Implementing Sentiment-Triggered Auto-Evaluation for High-Risk Interaction Flagging

What This Guide Covers

This guide details the architecture for automating the detection of high-risk customer interactions using Genesys Cloud CX Speech Analytics and the Quality API. By the end of this implementation, your organization will have a serverless integration that intercepts negative sentiment or specific keyword triggers during or immediately after a call, automatically generates a Quality Evaluation (QE) with a failing score, and assigns it to a supervisor for mandatory review.

Prerequisites, Roles & Licensing

Licensing Requirements

• Genesys Cloud CX Platform: Standard or Premium tier.
• Speech Analytics: This is an add-on license. You must have the Speech Analytics entitlement enabled for the users who are being recorded and evaluated.
• Quality Management: The Quality Management add-on is required to create Evaluations via API.
• Architect: Required for configuring the flow that initiates the webhook or for routing logic based on the flag.

Required Permissions & OAuth Scopes

If you are building a custom integration (Node.js, Python, etc.) to handle the webhook and post the evaluation, the OAuth Client must have the following scopes:

• quality:evaluation:create
• quality:evaluation:read
• interaction:read
• user:read
• routing:queue:read (if associating with specific queues)

For the User performing the manual review of the auto-generated QE:

• quality:evaluation:edit
• quality:evaluation:view

External Dependencies

• Speech Analytics Topic/Keyword: A configured Topic or Keyword in Genesys Cloud Speech Analytics that defines “High Risk” (e.g., “Threat of Litigation,” “Abuse,” “Complaint”).
• Webhook Endpoint: A publicly accessible HTTPS endpoint (AWS Lambda, Azure Function, or Heroku) to receive the Genesys Webhook.
• Evaluation Form: A pre-existing Quality Evaluation Form in Genesys Cloud that includes a section for “Auto-Flagged Issues.”

The Implementation Deep-Dive

1. Designing the Speech Analytics Trigger

The foundation of this architecture is not the Quality API, but the Speech Analytics configuration. Most implementations fail here because they rely on generic sentiment scores rather than actionable intents.

The Trap: Do not trigger auto-evaluations based solely on “Negative Sentiment” without context. A customer may sound frustrated because they are confused by an IVR, not because the agent failed. Triggering a QE on every negative sentiment spike creates noise, causing supervisors to ignore legitimate flags.

The Solution: Configure a Speech Analytics Topic or Keyword that captures high-risk language.

1. Navigate to Admin > Speech Analytics > Topics.
2. Create a new Topic named High_Risk_Interaction.
3. Add phrases such as “record this call,” “I want to sue,” “supervisor,” “cancel my service immediately,” or “this is unacceptable.”
4. Set the Confidence Threshold to high (e.g., 0.8) to reduce false positives.
5. Enable Real-time detection if your license supports it, otherwise rely on Post-call analysis.

Architectural Reasoning: By using a Topic, Genesys Cloud provides a structured JSON payload in the webhook containing the exact phrase, timestamp, and speaker role (Customer vs. Agent). This granularity allows us to populate the QE comments field with specific evidence, rather than a generic “Bad Call” flag.

2. Configuring the Webhook Event

Genesys Cloud emits events when Speech Analytics jobs complete. We need to subscribe to the analytics:speech:job:completed event.

The Trap: Subscribing to analytics:speech:job:started is useless for auto-evaluation because the transcript and sentiment data are not yet available. You must wait for the job completion.

Steps:

1. Navigate to Admin > Integrations > Webhooks.
2. Click Add Webhook.
3. Set Name to Speech_Analytics_High_Risk_Webhook.
4. Set URL to your public endpoint (e.g., https://api.yourdomain.com/genesys/speech-webhook).
5. Under Events, select analytics:speech:job:completed.
6. Under Filters, add a filter to reduce load.
   • Filter Field: topic
   • Filter Operator: contains
   • Filter Value: High_Risk_Interaction
   • Note: If you are using Keywords instead of Topics, you may need to filter on the keywords array in the payload, which requires more complex server-side filtering. Topic filtering is preferred for performance.

Architectural Reasoning: Filtering at the Genesys platform level reduces the number of HTTP requests hitting your server. If you do not filter here, your server will receive a webhook for every single call analyzed, regardless of risk level. This increases latency and costs.

3. Building the Webhook Handler (Serverless Function)

This function receives the Genesys event, parses the transcript data, determines if a QE is warranted, and constructs the API payload.

The Trap: Do not attempt to parse the entire transcript in real-time on every webhook. The transcript field in the webhook payload can be large. Only extract the relevant phrases or keywords that triggered the topic.

