Monitoring NICE Cognigy Webhook Endpoint Health Metrics via REST API with TypeScript

What You Will Build

A TypeScript service that constructs health query payloads, retrieves streaming metric data via range requests, calculates delivery success rates, and exposes an automated monitoring endpoint. This implementation uses the NICE Cognigy.AI REST API. The tutorial covers TypeScript with Node.js 18+.

Prerequisites

• OAuth2 Client Credentials flow with webhooks:read and monitoring:read scopes
• Cognigy.AI API v2
• Node.js 18+ runtime with native fetch support
• External dependencies: npm install express zod axios @types/node @types/express
• TypeScript compiler configuration targeting ES2022 with module: NodeNext

Authentication Setup

Cognigy.AI uses OAuth2 Bearer tokens for API access. The authentication flow requires a client ID and client secret issued through the Cognigy administration console. Token caching prevents unnecessary authentication requests and reduces load on the identity provider.

import axios from 'axios';

const COGNIGY_BASE_URL = 'https://api.cognigy.ai/api/v2';
const COGNIGY_AUTH_URL = 'https://auth.cognigy.ai/oauth/token';

interface AuthConfig {
  clientId: string;
  clientSecret: string;
  grantType: 'client_credentials';
  scope: string;
}

let cachedToken: string | null = null;
let tokenExpiry: number | null = null;

async function acquireBearerToken(config: AuthConfig): Promise<string> {
  if (cachedToken && tokenExpiry && Date.now() < tokenExpiry - 60000) {
    return cachedToken;
  }

  const response = await axios.post(COGNIGY_AUTH_URL, new URLSearchParams({
    client_id: config.clientId,
    client_secret: config.clientSecret,
    grant_type: config.grantType,
    scope: config.scope
  }));

  const data = response.data;
  cachedToken = data.access_token;
  tokenExpiry = Date.now() + (data.expires_in * 1000);
  return cachedToken;
}

The token cache checks expiration with a sixty second buffer to prevent mid-request authentication failures. The axios client posts form-encoded credentials to the authorization endpoint and extracts the access_token and expires_in fields.

Implementation

Step 1: Construct Health Query Payloads with Endpoint Arrays and Threshold Matrices

The Cognigy health query endpoint accepts structured JSON payloads that define which webhooks to monitor, acceptable latency boundaries, and specific HTTP failure codes to track. The payload structure must match the schema expected by /api/v2/webhooks/health/query.

import { z } from 'zod';

const HealthQuerySchema = z.object({
  endpointIds: z.array(z.string().uuid()),
  latencyThresholds: z.object({
    p50: z.number().int().positive(),
    p90: z.number().int().positive(),
    p95: z.number().int().positive(),
    p99: z.number().int().positive()
  }),
  failureCodes: z.array(z.number().int().min(400).max(599)),
  timeWindow: z.object({
    start: z.string().datetime(),
    end: z.string().datetime()
  })
});

interface HealthQueryPayload {
  endpointIds: string[];
  latencyThresholds: { p50: number; p90: number; p95: number; p99: number };
  failureCodes: number[];
  timeWindow: { start: string; end: string };
}

function constructHealthQuery(
  endpointIds: string[],
  thresholds: { p50: number; p90: number; p95: number; p99: number },
  failureCodes: number[]
): HealthQueryPayload {
  const now = new Date();
  const thirtyDaysAgo = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000);

  const payload: HealthQueryPayload = {
    endpointIds,
    latencyThresholds: thresholds,
    failureCodes,
    timeWindow: {
      start: thirtyDaysAgo.toISOString(),
      end: now.toISOString()
    }
  };

  HealthQuerySchema.parse(payload);
  return payload;
}

The zod schema validates the payload before transmission. The latency threshold matrix defines percentile boundaries that Cognigy uses to calculate delivery health. The time window defaults to a thirty day retention period, which aligns with standard Cognigy metric storage policies.

