Submitting Genesys Cloud Agent Assist Feedback via REST API with Node.js

What You Will Build

A Node.js module that validates, submits, and logs Agent Assist feedback payloads directly to the Genesys Cloud REST API. The implementation uses the official Genesys Cloud SDK for token management and axios for atomic POST operations with schema validation, latency tracking, and structured audit logging. The language covered is JavaScript (Node.js 18+).

Prerequisites

• OAuth Service Account with the agentassist:feedback:write scope
• Genesys Cloud @genesyscloud/purecloud-platform-client-v2 SDK (v2.0+)
• Node.js 18 or later
• External dependencies: axios, zod, uuid
• Environment variables: GENESYS_BASE_URL, GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, EXTERNAL_ML_WEBHOOK_URL

Authentication Setup

The Genesys Cloud OAuth 2.0 client credentials flow provides a short-lived bearer token. The SDK handles token acquisition and refresh automatically when configured correctly. Initialize the authentication client before routing any feedback requests.

import { Configuration, AuthClient } from '@genesyscloud/purecloud-platform-client-v2';

export async function initializeAuthClient(baseUrl, clientId, clientSecret) {
  const config = new Configuration({
    basePath: baseUrl,
    clientId: clientId,
    clientSecret: clientSecret,
  });

  const authClient = new AuthClient(config);
  
  const tokenResponse = await authClient.getOAuthToken({
    grant_type: 'client_credentials',
    scope: 'agentassist:feedback:write',
  });

  if (!tokenResponse.access_token) {
    throw new Error('Authentication failed: missing access token in response');
  }

  return {
    accessToken: tokenResponse.access_token,
    expiresIn: tokenResponse.expires_in,
    authClient,
  };
}

The access_token field must be attached to every subsequent POST request. Store the token in memory or a secure cache and verify expiration before submission. The SDK does not automatically refresh tokens for raw HTTP clients, so you must implement a wrapper or regenerate the token when expires_in approaches zero.

Implementation

Step 1: Payload Construction and Schema Validation

Agent Assist feedback must conform to strict engine constraints. The assist engine rejects payloads exceeding the maximum comment length, invalid sentiment matrices, or low relevance scores. A validation pipeline prevents storage failures and protects model training data from degradation.

The schema enforces a maximum comment length of 1000 characters, validates sentiment scores between -1.0 and 1.0, and requires a valid recommendation UUID. The spam detection pipeline scans for repetitive character sequences and enforces a minimum relevance threshold.

import { z } from 'zod';
import { v4 as uuidv4 } from 'uuid';

const FeedbackSchema = z.object({
  recommendationId: z.string().uuid('recommendationId must be a valid UUID'),
  feedbackType: z.enum(
    ['helpful', 'not_helpful', 'irrelevant', 'incorrect', 'duplicate', 'outdated'],
    'feedbackType must be a recognized assist engine directive'
  ),
  comments: z.string().max(1000, 'Comments must not exceed 1000 characters to prevent storage failures'),
  sentimentScore: z.number().min(-1.0).max(1.0, 'sentimentScore must be between -1.0 and 1.0'),
  sentimentConfidence: z.number().min(0.0).max(1.0, 'sentimentConfidence must be between 0.0 and 1.0'),
  relevanceScore: z.number().min(0.0).max(1.0, 'relevanceScore must be between 0.0 and 1.0'),
  agentId: z.string().uuid(),
  conversationId: z.string().uuid().optional(),
  metadata: z.record(z.string()).optional(),
});

export function validateFeedbackPayload(rawPayload) {
  const parsed = FeedbackSchema.safeParse(rawPayload);
  if (!parsed.success) {
    const errorDetails = parsed.error.errors.map(e => e.message).join('; ');
    throw new Error(`Schema validation failed: ${errorDetails}`);
  }

  const payload = parsed.data;

  // Spam detection verification pipeline
  const spamPattern = /(.)\1{20,}/;
  if (spamPattern.test(payload.comments)) {
    throw new Error('Spam detection triggered: repetitive character sequences detected in comments');
  }

  // Relevance score checking to prevent model degradation
  if (payload.relevanceScore < 0.3) {
    throw new Error('Relevance score below 0.3 threshold. Feedback rejected to prevent model degradation during assist scaling');
  }

  // Attach tracking identifiers
  payload.correlationId = uuidv4();
  payload.submittedAt = new Date().toISOString();

  return payload;
}

The validation function returns a clean, type-safe object or throws immediately. This prevents malformed data from reaching the Genesys Cloud API and ensures the assist engine receives only high-quality training signals.

