Implementing Real-Time Agent “Adherence Drift” Alerts using the WFM Notifications API

What This Guide Covers

You are building a real-time adherence monitoring system that detects when an agent’s adherence state begins to drift - not just the binary “in/out of adherence” snapshot, but the trend: an agent who has been drifting out of adherence for 8 minutes should trigger a supervisor nudge before the 15-minute formal exception threshold. When complete, supervisors receive a Teams/Slack alert within 90 seconds of an agent beginning to drift, agents receive a soft nudge via an in-app notification before they’re formally out of adherence, and your weekly exception count drops by 30% because gentle early intervention replaces formal exception documentation.

Prerequisites, Roles & Licensing

• Genesys Cloud: WFM license with scheduling enabled (included in CX 3 or as an add-on)
• Permissions:
  • Workforce Management > Adherence > View
  • Notifications > Notification > All (for the WebSocket channel)
• Alert delivery: Microsoft Teams webhook, Slack webhook, or in-app notification via the Agent UI SDK
• Infrastructure: A lightweight stateful service (Node.js, Python with asyncio) that maintains per-agent drift state

The Implementation Deep-Dive

1. Understanding Adherence State vs. Adherence Drift

Standard adherence monitoring tells you a binary state at a point in time:

t=10:00 - Agent is IN_ADHERENCE (status: Available, scheduled: Available)
t=10:15 - Agent is OUT_OF_ADHERENCE (status: Break, scheduled: Available)

Drift detection tracks the transition:

t=10:00 - IN_ADHERENCE
t=10:03 - State change detected: agent went to Lunch, scheduled for Available
           → DriftState: DRIFTING since 10:03 (3 minutes)
t=10:06 - DriftState: DRIFTING 6 minutes → soft nudge threshold reached
t=10:10 - DriftState: DRIFTING 10 minutes → supervisor alert threshold reached
t=10:15 - DriftState: OUT_OF_ADHERENCE (formal exception)

Drift detection provides 6-12 minutes of intervention window before a formal exception is logged.

2. Genesys Cloud WFM Notifications API Subscription

The WFM Notifications API broadcasts real-time adherence events via WebSocket:

import asyncio
import json
import requests
import websockets
from datetime import datetime, timedelta

GENESYS_BASE_URL = "https://api.mypurecloud.com"

async def subscribe_wfm_adherence_events(access_token: str):
    """
    Subscribe to real-time WFM adherence change events via notification WebSocket.
    """
    headers = {"Authorization": f"Bearer {access_token}", "Content-Type": "application/json"}
    
    # Create notification channel
    channel_resp = requests.post(
        f"{GENESYS_BASE_URL}/api/v2/notifications/channels",
        headers=headers
    )
    channel_resp.raise_for_status()
    channel = channel_resp.json()
    channel_id = channel["id"]
    websocket_url = channel["connectUri"]
    
    # Subscribe to adherence topic (all users - use management unit filter for large orgs)
    sub_resp = requests.post(
        f"{GENESYS_BASE_URL}/api/v2/notifications/channels/{channel_id}/subscriptions",
        headers=headers,
        json=[
            {"id": "v2.workforcemanagement.adherence"}
        ]
    )
    sub_resp.raise_for_status()
    
    print(f"Subscribed to WFM adherence events on channel {channel_id}")
    
    return websocket_url, channel_id

async def run_adherence_monitor(access_token: str):
    websocket_url, channel_id = await subscribe_wfm_adherence_events(access_token)
    
    drift_tracker = AdherenceDriftTracker()
    
    async with websockets.connect(websocket_url) as ws:
        while True:
            try:
                raw = await asyncio.wait_for(ws.recv(), timeout=30)
                event = json.loads(raw)
                
                # Respond to heartbeat
                if event.get("topicName") == "channel.metadata":
                    await ws.send(json.dumps({"message": "ping"}))
                    continue
                
                # Process adherence event
                if event.get("topicName") == "v2.workforcemanagement.adherence":
                    for agent_data in event.get("eventBody", {}).get("entities", []):
                        drift_tracker.process_adherence_event(agent_data)
            
            except asyncio.TimeoutError:
                # No event received - check for stuck drifts
                drift_tracker.check_drift_thresholds()
                continue

