Streaming Genesys Cloud Agent Assist Real-Time Sentiment Alerts via WebSocket with Python

How-To-Articles
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Streaming Genesys Cloud Agent Assist Real-Time Sentiment Alerts via WebSocket with Python

What You Will Build

  • A production-grade Python module that subscribes to Genesys Cloud conversational WebSocket events, filters real-time sentiment alerts against configurable thresholds, and pushes validated alerts to external coaching systems.
  • Uses the official /api/v2/conversations/events/stream WebSocket endpoint and the genesyscloud Python SDK for authentication and REST fallbacks.
  • Covers Python 3.9+ with websockets, pydantic, aiohttp, and logging.

Prerequisites

  • OAuth 2.0 Client Credentials grant configured in Genesys Cloud
  • Required scopes: conversation:read, analytics:events:read, agentassist:read
  • Genesys Cloud Python SDK >= 2.0.0
  • Python 3.9+ runtime
  • External dependencies: pip install websockets pydantic aiohttp genesyscloud

Authentication Setup

Genesys Cloud WebSocket connections require a valid Bearer token in the Authorization header. The Python SDK manages token lifecycle automatically, but you must extract the active token for the WebSocket handshake. The following code initializes the SDK, requests a client credentials token, and caches the expiry timestamp for automatic refresh before connection.

import time
from genesyscloud import PureCloudPlatformClientV2

def get_genesys_client(environment: str = "us-east-1") -> PureCloudPlatformClientV2:
    client = PureCloudPlatformClientV2()
    client.set_environment(environment)
    return client

def fetch_bearer_token(client: PureCloudPlatformClientV2, client_id: str, client_secret: str) -> tuple[str, float]:
    """Returns (token, expiry_timestamp). Refreshes automatically if expired."""
    token_response = client.authenticator.get_client_credentials_token(client_id, client_secret)
    access_token = token_response.access_token
    expires_in = token_response.expires_in
    expiry_timestamp = time.time() + expires_in - 30  # Refresh 30 seconds before expiry
    return access_token, expiry_timestamp

The get_client_credentials_token method handles the POST to /oauth/token with grant_type=client_credentials. You must store the expiry_timestamp and call fetch_bearer_token again when the current token approaches expiration. The WebSocket client will use this token in the initial handshake headers.

Implementation

Step 1: Subscription Payload Construction and Schema Validation

Genesys Cloud accepts a JSON subscription payload on the conversational WebSocket. The payload must declare the event types, interaction filters, and alert directives. You must validate the payload against WebSocket constraints: maximum payload size of 64 KB, maximum of 50 concurrent interaction subscriptions per connection, and a hard limit of 100 subscription messages per minute.

import json
from pydantic import BaseModel, Field, validator
from typing import List, Optional

class SentimentThresholdMatrix(BaseModel):
    positive: float = Field(ge=0.0, le=1.0, default=0.75)
    neutral: float = Field(ge=0.0, le=1.0, default=0.40)
    negative: float = Field(ge=0.0, le=1.0, default=0.65)

class AlertLevelDirective(BaseModel):
    level: str = Field(pattern=r"^(low|medium|high|critical)$")
    action: str = Field(pattern=r"^(notify|escalate|suppress)$")

class StreamSubscriptionPayload(BaseModel):
    interaction_ids: List[str] = Field(max_items=50)
    sentiment_thresholds: SentimentThresholdMatrix
    alert_directive: AlertLevelDirective
    context_window_seconds: int = Field(ge=10, le=600, default=120)

    @validator("interaction_ids")
    def validate_interaction_format(cls, v):
        for iid in v:
            if not iid.startswith("interaction:") or len(iid) < 15:
                raise ValueError("Interaction ID must follow Genesys Cloud format: interaction:<uuid>")
        return v

    def to_ws_json(self) -> str:
        return json.dumps({
            "id": "agentassist_sentiment_sub",
            "type": "subscribe",
            "events": ["conversation:ai:sentiment", "conversation:ai:agentassist"],
            "filters": {
                "interactionIds": self.interaction_ids,
                "sentimentThresholds": self.sentiment_thresholds.dict(),
                "alertDirective": self.alert_directive.dict(),
                "contextWindow": self.context_window_seconds
            }
        })

The to_ws_json method produces a compliant subscription message. You must send this payload immediately after the WebSocket handshake. The server responds with a {"type": "subscribe", "id": "agentassist_sentiment_sub", "status": "success"} acknowledgment. If the payload exceeds 64 KB or violates the interaction ID format, Genesys returns a {"type": "error", "code": 400, "message": "Invalid subscription payload"} message that you must handle by closing the connection and logging the schema violation.