Step 2: Validate Query Schemas Against Concurrent Request Quotas and Retention Policies

Cognigy enforces rate limits and metric retention windows. The client must inspect response headers and validate the requested time window before submitting the query. Exceeding retention policies returns a 422 Unprocessable Entity response.

interface RateLimitHeaders {
  'x-ratelimit-limit': string;
  'x-ratelimit-remaining': string;
  'x-ratelimit-reset': string;
}

function validateQuotaAndRetention(headers: Headers): void {
  const remaining = headers.get('x-ratelimit-remaining');
  const retentionLimit = headers.get('x-metric-retention-days');

  if (remaining && parseInt(remaining, 10) <= 2) {
    throw new Error('Concurrent request quota threshold reached. Backoff required.');
  }

  if (retentionLimit && parseInt(retentionLimit, 10) < 30) {
    throw new Error('Requested time window exceeds configured metric retention policy.');
  }
}

async function validateQueryEndpoint(token: string): Promise<void> {
  const response = await fetch(`${COGNIGY_BASE_URL}/webhooks/health/validate`, {
    method: 'HEAD',
    headers: {
      'Authorization': `Bearer ${token}`,
      'Content-Type': 'application/json'
    }
  });

  validateQuotaAndRetention(response.headers);

  if (!response.ok) {
    throw new Error(`Validation failed with status ${response.status}`);
  }
}

The validation call uses a HEAD request to retrieve quota headers without consuming bandwidth. The function checks x-ratelimit-remaining to prevent cascading 429 errors. It also verifies x-metric-retention-days to ensure the query window aligns with storage policies.

Step 3: Handle Health Retrieval via Streaming GET Operations with Range Request Support

Large metric extractions require HTTP range requests to prevent memory exhaustion. Cognigy supports Range: bytes=start-end headers on the health query endpoint. The client must issue sequential range requests and reassemble the response chunks.

async function streamHealthMetrics(
  token: string,
  payload: HealthQueryPayload
): Promise<string> {
  const url = `${COGNIGY_BASE_URL}/webhooks/health/query`;
  let assembledData = '';
  let startByte = 0;
  const chunkSize = 65536; // 64KB chunks

  while (true) {
    const response = await fetch(url, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${token}`,
        'Content-Type': 'application/json',
        'Range': `bytes=${startByte}-`
      },
      body: JSON.stringify(payload)
    });

    if (response.status === 416) {
      break; // Range not satisfiable, data fully retrieved
    }

    if (!response.ok) {
      throw new Error(`Stream retrieval failed: ${response.status} ${response.statusText}`);
    }

    const contentRange = response.headers.get('Content-Range');
    if (!contentRange) {
      throw new Error('Missing Content-Range header in streaming response');
    }

    const chunkBuffer = await response.arrayBuffer();
    const textDecoder = new TextDecoder('utf-8');
    assembledData += textDecoder.decode(chunkBuffer);
    startByte += chunkBuffer.byteLength;

    if (response.headers.get('Content-Length') && 
        parseInt(response.headers.get('Content-Length')!, 10) < chunkSize) {
      break; // Final chunk received
    }
  }

  return assembledData;
}

The loop issues POST requests with incremental Range headers. The Content-Range header indicates the current byte position. The function decodes each chunk using TextDecoder and appends it to the accumulator. The loop terminates when the server returns 416 Range Not Satisfiable or when a chunk is smaller than the requested size.

Step 4: Implement Health Analysis Logic Using Error Rate Trending and Delivery Success Calculation

The raw metric payload contains timestamped delivery records. The analysis pipeline calculates delivery success rates and trends error frequencies across sliding time windows.

interface MetricRecord {
  timestamp: string;
  endpointId: string;
  statusCode: number;
  latencyMs: number;
  success: boolean;
}

interface HealthAnalysisResult {
  deliverySuccessRate: number;
  errorRateTrend: 'increasing' | 'stable' | 'decreasing';
  averageLatency: number;
  criticalFailures: number;
}

function analyzeHealthMetrics(rawJson: string): HealthAnalysisResult {
  const records: MetricRecord[] = JSON.parse(rawJson);
  const totalRecords = records.length;
  const successfulDeliveries = records.filter(r => r.success).length;
  const deliverySuccessRate = totalRecords > 0 ? (successfulDeliveries / totalRecords) * 100 : 0;

  const latencies = records.map(r => r.latencyMs);
  const averageLatency = latencies.reduce((a, b) => a + b, 0) / (latencies.length || 1);

  const criticalFailures = records.filter(r => r.statusCode >= 500).length;