3. Stateful Drift Tracker

from dataclasses import dataclass, field
from typing import Optional

@dataclass
class AgentDriftState:
    agent_id: str
    agent_name: str
    current_state: str  # "IN_ADHERENCE" | "DRIFTING" | "OUT_OF_ADHERENCE"
    drift_start: Optional[datetime] = None
    last_adherence_state: str = "InAdherence"
    scheduled_activity: str = ""
    actual_activity: str = ""
    nudge_sent: bool = False
    supervisor_alerted: bool = False

class AdherenceDriftTracker:
    NUDGE_THRESHOLD_MINUTES = 6     # Soft nudge to agent after 6 minutes drifting
    SUPERVISOR_THRESHOLD_MINUTES = 10  # Supervisor alert after 10 minutes drifting
    
    def __init__(self):
        self.agent_states: dict[str, AgentDriftState] = {}
    
    def process_adherence_event(self, agent_data: dict):
        agent_id = agent_data.get("user", {}).get("id")
        agent_name = agent_data.get("user", {}).get("name", "Unknown Agent")
        adherence_state = agent_data.get("adherenceState", "")  # "InAdherence" | "OutOfAdherence"
        scheduled_activity = agent_data.get("scheduledActivityCategory", "")
        actual_activity = agent_data.get("actualActivityCategory", "")
        
        if agent_id not in self.agent_states:
            self.agent_states[agent_id] = AgentDriftState(
                agent_id=agent_id,
                agent_name=agent_name,
                current_state="IN_ADHERENCE" if adherence_state == "InAdherence" else "DRIFTING"
            )
        
        state = self.agent_states[agent_id]
        state.scheduled_activity = scheduled_activity
        state.actual_activity = actual_activity
        
        if adherence_state == "InAdherence":
            # Agent returned to adherence - reset drift state
            if state.current_state != "IN_ADHERENCE":
                drift_duration = (datetime.utcnow() - state.drift_start).total_seconds() / 60 if state.drift_start else 0
                print(f"[RESOLVED] {agent_name} returned to adherence after {drift_duration:.1f} minutes.")
            
            state.current_state = "IN_ADHERENCE"
            state.drift_start = None
            state.nudge_sent = False
            state.supervisor_alerted = False
        
        elif adherence_state == "OutOfAdherence":
            if state.current_state == "IN_ADHERENCE":
                # Just started drifting
                state.current_state = "DRIFTING"
                state.drift_start = datetime.utcnow()
                state.nudge_sent = False
                state.supervisor_alerted = False
                
                print(f"[DRIFT START] {agent_name} | Scheduled: {scheduled_activity} | Actual: {actual_activity}")
            
            # Check thresholds
            self.check_thresholds_for_agent(state)
    
    def check_thresholds_for_agent(self, state: AgentDriftState):
        if not state.drift_start or state.current_state == "IN_ADHERENCE":
            return
        
        drift_minutes = (datetime.utcnow() - state.drift_start).total_seconds() / 60
        
        # Soft nudge threshold
        if drift_minutes >= self.NUDGE_THRESHOLD_MINUTES and not state.nudge_sent:
            self.send_agent_nudge(state, drift_minutes)
            state.nudge_sent = True
        
        # Supervisor alert threshold
        if drift_minutes >= self.SUPERVISOR_THRESHOLD_MINUTES and not state.supervisor_alerted:
            self.send_supervisor_alert(state, drift_minutes)
            state.supervisor_alerted = True
    
    def check_drift_thresholds(self):
        """Called periodically to re-check all drifting agents."""
        for agent_id, state in self.agent_states.items():
            if state.current_state == "DRIFTING":
                self.check_thresholds_for_agent(state)
    
    def send_agent_nudge(self, state: AgentDriftState, drift_minutes: float):
        """
        Send a soft nudge to the agent - via in-app notification, SMS, or Teams DM.
        """
        print(f"[NUDGE] {state.agent_name} - drifting {drift_minutes:.0f}min. Scheduled: {state.scheduled_activity}")
        
        # Option 1: Teams DM to agent
        send_teams_message(
            user_email=get_agent_email(state.agent_id),
            message=f"👋 Heads up: You're currently showing as out of adherence ({drift_minutes:.0f} min). "
                    f"Scheduled activity: **{state.scheduled_activity}**. "
                    f"Please return to your scheduled task when possible.",
            is_private=True
        )
    
    def send_supervisor_alert(self, state: AgentDriftState, drift_minutes: float):
        """
        Alert the supervisor - more urgent, includes action options.
        """
        print(f"[SUPERVISOR ALERT] {state.agent_name} - out of adherence {drift_minutes:.0f}min.")
        
        send_teams_channel_alert(
            channel="#adherence-alerts",
            message=(
                f"⚠️ **Adherence Alert** - {state.agent_name}\n"
                f"Out of adherence for **{drift_minutes:.0f} minutes**\n"
                f"Scheduled: `{state.scheduled_activity}` | Actual: `{state.actual_activity}`\n"
                f"Action: Contact agent or review their activity."
            )
        )

The Trap - sending supervisor alerts for every single out-of-adherence event: A supervisor receiving 40 alerts per hour for 2-minute lunch overruns will learn to ignore them. Apply minimum thresholds: nudge at 6 minutes (soft), supervisor alert at 10 minutes (actionable). Exclude certain activity codes from alerts entirely - a planned “Team Meeting” that runs 3 minutes long is not worth an alert. Build an exclusion list of activity categories that should not trigger drift alerts.