Step 2: Atomic WebSocket Operations and Alert Push Handling

You must handle incoming WebSocket frames atomically to prevent race conditions during high-throughput streaming. Each frame must be parsed, verified against the expected schema, and deduplicated before processing. Genesys Cloud may send duplicate events during network retries, so you must maintain a sliding window of processed alert IDs.

import asyncio
import websockets
import uuid
from datetime import datetime, timezone
from collections import deque

class AlertProcessor:
    def __init__(self, dedup_window_seconds: int = 60):
        self.processed_alerts: deque = deque(maxlen=10000)
        self.dedup_window = dedup_window_seconds

    async def handle_incoming_frame(self, ws: websockets.WebSocketClientProtocol, raw_payload: str):
        try:
            payload = json.loads(raw_payload)
        except json.JSONDecodeError:
            await self._reject_frame(ws, "Invalid JSON format")
            return

        if payload.get("type") != "event" or payload.get("event") != "conversation:ai:sentiment":
            return  # Ignore non-sentiment events

        alert_id = payload.get("alertId")
        if not alert_id:
            await self._reject_frame(ws, "Missing alertId in event payload")
            return

        # Atomic deduplication check
        current_ts = time.time()
        if alert_id in self.processed_alerts:
            return
        if self.processed_alerts and (current_ts - self._get_oldest_ts() > self.dedup_window):
            self.processed_alerts.clear()  # Prune expired window

        self.processed_alerts.append({"id": alert_id, "ts": current_ts})
        await self._process_alert(payload)

    def _get_oldest_ts(self) -> float:
        return self.processed_alerts[0]["ts"] if self.processed_alerts else time.time()

    async def _reject_frame(self, ws: websockets.WebSocketClientProtocol, reason: str):
        error_msg = json.dumps({"type": "error", "reason": reason})
        await ws.send(error_msg)

    async def _process_alert(self, payload: dict):
        # Placeholder for validation pipeline (Step 3)
        pass

The handle_incoming_frame method parses the JSON, verifies the event type, and checks the alertId against the deduplication queue. If the alert is new, it appends the ID and timestamp to the deque. The maxlen=10000 prevents memory exhaustion during sustained streaming. You must send rejection messages back to the WebSocket only when the frame violates format requirements. Genesys Cloud does not require acknowledgment frames for successful events, so you omit them to reduce outbound traffic.

Step 3: Confidence Score Checking and Context Window Verification

Alert fatigue occurs when low-confidence sentiment detections or out-of-context window events trigger coaching tools. You must implement a validation pipeline that checks the confidence score against the threshold matrix and verifies that the event timestamp falls within the configured context window relative to the interaction start time.

import logging

logger = logging.getLogger("agentassist.streamer")

class ValidationPipeline:
    def __init__(self, thresholds: SentimentThresholdMatrix, context_window_seconds: int):
        self.thresholds = thresholds
        self.context_window = context_window_seconds

    def validate_sentiment_alert(self, payload: dict) -> bool:
        sentiment_data = payload.get("data", {}).get("sentiment", {})
        confidence = sentiment_data.get("confidence", 0.0)
        score = sentiment_data.get("score", 0.0)
        event_ts = datetime.fromisoformat(payload.get("eventTime", "")).timestamp()
        interaction_start = datetime.fromisoformat(payload.get("interactionStartTime", "")).timestamp()

        # Context window verification
        elapsed = event_ts - interaction_start
        if elapsed < 0 or elapsed > self.context_window:
            logger.debug("Alert dropped: outside context window. Elapsed: %.2fs", elapsed)
            return False

        # Confidence score checking
        required_confidence = self._get_required_confidence(score)
        if confidence < required_confidence:
            logger.debug("Alert dropped: confidence %.2f below threshold %.2f", confidence, required_confidence)
            return False

        return True

    def _get_required_confidence(self, score: float) -> float:
        if score > 0.5:
            return self.thresholds.positive
        elif score < -0.5:
            return self.thresholds.negative
        return self.thresholds.neutral

The validate_sentiment_alert method extracts the confidence and score from the data.sentiment object. It calculates the elapsed time between eventTime and interactionStartTime to enforce the context window. If the event falls outside the window or the confidence score does not meet the threshold matrix, the method returns False. You must log these drops at the DEBUG level to maintain audit visibility without cluttering production logs.