Step 2: Atomic POST Submission with Rate Limit Handling

Feedback submission uses an atomic POST operation to /api/v2/agentassist/feedback. The Genesys Cloud API processes each submission as a discrete event and triggers automatic model retraining when feedback volume crosses defined thresholds. You must implement retry logic for 429 responses to handle rate-limit cascades across microservices.

import axios from 'axios';

const MAX_RETRY_ATTEMPTS = 3;
const BASE_DELAY_MS = 1000;

export async function submitFeedbackAtomically(baseUrl, accessToken, validatedPayload) {
  const endpoint = `${baseUrl}/api/v2/agentassist/feedback`;
  let attempt = 0;
  let lastError = null;

  while (attempt < MAX_RETRY_ATTEMPTS) {
    try {
      const startTime = Date.now();
      
      const response = await axios.post(endpoint, validatedPayload, {
        headers: {
          Authorization: `Bearer ${accessToken}`,
          'Content-Type': 'application/json',
          Accept: 'application/json',
          'X-Correlation-ID': validatedPayload.correlationId,
        },
        timeout: 10000,
        validateStatus: (status) => status < 500,
      });

      const latencyMs = Date.now() - startTime;

      if (response.status !== 200 && response.status !== 201) {
        throw new Error(`Format verification failed: received status ${response.status}`);
      }

      return {
        success: true,
        status: response.status,
        data: response.data,
        latencyMs,
        correlationId: validatedPayload.correlationId,
      };
    } catch (error) {
      lastError = error;

      if (error.response?.status === 429 && attempt < MAX_RETRY_ATTEMPTS - 1) {
        const retryAfterSeconds = parseInt(error.response.headers['retry-after'], 10) || BASE_DELAY_MS / 1000;
        const backoffMs = Math.max(retryAfterSeconds * 1000, BASE_DELAY_MS * Math.pow(2, attempt));
        
        await new Promise(resolve => setTimeout(resolve, backoffMs));
        attempt++;
        continue;
      }

      throw error;
    }
  }

  throw lastError;
}

HTTP Request/Response Cycle

POST /api/v2/agentassist/feedback HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Content-Type: application/json
Accept: application/json
X-Correlation-ID: a1b2c3d4-e5f6-7890-abcd-ef1234567890

{
  "recommendationId": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "feedbackType": "helpful",
  "comments": "The suggested knowledge article resolved the customer inquiry immediately.",
  "sentimentScore": 0.85,
  "sentimentConfidence": 0.92,
  "relevanceScore": 0.78,
  "agentId": "8b7c6d5e-4f3a-2b1c-0d9e-8f7a6b5c4d3e",
  "correlationId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "submittedAt": "2024-06-15T14:32:10.000Z"
}

HTTP/1.1 201 Created
Content-Type: application/json
Location: /api/v2/agentassist/feedback/a1b2c3d4-e5f6-7890-abcd-ef1234567890

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "recommendationId": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "feedbackType": "helpful",
  "status": "accepted",
  "processedAt": "2024-06-15T14:32:11.240Z",
  "retrainingTriggered": false
}

The response includes a processedAt timestamp and a retrainingTriggered flag. The assist engine aggregates feedback and initiates background model updates when thresholds are met. You do not control retraining directly, but the API confirms successful ingestion.

Step 3: Latency Tracking, Audit Logging, and Webhook Synchronization

Production feedback pipelines require observability. You must track submission latency, record audit logs for quality governance, and synchronize events with external ML pipelines via webhook callbacks. This step ensures alignment between Genesys Cloud assist signals and your custom training infrastructure.

import fs from 'fs/promises';
import path from 'path';

export async function recordAuditAndSync(submissionResult, validatedPayload, webhookUrl) {
  const auditEntry = {
    timestamp: new Date().toISOString(),
    correlationId: submissionResult.correlationId,
    agentId: validatedPayload.agentId,
    recommendationId: validatedPayload.recommendationId,
    feedbackType: validatedPayload.feedbackType,
    status: submissionResult.success ? 'submitted' : 'failed',
    latencyMs: submissionResult.latencyMs,
    sentimentScore: validatedPayload.sentimentScore,
    relevanceScore: validatedPayload.relevanceScore,
    commentsLength: validatedPayload.comments.length,
  };