  // Calculate error rate trend using first and second half comparison
  const midPoint = Math.floor(totalRecords / 2);
  const firstHalfErrors = records.slice(0, midPoint).filter(r => !r.success).length;
  const secondHalfErrors = records.slice(midPoint).filter(r => !r.success).length;

  let errorRateTrend: 'increasing' | 'stable' | 'decreasing' = 'stable';
  if (secondHalfErrors > firstHalfErrors * 1.2) {
    errorRateTrend = 'increasing';
  } else if (secondHalfErrors < firstHalfErrors * 0.8) {
    errorRateTrend = 'decreasing';
  }

  return {
    deliverySuccessRate,
    errorRateTrend,
    averageLatency,
    criticalFailures
  };
}

The analysis function parses the reassembled JSON into typed records. It calculates the delivery success rate by dividing successful records by the total count. The error rate trend compares the first half of the time window against the second half using a twenty percent deviation threshold. This sliding window approach detects connectivity degradation before it triggers full service outages.

Step 5: Synchronize Health Alert Events with External Monitoring Platforms

When the analysis pipeline detects an increasing error trend or success rate drops below ninety percent, the system must dispatch alert events to external monitoring platforms. The synchronization uses standard webhook callbacks with structured payloads.

interface AlertEvent {
  eventType: 'health_degradation' | 'connectivity_failure';
  timestamp: string;
  endpointId: string;
  metrics: HealthAnalysisResult;
  severity: 'warning' | 'critical';
}

async function dispatchHealthAlert(token: string, alert: AlertEvent): Promise<void> {
  const cognigyAlertEndpoint = `${COGNIGY_BASE_URL}/monitoring/alerts/sync`;
  const externalWebhookUrl = process.env.EXTERNAL_MONITORING_WEBHOOK;

  const payload = {
    ...alert,
    source: 'cognigy-health-monitor',
    propagatedAt: new Date().toISOString()
  };

  // Sync with Cognigy internal alerting
  await fetch(cognigyAlertEndpoint, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${token}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify(payload)
  });

  // Forward to external monitoring platform
  if (externalWebhookUrl) {
    await fetch(externalWebhookUrl, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(payload)
    });
  }
}

The dispatch function posts the alert to Cognigy’s internal synchronization endpoint and forwards the same payload to an external monitoring webhook. The EXTERAL_MONITORING_WEBHOOK environment variable allows infrastructure teams to route alerts to Datadog, PagerDuty, or custom dashboards.

Step 6: Track Retrieval Latency, Generate Audit Logs, and Expose Health Monitor

Production systems require operational telemetry. The monitor tracks API retrieval latency, calculates accuracy rates, writes audit logs for compliance, and exposes an HTTP endpoint for automated reliability management.

import express from 'express';
import fs from 'fs';

const app = express();
const AUDIT_LOG_PATH = './cognigy-health-audit.log';

interface AuditEntry {
  timestamp: string;
  action: string;
  requestPayload: object;
  responseLatencyMs: number;
  accuracyRate: number;
  status: 'success' | 'failure';
}

function writeAuditLog(entry: AuditEntry): void {
  const logLine = `${entry.timestamp} | ${entry.action} | Latency: ${entry.responseLatencyMs}ms | Accuracy: ${entry.accuracyRate}% | Status: ${entry.status}\n`;
  fs.appendFileSync(AUDIT_LOG_PATH, logLine, 'utf-8');
}

app.post('/health/monitor', async (req, res) => {
  const startTime = Date.now();
  try {
    const token = await acquireBearerToken({
      clientId: process.env.COGNIGY_CLIENT_ID!,
      clientSecret: process.env.COGNIGY_CLIENT_SECRET!,
      grantType: 'client_credentials',
      scope: 'webhooks:read monitoring:read'
    });

    await validateQueryEndpoint(token);

    const queryPayload = constructHealthQuery(
      ['550e8400-e29b-41d4-a716-446655440000'],
      { p50: 200, p90: 400, p95: 600, p99: 1000 },
      [500, 502, 503, 504]
    );

    const rawMetrics = await streamHealthMetrics(token, queryPayload);
    const analysis = analyzeHealthMetrics(rawMetrics);

    const retrievalLatency = Date.now() - startTime;
    const accuracyRate = analysis.deliverySuccessRate;

    const auditEntry: AuditEntry = {
      timestamp: new Date().toISOString(),
      action: 'health_monitor_execution',
      requestPayload: queryPayload,
      responseLatencyMs: retrievalLatency,
      accuracyRate,
      status: analysis.deliverySuccessRate >= 90 ? 'success' : 'failure'
    };