Code Example (Node.js/Express):

const express = require('express');
const axios = require('axios');
const crypto = require('crypto');

const app = express();
app.use(express.json());

// Genesys Cloud OAuth Token Endpoint
const GENESYS_AUTH_URL = 'https://api.mypurecloud.com/oauth/token';
const GENESYS_BASE_URL = 'https://api.mypurecloud.com';
const CLIENT_ID = process.env.GENESYS_CLIENT_ID;
const CLIENT_SECRET = process.env.GENESYS_CLIENT_SECRET;

let accessToken = null;
let tokenExpiry = 0;

async function getAccessToken() {
    if (accessToken && Date.now() < tokenExpiry) {
        return accessToken;
    }

    const auth = Buffer.from(`${CLIENT_ID}:${CLIENT_SECRET}`).toString('base64');
    const response = await axios.post(GENESYS_AUTH_URL, 'grant_type=client_credentials', {
        headers: {
            'Authorization': `Basic ${auth}`,
            'Content-Type': 'application/x-www-form-urlencoded'
        }
    });

    accessToken = response.data.access_token;
    // Genesys tokens expire in 1 hour (3600 seconds). Subtract 60s for safety.
    tokenExpiry = Date.now() + (3540 * 1000);
    return accessToken;
}

app.post('/genesys/speech-webhook', async (req, res) => {
    const event = req.body;

    // 1. Validate Event Type
    if (event.eventType !== 'analytics:speech:job:completed') {
        return res.status(200).send('Ignored');
    }

    // 2. Extract Critical Data
    const interactionId = event.data.interactionId;
    const participantId = event.data.participantId; // The Agent's Participant ID
    const userId = event.data.userId; // The Agent's User ID
    const topics = event.data.topics || [];
    const phrases = event.data.phrases || [];

    // 3. Check for High-Risk Trigger
    const highRiskTopic = topics.find(t => t.name === 'High_Risk_Interaction');
    
    if (!highRiskTopic) {
        // If no high-risk topic found, exit gracefully
        return res.status(200).send('No High-Risk Topic Detected');
    }

    // 4. Construct QE Payload
    // We need the Evaluation Form ID. Hardcode or fetch from DB.
    const FORM_ID = 'your-evaluation-form-id-here'; 
    
    // Calculate a timestamp for the evaluation
    const evalDate = new Date().toISOString();

    // Build the Sections Array
    // Assume Form Section ID for "Comments" is 'section-comments'
    // Assume Form Section ID for "Sentiment Score" is 'section-sentiment'
    
    const sections = [
        {
            sectionId: 'section-comments',
            questionId: 'q-auto-flag', // Question ID in the form
            answer: {
                value: `Auto-Flagged: High Risk Interaction detected. Phrase: "${highRiskTopic.phrases[0].text}" at ${highRiskTopic.phrases[0].startTimestamp}`
            }
        },
        {
            sectionId: 'section-sentiment',
            questionId: 'q-score', // Question ID for the numeric score
            answer: {
                value: 0 // Auto-fail score
            }
        }
    ];

    const payload = {
        formId: FORM_ID,
        userId: userId,
        interactionId: interactionId,
        participantId: participantId,
        evaluatorId: 'system-bot-user-id', // Must be a valid User ID (e.g., a service account)
        evaluatedAt: evalDate,
        sections: sections,
        status: 'pending', // Or 'reviewed' if you want it to be final, but 'pending' is safer for audit trails
        notes: 'Generated via Speech Analytics Auto-Eval Integration'
    };

    try {
        const token = await getAccessToken();
        
        // 5. Post to Quality API
        const response = await axios.post(`${GENESYS_BASE_URL}/api/v2/quality/evaluations`, payload, {
            headers: {
                'Authorization': `Bearer ${token}`,
                'Content-Type': 'application/json'
            }
        });

        console.log('QE Created:', response.data.id);
        res.status(200).send('QE Created Successfully');

    } catch (error) {
        console.error('Error creating QE:', error.response ? error.response.data : error.message);
        res.status(500).send('Failed to create QE');
    }
});

app.listen(3000, () => console.log('Webhook Listener Running on Port 3000'));

Architectural Reasoning:

• Service Account: The evaluatorId must be a real User in Genesys Cloud. Create a dedicated “System Bot” user with minimal permissions. Do not use a human supervisor’s ID for automated actions, as this pollutes their performance metrics.
• Status ‘pending’: Setting the status to pending ensures the evaluation appears in the Supervisor’s “To Do” list. If you set it to reviewed, it may bypass notification workflows unless explicitly configured.

4. Configuring the Quality Evaluation Form

The auto-generated QE must map to an existing form. You cannot create a form dynamically via API during the webhook execution.

Steps:

1. Navigate to Admin > Quality > Evaluation Forms.
2. Create or edit a form named Auto_QC_High_Risk.
3. Add a Section called Automated Flags.
4. Add a Question Auto-Flag Reason (Type: Text Area).
5. Add a Question Automated Score (Type: Numeric).
   • Set the Weight to 100%.
   • Set the Passing Score to 100.
   • This ensures any auto-generated evaluation with a score of 0 is automatically marked as “Fail” in reporting.