  // Generate feedback audit logs for quality governance
  const logDir = path.resolve(process.cwd(), 'audit_logs');
  await fs.mkdir(logDir, { recursive: true });
  const logFile = path.join(logDir, `feedback-${new Date().toISOString().split('T')[0]}.json`);
  
  try {
    const existing = await fs.readFile(logFile, 'utf-8').catch(() => '[]');
    const logs = JSON.parse(existing);
    logs.push(auditEntry);
    await fs.writeFile(logFile, JSON.stringify(logs, null, 2));
  } catch (logError) {
    console.error('Audit log write failed:', logError.message);
  }

  // Synchronize feedback events with external ML pipelines via webhook callbacks
  if (webhookUrl && submissionResult.success) {
    try {
      await axios.post(webhookUrl, {
        event: 'agentassist.feedback.submitted',
        payload: {
          correlationId: submissionResult.correlationId,
          recommendationId: validatedPayload.recommendationId,
          feedbackType: validatedPayload.feedbackType,
          sentimentMatrix: {
            score: validatedPayload.sentimentScore,
            confidence: validatedPayload.sentimentConfidence,
          },
          adoptionImpact: validatedPayload.relevanceScore > 0.6 ? 'high' : 'medium',
        },
        latencyMs: submissionResult.latencyMs,
        processedAt: submissionResult.data.processedAt,
      }, { timeout: 5000 });
    } catch (webhookError) {
      console.error('External ML pipeline sync failed:', webhookError.message);
    }
  }

  return auditEntry;
}

The audit log captures every submission attempt with deterministic structure. The webhook payload exposes adoption impact rates by mapping relevance scores to categorical tiers. External pipelines receive the data within milliseconds of Genesys Cloud acceptance, enabling real-time model alignment.

Complete Working Example

The following module exports a reusable AgentAssistFeedbackSubmitter class. It handles authentication, validation, submission, retry logic, audit logging, and webhook synchronization. You can instantiate it in automated agent management workflows or batch processing scripts.

import { Configuration, AuthClient } from '@genesyscloud/purecloud-platform-client-v2';
import axios from 'axios';
import { z } from 'zod';
import { v4 as uuidv4 } from 'uuid';
import fs from 'fs/promises';
import path from 'path';

const FeedbackSchema = z.object({
  recommendationId: z.string().uuid(),
  feedbackType: z.enum(['helpful', 'not_helpful', 'irrelevant', 'incorrect', 'duplicate', 'outdated']),
  comments: z.string().max(1000),
  sentimentScore: z.number().min(-1.0).max(1.0),
  sentimentConfidence: z.number().min(0.0).max(1.0),
  relevanceScore: z.number().min(0.0).max(1.0),
  agentId: z.string().uuid(),
  conversationId: z.string().uuid().optional(),
  metadata: z.record(z.string()).optional(),
});

export class AgentAssistFeedbackSubmitter {
  constructor(config) {
    this.baseUrl = config.baseUrl;
    this.clientId = config.clientId;
    this.clientSecret = config.clientSecret;
    this.webhookUrl = config.webhookUrl;
    this.authClient = null;
    this.accessToken = null;
    this.tokenExpiry = null;
  }

  async initialize() {
    const gcConfig = new Configuration({
      basePath: this.baseUrl,
      clientId: this.clientId,
      clientSecret: this.clientSecret,
    });
    this.authClient = new AuthClient(gcConfig);
    await this.refreshToken();
  }

  async refreshToken() {
    const response = await this.authClient.getOAuthToken({
      grant_type: 'client_credentials',
      scope: 'agentassist:feedback:write',
    });
    this.accessToken = response.access_token;
    this.tokenExpiry = Date.now() + (response.expires_in * 1000);
  }

  ensureValidToken() {
    if (!this.accessToken || Date.now() >= this.tokenExpiry - 60000) {
      return this.refreshToken();
    }
    return Promise.resolve();
  }

  validatePayload(raw) {
    const parsed = FeedbackSchema.safeParse(raw);
    if (!parsed.success) throw new Error(parsed.error.errors.map(e => e.message).join('; '));

    const payload = parsed.data;
    if (/(.)\1{20,}/.test(payload.comments)) {
      throw new Error('Spam detection triggered: repetitive character sequences detected');
    }
    if (payload.relevanceScore < 0.3) {
      throw new Error('Relevance score below threshold. Feedback rejected to prevent model degradation');
    }

    payload.correlationId = uuidv4();
    payload.submittedAt = new Date().toISOString();
    return payload;
  }

  async submit(rawPayload) {
    await this.ensureValidToken();
    const validated = this.validatePayload(rawPayload);
    const startTime = Date.now();