Step 4: External Coaching Sync, Metrics Tracking, and Audit Logging

You must expose a callback mechanism to synchronize validated alerts with external coaching tools. Simultaneously, you must track streaming latency, alert delivery success rates, and generate structured audit logs for assist governance. The following class integrates callbacks, metrics, and audit logging into the stream lifecycle.

import aiohttp
from typing import Callable, Optional, Dict, Any
from dataclasses import dataclass, field

@dataclass
class StreamMetrics:
    total_events: int = 0
    validated_events: int = 0
    dropped_events: int = 0
    latency_samples: list[float] = field(default_factory=list)
    success_rate: float = 0.0

    def update(self, is_valid: bool, latency: float):
        self.total_events += 1
        self.latency_samples.append(latency)
        if is_valid:
            self.validated_events += 1
        else:
            self.dropped_events += 1
        self.success_rate = self.validated_events / self.total_events if self.total_events > 0 else 0.0

class CoachingSyncManager:
    def __init__(self, callback: Optional[Callable[[Dict[str, Any]], None]] = None):
        self.callback = callback
        self.metrics = StreamMetrics()
        self.audit_log: list[Dict[str, Any]] = []

    async def process_validated_alert(self, payload: dict, receipt_time: float):
        latency = receipt_time - datetime.fromisoformat(payload["eventTime"]).timestamp()
        self.metrics.update(is_valid=True, latency=latency)

        audit_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "alertId": payload.get("alertId"),
            "interactionId": payload.get("interactionId"),
            "confidence": payload["data"]["sentiment"]["confidence"],
            "latency_ms": round(latency * 1000, 2),
            "status": "pushed_to_coaching"
        }
        self.audit_log.append(audit_entry)

        if self.callback:
            try:
                # Simulate async callback execution without blocking WS receive
                await asyncio.get_event_loop().run_in_executor(None, self.callback, payload)
            except Exception as e:
                logger.error("Coaching callback failed: %s", str(e))
                audit_entry["status"] = "callback_failed"

    def get_metrics_report(self) -> dict:
        avg_latency = sum(self.metrics.latency_samples) / len(self.metrics.latency_samples) if self.metrics.latency_samples else 0
        return {
            "total_events": self.metrics.total_events,
            "validated_events": self.metrics.validated_events,
            "dropped_events": self.metrics.dropped_events,
            "success_rate": self.metrics.success_rate,
            "avg_latency_ms": round(avg_latency * 1000, 2)
        }

The process_validated_alert method calculates latency between the Genesys Cloud event timestamp and local receipt time. It updates the StreamMetrics dataclass, appends a structured audit entry, and invokes the external coaching callback in a separate executor to prevent blocking the WebSocket receive loop. You must track success_rate and avg_latency_ms for stream efficiency monitoring. The audit log persists all governance-relevant fields including alertId, interactionId, confidence, and processing status.

Complete Working Example

import asyncio
import logging
import time
from datetime import datetime, timezone
from typing import Callable, Dict, Any, Optional

import websockets
from genesyscloud import PureCloudPlatformClientV2

# Import classes from previous steps
# from ... import AlertProcessor, ValidationPipeline, CoachingSyncManager, StreamSubscriptionPayload, SentimentThresholdMatrix, AlertLevelDirective

async def run_agent_assist_streamer(
    environment: str,
    client_id: str,
    client_secret: str,
    interaction_ids: list[str],
    coaching_callback: Optional[Callable[[Dict[str, Any]], None]] = None
):
    client = PureCloudPlatformClientV2()
    client.set_environment(environment)
    token, expiry = fetch_bearer_token(client, client_id, client_secret)

    ws_url = f"wss://api.{environment}.mypurecloud.com/api/v2/conversations/events/stream"
    headers = {"Authorization": f"Bearer {token}"}

    subscription = StreamSubscriptionPayload(
        interaction_ids=interaction_ids,
        sentiment_thresholds=SentimentThresholdMatrix(),
        alert_directive=AlertLevelDirective(level="high", action="notify"),
        context_window_seconds=180
    )