The Trap: Do not use the same Evaluation Form for manual QC and Auto-QC. Mixing automated and manual evaluations in one form makes it difficult to report on “Human QC Performance” vs “Automated Compliance Flags.” Keep them separate to maintain data integrity in WEM (Workforce Engagement Management) reporting.

5. Routing the Notification to Supervisors

Creating the QE is only half the battle. The supervisor must be notified.

Steps:

1. Navigate to Admin > Notifications.
2. Create a new Notification Rule.
3. Trigger: Quality Evaluation Created.
4. Filter: Evaluator ID equals System Bot User ID.
5. Filter: Form ID equals Auto_QC_High_Risk.
6. Recipient: Select the Queue or Group of Supervisors responsible for the agent’s team.
7. Action: Send Email and/or In-App Notification.

Architectural Reasoning: Using Genesys Notifications rather than sending emails directly from your webhook server ensures that the notification is tied to the Genesys user identity and respects the user’s notification preferences (e.g., do not disturb hours). It also provides a single pane of glass for all Quality-related alerts.

Validation, Edge Cases & Troubleshooting

Edge Case 1: The “Split-Party” Call Failure

The Failure Condition:
The webhook fires, but the Quality API returns a 400 Bad Request with the error: Participant ID not found in Interaction.

The Root Cause:
This occurs in complex Architect flows where the interaction is transferred, merged, or split. The Speech Analytics job is associated with a specific participantId. If that participant is no longer the “primary” participant in the interaction record (e.g., the agent was transferred out, but the call continued with another agent), the participantId in the webhook payload may not match the active participant in the Quality API context.

The Solution:
In your webhook handler, perform a lookup of the Interaction via the interaction:read API before creating the QE.

1. Fetch the interaction: GET /api/v2/analytics/interactions/{interactionId}.
2. Validate that the participantId from the webhook exists in the participants array of the interaction.
3. If not, find the most recent active agent participant and use that participantId for the QE creation. This ensures the evaluation is attached to the correct agent’s record.

Edge Case 2: Transcript Latency and Real-Time Expectations

The Failure Condition:
Supervisors complain that the “Real-Time” flag did not appear during the call.

The Root Cause:
Genesys Cloud Speech Analytics is near-real-time, not instantaneous. There is a typical latency of 30-60 seconds for transcription and topic analysis. Furthermore, the QE creation via API is asynchronous.

The Solution:
Manage expectations. This system is for Post-Call or Near-End-Call flagging. If you require true real-time intervention (e.g., popping a window on the agent’s screen), you must use the Genesys Cloud Architect Real-Time Speech Analytics feature.

1. In Architect, use the Speech Analytics block.
2. Configure it to trigger a Webhook or Set Variable when the topic is detected.
3. Use the Pop-up or Screen Pop feature in Architect to alert the agent or supervisor immediately.
4. Do not use the Quality API for real-time alerts; it is too slow and creates clutter in the QE queue. Use Architect for real-time, and the Quality API for post-call documentation and scoring.

Edge Case 3: OAuth Token Expiry During Batch Processing

The Failure Condition:
Your webhook handler fails intermittently with 401 Unauthorized.

The Root Cause:
If your server processes multiple webhooks concurrently or holds onto a token for too long, the token may expire mid-request.

The Solution:
Implement a robust token caching mechanism with a “refresh ahead” strategy.

1. Store the token and its expiry time in memory (or Redis).
2. Check the expiry time on every request.
3. If Date.now() > tokenExpiry - 30000 (30 seconds before expiry), trigger a refresh.
4. Use a mutex or lock to prevent multiple concurrent refresh calls from generating new tokens simultaneously.
5. In Node.js, use a library like axios-oauth-client or implement a simple wrapper that handles the refresh logic transparently.

Edge Case 4: Duplicate Evaluations

The Failure Condition:
An agent receives two QEs for the same call because the webhook was delivered twice by Genesys Cloud.

The Root Cause:
Genesys Cloud guarantees “at least once” delivery for webhooks. Network glitches can cause retries.

The Solution:
Implement idempotency in your webhook handler.

1. Before creating the QE, query the Quality API:
   GET /api/v2/quality/evaluations?interactionId={interactionId}&userId={userId}&formId={formId}
2. If the API returns an existing evaluation with the same interactionId, userId, and formId, skip creation and return 200 OK.
3. Store a hash of the interactionId + participantId in a database (e.g., DynamoDB or PostgreSQL) with a TTL of 24 hours to quickly reject duplicates without hitting the Genesys API unnecessarily.

Official References

• Genesys Cloud Speech Analytics Webhook Events
• Genesys Cloud Quality API - Create Evaluation
• Configuring Speech Analytics Topics
• OAuth 2.0 Client Credentials Flow