4. Teams Webhook Integration

import requests

TEAMS_WEBHOOK_URL = "https://your-org.webhook.office.com/webhookb2/..."
TEAMS_SUPERVISOR_CHANNEL_WEBHOOK = "https://your-org.webhook.office.com/webhookb2/supervisor..."

def send_teams_channel_alert(channel: str, message: str):
    """Send formatted Adaptive Card to Teams supervisor channel."""
    payload = {
        "type": "message",
        "attachments": [
            {
                "contentType": "application/vnd.microsoft.card.adaptive",
                "content": {
                    "$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
                    "type": "AdaptiveCard",
                    "version": "1.4",
                    "body": [
                        {
                            "type": "TextBlock",
                            "text": "⚠️ Adherence Alert",
                            "weight": "Bolder",
                            "size": "Medium",
                            "color": "Warning"
                        },
                        {
                            "type": "TextBlock",
                            "text": message,
                            "wrap": True
                        }
                    ],
                    "actions": [
                        {
                            "type": "Action.OpenUrl",
                            "title": "View in Genesys Cloud WFM",
                            "url": "https://apps.mypurecloud.com/workforce-management/"
                        }
                    ]
                }
            }
        ]
    }
    
    requests.post(TEAMS_SUPERVISOR_CHANNEL_WEBHOOK, json=payload)

5. Adherence Drift Analytics Dashboard

Track drift patterns over time to identify systemic issues:

def build_drift_summary_report(drift_events: list[dict], period_days: int = 7) -> dict:
    """
    Summarize drift events to identify patterns:
    - Which agents drift most frequently?
    - Which time periods have highest drift rates?
    - Which scheduled activities have highest non-adherence?
    """
    from collections import defaultdict
    
    agent_drift_counts = defaultdict(int)
    activity_drift_counts = defaultdict(int)
    hourly_drift_counts = defaultdict(int)
    
    for event in drift_events:
        agent_drift_counts[event["agentName"]] += 1
        activity_drift_counts[event["scheduledActivity"]] += 1
        hour = event["driftStartTime"][:13]  # "2025-05-14T14"
        hourly_drift_counts[hour] += 1
    
    return {
        "reportPeriodDays": period_days,
        "topDriftingAgents": sorted(
            [{"agent": k, "driftCount": v} for k, v in agent_drift_counts.items()],
            key=lambda x: x["driftCount"], reverse=True
        )[:10],
        "activityDriftRates": sorted(
            [{"activity": k, "driftCount": v} for k, v in activity_drift_counts.items()],
            key=lambda x: x["driftCount"], reverse=True
        ),
        "peakDriftHours": sorted(
            [{"hour": k, "driftCount": v} for k, v in hourly_drift_counts.items()],
            key=lambda x: x["driftCount"], reverse=True
        )[:5]
    }

Validation, Edge Cases & Troubleshooting

Edge Case 1: WebSocket Reconnection Losing Drift State

If the notification WebSocket disconnects and reconnects, you lose the in-memory drift state - agents who were drifting before the reconnect appear as new events after. On reconnect, immediately query the WFM Adherence API (GET /api/v2/workforcemanagement/adherence) to restore the current adherence state for all agents before resuming event processing. Preserve drift_start timestamps by storing them in Redis rather than in-memory, so reconnections don’t reset the drift clock.

Edge Case 2: Adherence Events During Planned Overtime (OT)

Agents working approved overtime may show as “out of adherence” if their OT schedule wasn’t imported into WFM before the shift. Build a pre-check: before sending any alert for an agent, query their current schedule to verify there isn’t an approved OT or exception code covering the current period. Skip drift alerts when the agent’s current schedule shows an approved exception.

Edge Case 3: High Event Volume During Mass Schedule Changes

When a supervisor pushes a mass schedule update to 200 agents simultaneously, the WFM notifications API broadcasts 200 adherence events within seconds. Your drift tracker must process these without creating spurious alerts (agents appearing to “drift” due to the schedule update latency). Implement a 60-second dampening window after detecting a mass schedule change event: buffer adherence events and process them after the window closes, once the WFM system has settled.

Edge Case 4: Privacy Concerns - Agents Aware of Real-Time Monitoring Intensity

Real-time drift monitoring at 6-minute precision may create stress for agents who feel micromanaged. Communicate the system’s purpose clearly: the goal is supportive early intervention, not punitive monitoring. Configure the agent-facing nudge to be supportive in tone (not accusatory), and ensure supervisors are trained to use alerts for coaching conversations, not disciplinary actions. Include a “drift window” in the agent portal showing their own current adherence metrics - transparency reduces the surveillance perception.

Official References

• Genesys Cloud WFM Adherence API - Developer Center
• Genesys Cloud Notification Service - Developer Center
• Microsoft Teams Incoming Webhooks - Microsoft Documentation
• Genesys Cloud WFM Real-Time Adherence Overview - Resource Center