    processor = AlertProcessor()
    validator = ValidationPipeline(subscription.sentiment_thresholds, subscription.context_window_seconds)
    sync_manager = CoachingSyncManager(coaching_callback)

    async with websockets.connect(ws_url, extra_headers=headers) as ws:
        # Send subscription payload
        await ws.send(subscription.to_ws_json())
        ack = await asyncio.wait_for(ws.recv(), timeout=10)
        if json.loads(ack).get("status") != "success":
            raise RuntimeError("Subscription rejected by Genesys Cloud WebSocket")

        logger.info("WebSocket subscribed to Agent Assist sentiment stream")

        try:
            while True:
                raw = await ws.recv()
                receipt_time = time.time()
                
                # Override processor to integrate validator and sync manager
                payload = json.loads(raw)
                if payload.get("type") == "event" and payload.get("event") == "conversation:ai:sentiment":
                    alert_id = payload.get("alertId")
                    if alert_id not in processor.processed_alerts:
                        processor.processed_alerts.append({"id": alert_id, "ts": receipt_time})
                        
                        if validator.validate_sentiment_alert(payload):
                            await sync_manager.process_validated_alert(payload, receipt_time)
                        else:
                            sync_manager.metrics.update(is_valid=False, latency=0)
                            logger.debug("Alert filtered by validation pipeline: %s", alert_id)
        except websockets.exceptions.ConnectionClosed as e:
            logger.warning("WebSocket connection closed: %s", e.code)
        except asyncio.TimeoutError:
            logger.error("Subscription acknowledgment timeout")
        finally:
            print("Stream Metrics Report:", json.dumps(sync_manager.get_metrics_report(), indent=2))
            print("Audit Log Entries:", len(sync_manager.audit_log))

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    asyncio.run(
        run_agent_assist_streamer(
            environment="us-east-1",
            client_id="YOUR_CLIENT_ID",
            client_secret="YOUR_CLIENT_SECRET",
            interaction_ids=["interaction:12345678-1234-1234-1234-123456789012"],
            coaching_callback=lambda data: print("Coaching Tool Received:", data.get("alertId"))
        )
    )

Replace YOUR_CLIENT_ID and YOUR_CLIENT_SECRET with your Genesys Cloud application credentials. The script initializes the SDK, establishes the WebSocket connection, sends the subscription payload, and enters a continuous receive loop. The validation pipeline filters events, the sync manager tracks metrics and audit logs, and the coaching callback receives validated alerts. The loop gracefully handles connection closures and prints a final metrics report.

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: The Bearer token expired during the WebSocket session or the client credentials lack required scopes.
  • Fix: Implement token refresh logic before the connection handshake. Verify that the OAuth application has conversation:read and analytics:events:read scopes enabled in the Genesys Cloud admin console.
  • Code Fix: Replace static token usage with a token manager that checks time.time() > expiry_timestamp and calls fetch_bearer_token before websockets.connect.

Error: 429 Too Many Requests

  • Cause: Exceeding the 100 subscription messages per minute limit or sending rapid reconnection attempts after a drop.
  • Fix: Implement exponential backoff for reconnections and throttle subscription updates. Genesys Cloud returns a {"type": "error", "code": 429, "message": "Rate limit exceeded"} frame.
  • Code Fix: Add a rate limiter class that tracks outbound message timestamps and applies asyncio.sleep() delays when the threshold is approached.

Error: Schema Validation Failure

  • Cause: Interaction IDs do not match the interaction:<uuid> format or the payload exceeds 64 KB.
  • Fix: Validate all input parameters using Pydantic before serialization. Trim interaction lists to 50 maximum.
  • Code Fix: Use the StreamSubscriptionPayload model from Step 1. Catch pydantic.ValidationError and log the specific field violations before attempting WebSocket transmission.

Error: WebSocket Connection Closed (Code 1006)

  • Cause: Network interruption, server-side maintenance, or prolonged idle time without ping/pong frames.
  • Fix: Enable websockets ping/pong keepalive and implement automatic reconnection with state preservation.
  • Code Fix: Pass ping_interval=20, ping_timeout=10 to websockets.connect. Wrap the receive loop in a retry mechanism that preserves the processed_alerts deque to avoid duplicate processing after reconnection.

Official References