    writeAuditLog(auditEntry);

    if (analysis.errorRateTrend === 'increasing' || analysis.deliverySuccessRate < 90) {
      await dispatchHealthAlert(token, {
        eventType: 'health_degradation',
        timestamp: new Date().toISOString(),
        endpointId: queryPayload.endpointIds[0],
        metrics: analysis,
        severity: analysis.deliverySuccessRate < 80 ? 'critical' : 'warning'
      });
    }

    res.json({
      status: 'completed',
      analysis,
      retrievalLatencyMs: retrievalLatency,
      auditLogged: true
    });
  } catch (error) {
    const retrievalLatency = Date.now() - startTime;
    writeAuditLog({
      timestamp: new Date().toISOString(),
      action: 'health_monitor_execution',
      requestPayload: {},
      responseLatencyMs: retrievalLatency,
      accuracyRate: 0,
      status: 'failure'
    });

    res.status(500).json({ error: error instanceof Error ? error.message : 'Unknown failure' });
  }
});

export default app;

The Express route handles the full lifecycle: authentication, validation, streaming retrieval, analysis, alerting, and audit logging. The writeAuditLog function appends structured entries to a local file for security governance compliance. The route returns a JSON response containing the analysis results and operational telemetry.

Complete Working Example

import express from 'express';
import fs from 'fs';
import { z } from 'zod';
import axios from 'axios';

const COGNIGY_BASE_URL = 'https://api.cognigy.ai/api/v2';
const COGNIGY_AUTH_URL = 'https://auth.cognigy.ai/oauth/token';
const AUDIT_LOG_PATH = './cognigy-health-audit.log';

interface AuthConfig {
  clientId: string;
  clientSecret: string;
  grantType: 'client_credentials';
  scope: string;
}

interface HealthQueryPayload {
  endpointIds: string[];
  latencyThresholds: { p50: number; p90: number; p95: number; p99: number };
  failureCodes: number[];
  timeWindow: { start: string; end: string };
}

interface MetricRecord {
  timestamp: string;
  endpointId: string;
  statusCode: number;
  latencyMs: number;
  success: boolean;
}

interface HealthAnalysisResult {
  deliverySuccessRate: number;
  errorRateTrend: 'increasing' | 'stable' | 'decreasing';
  averageLatency: number;
  criticalFailures: number;
}

interface AlertEvent {
  eventType: 'health_degradation' | 'connectivity_failure';
  timestamp: string;
  endpointId: string;
  metrics: HealthAnalysisResult;
  severity: 'warning' | 'critical';
}

interface AuditEntry {
  timestamp: string;
  action: string;
  requestPayload: object;
  responseLatencyMs: number;
  accuracyRate: number;
  status: 'success' | 'failure';
}

let cachedToken: string | null = null;
let tokenExpiry: number | null = null;

async function acquireBearerToken(config: AuthConfig): Promise<string> {
  if (cachedToken && tokenExpiry && Date.now() < tokenExpiry - 60000) {
    return cachedToken;
  }

  const response = await axios.post(COGNIGY_AUTH_URL, new URLSearchParams({
    client_id: config.clientId,
    client_secret: config.clientSecret,
    grant_type: config.grantType,
    scope: config.scope
  }));

  const data = response.data;
  cachedToken = data.access_token;
  tokenExpiry = Date.now() + (data.expires_in * 1000);
  return cachedToken;
}

const HealthQuerySchema = z.object({
  endpointIds: z.array(z.string().uuid()),
  latencyThresholds: z.object({
    p50: z.number().int().positive(),
    p90: z.number().int().positive(),
    p95: z.number().int().positive(),
    p99: z.number().int().positive()
  }),
  failureCodes: z.array(z.number().int().min(400).max(599)),
  timeWindow: z.object({
    start: z.string().datetime(),
    end: z.string().datetime()
  })
});

function constructHealthQuery(
  endpointIds: string[],
  thresholds: { p50: number; p90: number; p95: number; p99: number },
  failureCodes: number[]
): HealthQueryPayload {
  const now = new Date();
  const thirtyDaysAgo = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000);

  const payload: HealthQueryPayload = {
    endpointIds,
    latencyThresholds: thresholds,
    failureCodes,
    timeWindow: { start: thirtyDaysAgo.toISOString(), end: now.toISOString() }
  };

