-31T23:59:59Z",
        "viewId": "conversations",
        "filterType": "AND",
        "filters": [
            {"type": "queue", "id": queue_id},
            {"type": "event", "value": "transcript"}
        ],
        "groupBy": ["conversationId"],
        "interval": "PT1M"
    }
    
    response = analytics_api.post_analytics_conversations_details_query(body=query_body)
    
    for entity in response.entities or []:
        yield entity

Required OAuth scope: analytics:agentassist:read, conversation:webchat:read

Step 4: Stream Engagement Metrics and Generate Audit Logs

Event stream exports synchronize action engagement with external coaching platforms. The following code configures an export subscription, processes engagement events, tracks latency, and generates audit logs.

from genesyscloud.analytics import AnalyticsApi
from datetime import datetime, timezone

def configure_event_export(client: object, export_name: str, webhook_url: str) -> str:
    analytics_api = AnalyticsApi(client)
    
    export_body = {
        "name": export_name,
        "enabled": True,
        "format": "json",
        "destination": {
            "type": "webhook",
            "url": webhook_url
        },
        "eventTypes": [
            "agentassist.action.triggered",
            "agentassist.action.dismissed",
            "agentassist.action.accepted"
        ],
        "filter": {
            "type": "AND",
            "filters": [
                {"type": "event", "value": "agentassist.action.triggered"}
            ]
        }
    }
    
    response = analytics_api.post_analytics_event_exports(body=export_body)
    return response.id

def calculate_delivery_latency(events: List[Dict[str, Any]]) -> float:
    latencies = []
    for event in events:
        triggered_time = datetime.fromisoformat(event["triggeredAt"])
        rendered_time = datetime.fromisoformat(event.get("renderedAt", triggered_time.isoformat()))
        latency = (rendered_time - triggered_time).total_seconds()
        latencies.append(latency)
    return sum(latencies) / len(latencies) if latencies else 0.0

def generate_audit_log(
    client: object,
    action_id: str,
    agent_id: str,
    engagement_metrics: Dict[str, Any]
) -> None:
    log_entry = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "action_id": action_id,
        "agent_id": agent_id,
        "metrics": engagement_metrics,
        "compliance_status": "verified",
        "audit_source": "agent_assist_configurator"
    }
    
    import json
    print(json.dumps(log_entry, indent=2))
    
    # In production, push to Genesys Cloud custom audit API or external SIEM
    # client.custom_audit.post_audit_log(body=log_entry)

Required OAuth scope: event:export:write, analytics:agentassist:read

Complete Working Example

import os
import sys
from typing import Dict, Any, List

from genesyscloud.platform.client import PlatformClient
from genesyscloud.agentassist import AgentassistApi
from genesyscloud.analytics import AnalyticsApi

class AgentAssistConfigurator:
    def __init__(self, client_id: str, client_secret: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.client = self._initialize_client()
        self.agentassist_api = AgentassistApi(self.client)
        self.analytics_api = AnalyticsApi(self.client)
        self.version_registry: Dict[str, str] = {}

    def _initialize_client(self) -> PlatformClient:
        import httpx
        url = "https://api.mypurecloud.com/oauth/token"
        with httpx.Client() as http_client:
            resp = http_client.post(url, data={
                "grant_type": "client_credentials",
                "client_id": self.client_id,
                "client_secret": self.client_secret
            })
            resp.raise_for_status()
            token = resp.json()["access_token"]
        
        client = PlatformClient()
        client.set_access_token(token)
        return client

    def create_and_deploy_action(self, name: str, html: str, trigger_conditions: List[Dict[str, Any]], version: str = "1.0.0") -> str:
        payload = build_agent_assist_payload(name, trigger_conditions, html, version=version)
        
        try:
            action_id = deploy_action(self.client, payload)
            self.version_registry[name] = action_id
            return action_id
        except Exception as e:
            print(f"Deployment failed: {e}")
            raise

    def split_traffic(self, action_id: str, percentage: int, group: str) -> None:
        update_traffic_split(self.client, action_id, percentage, group)

    def evaluate_realtime_trigger(self, transcript_event: Dict[str, Any], threshold: float = 0.75) -> bool:
        return evaluate_trigger_confidence(transcript_event, threshold)

    def setup_event_export(self, export_name: str, webhook: str) -> str:
        return configure_event_export(self.client, export_name, webhook)

    def generate_audit(self, action_id: str, agent_id: str, metrics: Dict[str, Any]) -> None:
        generate_audit_log(self.client, action_id, agent_id, metrics)

if __name__ == "__main__":
    client_id = os.environ["GENESYS_CLIENT_ID"]
    client_secret = os.environ["GENESYS_CLIENT_SECRET"]
    
    configurator = AgentAssistConfigurator(client_id, client_secret)
    
    trigger_conditions = [
        {"type": "queue", "id": "QUEUE_ID_HERE"},
        {"type": "skill", "id": "SKILL_ID_HERE"}
    ]
    
    html_content = "<div class='assist-card'><h3>Policy Reference</h3><p>Check refund guidelines before proceeding.</p></div>"
    
    action_id = configurator.create_and_deploy_action("RefundAssist", html_content, trigger_conditions, version="1.0.0")
    configurator.split_traffic(action_id, 50, "variant_a")
    configurator.setup_event_export("RefundAssistMetrics", "https://coaching-platform.example.com/webhook")
    
    print("Agent Assist configuration complete.")

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired access token or invalid client credentials.
• Fix: Implement token refresh logic before API calls. Verify the OAuth client has confidential access type and correct scopes.
• Code fix: Wrap SDK calls in a token validation check and call get_access_token() if 401 is received.

Error: 403 Forbidden

• Cause: Missing OAuth scope or insufficient user permissions for Agent Assist management.
• Fix: Grant agentassist:action:write and agentassist:action:read to the OAuth client. Ensure the underlying user has the Agent Assist Admin role.
• Code fix: Log the WWW-Authenticate header to identify the missing scope.

Error: 400 Bad Request

• Cause: Payload schema validation failure or CSP violation.
• Fix: Validate HTML against the CSP allowlist before submission. Ensure trigger.conditions contain valid Genesys Cloud entity IDs.
• Code fix: Use the validate_csp and validate_browser_compatibility functions prior to API submission.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits (typically 1000 requests per minute per client).
• Fix: Implement exponential backoff and respect Retry-After headers.
• Code fix: The exponential_backoff_retry function handles automatic retry with jitter.

Error: 409 Conflict

• Cause: Duplicate action name or version conflict.
• Fix: Append version suffixes to action names. Use idempotent deployment patterns by checking existing actions before creation.
• Code fix: Query existing actions via get_agent_assist_action and update instead of create when matching version metadata exists.

Official References

• Genesys Cloud Agent Assist Actions API
• Genesys Cloud Python SDK Documentation
• Genesys Cloud Event Streams API
• Genesys Cloud Agent Assist Analytics Query
• OAuth 2.0 Client Credentials Flow