Evaluating Genesys Cloud LLM Gateway API Response Latency via TypeScript

What You Will Build

You will build a TypeScript module that submits LLM Gateway evaluation payloads, measures exact response latency, validates against inference engine constraints, calculates automatic percentiles, detects cold starts and network jitter, synchronizes with external observability webhooks, tracks measurement success rates, generates structured audit logs, and exposes a programmatic latency evaluator for automated Genesys Cloud management.

Prerequisites

• OAuth client credentials flow with scopes: ai:llm-gateway:read ai:llm-gateway:write
• Genesys Cloud API version: v2
• Node.js runtime: v18.0.0 or higher
• Dependencies: npm install axios typescript @types/node
• TypeScript configuration: target: ES2020, module: commonjs, strict: true

Authentication Setup

Genesys Cloud requires OAuth 2.0 client credentials authentication. The following code fetches an access token, caches it, and handles token expiration. The token request targets /oauth/token and requires no special scopes for the token endpoint itself, but the returned token must carry ai:llm-gateway:read ai:llm-gateway:write for subsequent API calls.

import axios, { AxiosError } from 'axios';

interface OauthConfig {
  clientId: string;
  clientSecret: string;
  environment: string;
}

interface TokenResponse {
  access_token: string;
  expires_in: number;
  token_type: string;
  scope: string;
}

class AuthManager {
  private token: string | null = null;
  private expiresAt: number = 0;
  private config: OauthConfig;

  constructor(config: OauthConfig) {
    this.config = config;
  }

  async getToken(): Promise<string> {
    if (this.token && Date.now() < this.expiresAt) {
      return this.token;
    }

    const url = `https://${this.config.environment}/oauth/token`;
    const params = new URLSearchParams({
      grant_type: 'client_credentials',
      scope: 'ai:llm-gateway:read ai:llm-gateway:write'
    });

    try {
      const response = await axios.post<TokenResponse>(url, params, {
        auth: {
          username: this.config.clientId,
          password: this.config.clientSecret
        },
        headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
      });

      this.token = response.data.access_token;
      this.expiresAt = Date.now() + (response.data.expires_in * 1000) - 10000;
      return this.token;
    } catch (error) {
      if (error instanceof AxiosError) {
        throw new Error(`OAuth token fetch failed with status ${error.response?.status}: ${error.message}`);
      }
      throw error;
    }
  }
}

Implementation

Step 1: Initialize Client and Configure Retry Logic

The Genesys Cloud API enforces rate limits that return HTTP 429. You must implement exponential backoff retry logic before making evaluation requests. The following utility wraps axios requests and automatically retries on 429 responses up to a maximum threshold.

interface RetryOptions {
  maxRetries: number;
  baseDelayMs: number;
}

async function requestWithRetry<T>(
  requestFn: () => Promise<T>,
  options: RetryOptions = { maxRetries: 3, baseDelayMs: 1000 }
): Promise<T> {
  let lastError: Error | null = null;

  for (let attempt = 0; attempt <= options.maxRetries; attempt++) {
    try {
      return await requestFn();
    } catch (error) {
      lastError = error instanceof Error ? error : new Error(String(error));
      
      if (error instanceof AxiosError && error.response?.status === 429) {
        const delay = options.baseDelayMs * Math.pow(2, attempt);
        console.log(`Rate limit hit. Retrying in ${delay}ms (attempt ${attempt + 1}/${options.maxRetries + 1})`);
        await new Promise(resolve => setTimeout(resolve, delay));
        continue;
      }
      
      throw lastError;
    }
  }
  throw lastError;
}

Step 2: Construct and Validate Evaluation Payloads

The LLM Gateway evaluation endpoint requires a structured payload containing a request ID reference, a token matrix defining input and output expectations, and a threshold directive specifying maximum latency and measurement window limits. You must validate these fields against inference engine constraints before submission to prevent evaluation failures.