  HealthQuerySchema.parse(payload);
  return payload;
}

async function validateQueryEndpoint(token: string): Promise<void> {
  const response = await fetch(`${COGNIGY_BASE_URL}/webhooks/health/validate`, {
    method: 'HEAD',
    headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' }
  });

  const remaining = response.headers.get('x-ratelimit-remaining');
  if (remaining && parseInt(remaining, 10) <= 2) {
    throw new Error('Concurrent request quota threshold reached. Backoff required.');
  }

  if (!response.ok) {
    throw new Error(`Validation failed with status ${response.status}`);
  }
}

async function streamHealthMetrics(token: string, payload: HealthQueryPayload): Promise<string> {
  const url = `${COGNIGY_BASE_URL}/webhooks/health/query`;
  let assembledData = '';
  let startByte = 0;
  const chunkSize = 65536;

  while (true) {
    const response = await fetch(url, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${token}`,
        'Content-Type': 'application/json',
        'Range': `bytes=${startByte}-`
      },
      body: JSON.stringify(payload)
    });

    if (response.status === 416) break;
    if (!response.ok) throw new Error(`Stream retrieval failed: ${response.status}`);
    if (!response.headers.get('Content-Range')) throw new Error('Missing Content-Range header');

    const chunkBuffer = await response.arrayBuffer();
    assembledData += new TextDecoder('utf-8').decode(chunkBuffer);
    startByte += chunkBuffer.byteLength;

    if (response.headers.get('Content-Length') && 
        parseInt(response.headers.get('Content-Length')!, 10) < chunkSize) {
      break;
    }
  }

  return assembledData;
}

function analyzeHealthMetrics(rawJson: string): HealthAnalysisResult {
  const records: MetricRecord[] = JSON.parse(rawJson);
  const totalRecords = records.length;
  const successfulDeliveries = records.filter(r => r.success).length;
  const deliverySuccessRate = totalRecords > 0 ? (successfulDeliveries / totalRecords) * 100 : 0;
  const latencies = records.map(r => r.latencyMs);
  const averageLatency = latencies.reduce((a, b) => a + b, 0) / (latencies.length || 1);
  const criticalFailures = records.filter(r => r.statusCode >= 500).length;

  const midPoint = Math.floor(totalRecords / 2);
  const firstHalfErrors = records.slice(0, midPoint).filter(r => !r.success).length;
  const secondHalfErrors = records.slice(midPoint).filter(r => !r.success).length;

  let errorRateTrend: 'increasing' | 'stable' | 'decreasing' = 'stable';
  if (secondHalfErrors > firstHalfErrors * 1.2) errorRateTrend = 'increasing';
  else if (secondHalfErrors < firstHalfErrors * 0.8) errorRateTrend = 'decreasing';

  return { deliverySuccessRate, errorRateTrend, averageLatency, criticalFailures };
}

async function dispatchHealthAlert(token: string, alert: AlertEvent): Promise<void> {
  const cognigyAlertEndpoint = `${COGNIGY_BASE_URL}/monitoring/alerts/sync`;
  const externalWebhookUrl = process.env.EXTERNAL_MONITORING_WEBHOOK;

  const payload = { ...alert, source: 'cognigy-health-monitor', propagatedAt: new Date().toISOString() };

  await fetch(cognigyAlertEndpoint, {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(payload)
  });

  if (externalWebhookUrl) {
    await fetch(externalWebhookUrl, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify(payload)
    });
  }
}

function writeAuditLog(entry: AuditEntry): void {
  const logLine = `${entry.timestamp} | ${entry.action} | Latency: ${entry.responseLatencyMs}ms | Accuracy: ${entry.accuracyRate}% | Status: ${entry.status}\n`;
  fs.appendFileSync(AUDIT_LOG_PATH, logLine, 'utf-8');
}

const app = express();

app.post('/health/monitor', async (req, res) => {
  const startTime = Date.now();
  try {
    const token = await acquireBearerToken({
      clientId: process.env.COGNIGY_CLIENT_ID!,
      clientSecret: process.env.COGNIGY_CLIENT_SECRET!,
      grantType: 'client_credentials',
      scope: 'webhooks:read monitoring:read'
    });

    await validateQueryEndpoint(token);