    const response = await axios.post(`${this.baseUrl}/api/v2/agentassist/feedback`, validated, {
      headers: {
        Authorization: `Bearer ${this.accessToken}`,
        'Content-Type': 'application/json',
        Accept: 'application/json',
        'X-Correlation-ID': validated.correlationId,
      },
      timeout: 10000,
    });

    const latencyMs = Date.now() - startTime;
    const result = { success: true, status: response.status, data: response.data, latencyMs, correlationId: validated.correlationId };
    await this.auditAndSync(result, validated);
    return result;
  }

  async auditAndSync(result, payload) {
    const auditEntry = {
      timestamp: new Date().toISOString(),
      correlationId: result.correlationId,
      agentId: payload.agentId,
      recommendationId: payload.recommendationId,
      feedbackType: payload.feedbackType,
      status: result.success ? 'submitted' : 'failed',
      latencyMs: result.latencyMs,
      sentimentScore: payload.sentimentScore,
      relevanceScore: payload.relevanceScore,
    };

    const logDir = path.resolve(process.cwd(), 'audit_logs');
    await fs.mkdir(logDir, { recursive: true });
    const logFile = path.join(logDir, `feedback-${new Date().toISOString().split('T')[0]}.json`);
    try {
      const existing = await fs.readFile(logFile, 'utf-8').catch(() => '[]');
      const logs = JSON.parse(existing);
      logs.push(auditEntry);
      await fs.writeFile(logFile, JSON.stringify(logs, null, 2));
    } catch (err) {
      console.error('Audit log write failed:', err.message);
    }

    if (this.webhookUrl && result.success) {
      try {
        await axios.post(this.webhookUrl, {
          event: 'agentassist.feedback.submitted',
          payload: {
            correlationId: result.correlationId,
            recommendationId: payload.recommendationId,
            feedbackType: payload.feedbackType,
            sentimentMatrix: { score: payload.sentimentScore, confidence: payload.sentimentConfidence },
            adoptionImpact: payload.relevanceScore > 0.6 ? 'high' : 'medium',
          },
          latencyMs: result.latencyMs,
        }, { timeout: 5000 });
      } catch (err) {
        console.error('External ML pipeline sync failed:', err.message);
      }
    }
  }
}

Initialize the class with your environment credentials, call initialize(), then invoke submit() with raw feedback objects. The class handles token rotation, schema enforcement, atomic submission, latency measurement, audit persistence, and webhook dispatch automatically.

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The bearer token has expired, the client credentials are incorrect, or the OAuth token endpoint returned an empty access_token field.
• How to fix it: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET match a registered Service Account. Call submitter.refreshToken() before the next request. Ensure the token is refreshed at least 60 seconds before expiration.
• Code showing the fix: The ensureValidToken() method in the complete example automatically triggers a refresh when the expiry window approaches.

Error: 403 Forbidden

• What causes it: The OAuth token lacks the agentassist:feedback:write scope, or the Service Account does not have the Agent Assist Feedback Writer role assigned in the Genesys Cloud admin console.
• How to fix it: Regenerate the token with the explicit scope string. Assign the required role to the Service Account. Verify the organization enables Agent Assist features.
• Code showing the fix: The refreshToken() method explicitly requests scope: 'agentassist:feedback:write'. Adjust the role assignment in the admin portal if the error persists.

Error: 400 Bad Request

• What causes it: The payload violates the assist engine constraints. Common triggers include comments exceeding 1000 characters, invalid UUID formats for recommendationId or agentId, sentiment scores outside the -1.0 to 1.0 range, or relevance scores below the 0.3 threshold.
• How to fix it: Run the payload through the validatePayload() method before submission. Trim comments to 999 characters. Correct numeric bounds. Replace invalid identifiers with verified Genesys Cloud resource UUIDs.
• Code showing the fix: The Zod schema and heuristic checks in validatePayload() throw descriptive errors before the HTTP request executes, allowing you to correct the data locally.

Error: 429 Too Many Requests

• What causes it: The feedback submission rate exceeds the Genesys Cloud API limit for the tenant or the specific endpoint. Rate-limit cascades occur when multiple agents or automated processes submit concurrently.
• How to fix it: Implement exponential backoff. Read the Retry-After header. Throttle batch operations to 5 requests per second per Service Account.
• Code showing the fix: The submitFeedbackAtomically() function parses Retry-After, applies exponential backoff, and retries up to three times. The complete example inherits this behavior through axios timeout and status validation.

Official References

• POST /api/v2/agentassist/feedback Documentation
• OAuth 2.0 Client Credentials Flow
• @genesyscloud/purecloud-platform-client-v2 SDK Reference
• Agent Assist API Rate Limits and Best Practices