interface EvaluationPayload {
  requestId: string;
  model: string;
  tokenMatrix: {
    input: number;
    output: number;
  };
  thresholdDirective: {
    maxLatencyMs: number;
    maxWindowMs: number;
  };
  constraints: {
    maxTokens: number;
    temperature: number;
  };
}

interface ValidationRule {
  minInputTokens: number;
  maxInputTokens: number;
  maxOutputTokens: number;
  maxWindowMs: number;
}

function validatePayload(payload: EvaluationPayload, rules: ValidationRule): void {
  if (!payload.requestId || typeof payload.requestId !== 'string') {
    throw new Error('Validation failed: requestId must be a non-empty string');
  }

  if (payload.tokenMatrix.input < rules.minInputTokens || payload.tokenMatrix.input > rules.maxInputTokens) {
    throw new Error(`Validation failed: input tokens must be between ${rules.minInputTokens} and ${rules.maxInputTokens}`);
  }

  if (payload.tokenMatrix.output > rules.maxOutputTokens) {
    throw new Error(`Validation failed: output tokens exceed engine limit of ${rules.maxOutputTokens}`);
  }

  if (payload.thresholdDirective.maxWindowMs > rules.maxWindowMs) {
    throw new Error(`Validation failed: maxWindowMs exceeds platform limit of ${rules.maxWindowMs}`);
  }

  if (payload.constraints.temperature < 0 || payload.constraints.temperature > 2) {
    throw new Error('Validation failed: temperature must be between 0 and 2');
  }
}

Step 3: Submit Evaluation and Measure Latency

You will submit the validated payload to /api/v2/ai/llm-gateway/evaluations. The measurement must capture network round-trip time and processing time separately. Cold start detection compares the first request latency in a rolling window against subsequent requests. Network jitter is calculated as the standard deviation of recorded latencies.

interface LatencyRecord {
  requestId: string;
  latencyMs: number;
  timestamp: number;
  success: boolean;
  coldStart: boolean;
}

class LatencyProfiler {
  private records: LatencyRecord[] = [];
  private windowStart: number = 0;
  private readonly windowMs = 300000; // 5 minute rolling window

  constructor() {
    this.resetWindow();
  }

  private resetWindow(): void {
    this.windowStart = Date.now();
  }

  addRecord(record: LatencyRecord): void {
    const now = Date.now();
    if (now - this.windowStart > this.windowMs) {
      this.resetWindow();
    }
    this.records.push(record);
  }

  getColdStartStatus(): boolean {
    return this.records.length === 0;
  }

  calculateJitter(): number {
    if (this.records.length < 2) return 0;
    const latencies = this.records.map(r => r.latencyMs);
    const mean = latencies.reduce((a, b) => a + b, 0) / latencies.length;
    const variance = latencies.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / latencies.length;
    return Math.sqrt(variance);
  }

  calculatePercentiles(): { p50: number; p95: number; p99: number } {
    const latencies = this.records.map(r => r.latencyMs).sort((a, b) => a - b);
    if (latencies.length === 0) return { p50: 0, p95: 0, p99: 0 };
    
    const p50 = latencies[Math.floor(latencies.length * 0.5)];
    const p95 = latencies[Math.floor(latencies.length * 0.95)] || latencies[latencies.length - 1];
    const p99 = latencies[Math.floor(latencies.length * 0.99)] || latencies[latencies.length - 1];
    
    return { p50, p95, p99 };
  }

  getRecords(): LatencyRecord[] {
    return this.records;
  }
}

Step 4: Process Results, Calculate Percentiles, and Sync Observability

After receiving the evaluation response, you must verify the format, trigger automatic percentile calculation, synchronize with external observability stacks via webhooks, track success rates, and generate audit logs. The following code demonstrates the complete evaluation loop with pagination support for historical data retrieval.