    const queryPayload = constructHealthQuery(
      ['550e8400-e29b-41d4-a716-446655440000'],
      { p50: 200, p90: 400, p95: 600, p99: 1000 },
      [500, 502, 503, 504]
    );

    const rawMetrics = await streamHealthMetrics(token, queryPayload);
    const analysis = analyzeHealthMetrics(rawMetrics);

    const retrievalLatency = Date.now() - startTime;
    writeAuditLog({
      timestamp: new Date().toISOString(),
      action: 'health_monitor_execution',
      requestPayload: queryPayload,
      responseLatencyMs: retrievalLatency,
      accuracyRate: analysis.deliverySuccessRate,
      status: analysis.deliverySuccessRate >= 90 ? 'success' : 'failure'
    });

    if (analysis.errorRateTrend === 'increasing' || analysis.deliverySuccessRate < 90) {
      await dispatchHealthAlert(token, {
        eventType: 'health_degradation',
        timestamp: new Date().toISOString(),
        endpointId: queryPayload.endpointIds[0],
        metrics: analysis,
        severity: analysis.deliverySuccessRate < 80 ? 'critical' : 'warning'
      });
    }

    res.json({ status: 'completed', analysis, retrievalLatencyMs: retrievalLatency, auditLogged: true });
  } catch (error) {
    const retrievalLatency = Date.now() - startTime;
    writeAuditLog({
      timestamp: new Date().toISOString(),
      action: 'health_monitor_execution',
      requestPayload: {},
      responseLatencyMs: retrievalLatency,
      accuracyRate: 0,
      status: 'failure'
    });
    res.status(500).json({ error: error instanceof Error ? error.message : 'Unknown failure' });
  }
});

app.listen(3000, () => console.log('Health monitor listening on port 3000'));

export default app;

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The Bearer token is expired, malformed, or lacks the required webhooks:read or monitoring:read scopes.
• How to fix it: Verify the client credentials in the environment variables. Ensure the token cache expiration buffer accounts for network latency. Revoke and regenerate credentials if the client secret was rotated.
• Code showing the fix:

// Add explicit scope validation during token acquisition
if (!response.data.scope.includes('webhooks:read')) {
  throw new Error('Missing required webhooks:read scope in OAuth response');
}

Error: 429 Too Many Requests

• What causes it: The client exceeded Cognigy rate limits during concurrent health queries or rapid polling cycles.
• How to fix it: Implement exponential backoff with jitter. Check x-ratelimit-reset headers to align retry timing with server windows.
• Code showing the fix:

async function fetchWithRetry(url: string, options: RequestInit, maxRetries = 3): Promise<Response> {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    const response = await fetch(url, options);
    if (response.status !== 429) return response;
    
    const resetTime = parseInt(response.headers.get('x-ratelimit-reset') || '60', 10);
    const jitter = Math.random() * 1000;
    await new Promise(resolve => setTimeout(resolve, (resetTime * 1000) + jitter));
  }
  throw new Error('Max retries exceeded for 429 rate limit');
}

Error: 416 Range Not Satisfiable

• What causes it: The Range header requests bytes beyond the total payload size, or the server does not support range requests for the requested resource.
• How to fix it: Validate the Content-Length header before issuing the next range request. Fall back to standard non-streaming retrieval if the payload is smaller than the chunk threshold.
• Code showing the fix:

const contentLength = response.headers.get('Content-Length');
if (contentLength && parseInt(contentLength, 10) < chunkSize) {
  // Payload fits in memory, abort streaming and return full response
  return new TextDecoder('utf-8').decode(await response.arrayBuffer());
}

Error: 422 Unprocessable Entity

• What causes it: The query payload violates schema constraints or requests a time window exceeding the configured metric retention policy.
• How to fix it: Run the payload through the Zod schema before transmission. Adjust the timeWindow start date to align with the x-metric-retention-days header value.
• Code showing the fix:

// Dynamic retention adjustment
const retentionDays = parseInt(headers.get('x-metric-retention-days') || '30', 10);
const adjustedStart = new Date(Date.now() - (retentionDays * 24 * 60 * 60 * 1000)).toISOString();
payload.timeWindow.start = adjustedStart;

Official References

• NICE Cognigy.AI REST API Documentation
• Webhook Management Endpoints
• Monitoring and Metrics API
• OAuth2 Authentication Guide
• HTTP Range Requests Specification