interface EvaluationResult {
  id: string;
  status: string;
  latencyMs: number;
  tokensProcessed: number;
  timestamp: string;
}

interface WebhookPayload {
  event: string;
  latencyMs: number;
  success: boolean;
  percentiles: { p50: number; p95: number; p99: number };
  jitterMs: number;
  timestamp: string;
}

interface AuditLogEntry {
  action: string;
  requestId: string;
  status: string;
  latencyMs: number | null;
  timestamp: string;
}

class LlmGatewayLatencyEvaluator {
  private auth: AuthManager;
  private profiler: LatencyProfiler;
  private webhookUrl: string;
  private successCount: number = 0;
  private failureCount: number = 0;
  private auditLogs: AuditLogEntry[] = [];

  constructor(environment: string, clientId: string, clientSecret: string, webhookUrl: string) {
    this.auth = new AuthManager({ environment, clientId, clientSecret });
    this.profiler = new LatencyProfiler();
    this.webhookUrl = webhookUrl;
  }

  async evaluate(payload: EvaluationPayload): Promise<EvaluationResult> {
    validatePayload(payload, {
      minInputTokens: 1,
      maxInputTokens: 8000,
      maxOutputTokens: 4096,
      maxWindowMs: 60000
    });

    const token = await this.auth.getToken();
    const baseUrl = `https://${this.auth.config?.environment || 'mycompany'}${environment}/api/v2/ai/llm-gateway/evaluations`;
    const url = `https://${this.auth.config.environment}/api/v2/ai/llm-gateway/evaluations`;
    
    const startTime = Date.now();
    const isColdStart = this.profiler.getColdStartStatus();

    try {
      const result = await requestWithRetry(async () => {
        const response = await axios.post<EvaluationResult>(url, payload, {
          headers: {
            Authorization: `Bearer ${token}`,
            'Content-Type': 'application/json'
          }
        });
        return response.data;
      });

      const latencyMs = Date.now() - startTime;
      const success = result.status === 'completed' || result.status === 'success';
      
      this.profiler.addRecord({
        requestId: payload.requestId,
        latencyMs,
        timestamp: Date.now(),
        success,
        coldStart: isColdStart
      });

      if (success) this.successCount++;
      else this.failureCount++;

      this.auditLogs.push({
        action: 'llm_gateway_evaluation',
        requestId: payload.requestId,
        status: success ? 'success' : 'failed',
        latencyMs,
        timestamp: new Date().toISOString()
      });

      await this.syncObservability(latencyMs, success);
      return result;
    } catch (error) {
      const latencyMs = Date.now() - startTime;
      this.failureCount++;
      
      this.auditLogs.push({
        action: 'llm_gateway_evaluation',
        requestId: payload.requestId,
        status: 'error',
        latencyMs,
        timestamp: new Date().toISOString()
      });

      throw error;
    }
  }

  private async syncObservability(latencyMs: number, success: boolean): Promise<void> {
    const percentiles = this.profiler.calculatePercentiles();
    const jitter = this.profiler.calculateJitter();

    const webhookPayload: WebhookPayload = {
      event: 'llm_latency_evaluated',
      latencyMs,
      success,
      percentiles,
      jitterMs: jitter,
      timestamp: new Date().toISOString()
    };

    try {
      await axios.post(this.webhookUrl, webhookPayload, {
        headers: { 'Content-Type': 'application/json' },
        timeout: 5000
      });
    } catch (error) {
      console.error('Webhook sync failed:', error);
    }
  }

  async getEvaluationHistory(page: number = 1, pageSize: number = 20): Promise<EvaluationResult[]> {
    const token = await this.auth.getToken();
    const url = `https://${this.auth.config.environment}/api/v2/ai/llm-gateway/evaluations`;
    
    const params = new URLSearchParams({
      page: String(page),
      pageSize: String(pageSize)
    });

    try {
      const response = await axios.get<any>(url, {
        headers: { Authorization: `Bearer ${token}` },
        params
      });
      
      return response.data.entities || [];
    } catch (error) {
      throw new Error(`Failed to fetch evaluation history: ${error}`);
    }
  }

  getMetrics(): { successRate: number; percentiles: { p50: number; p95: number; p99: number }; jitterMs: number; auditLogs: AuditLogEntry[] } {
    const total = this.successCount + this.failureCount;
    return {
      successRate: total > 0 ? this.successCount / total : 0,
      percentiles: this.profiler.calculatePercentiles(),
      jitterMs: this.profiler.calculateJitter(),
      auditLogs: this.auditLogs
    };
  }
}

Complete Working Example

The following script demonstrates the complete workflow from initialization to evaluation execution, metric retrieval, and audit log export. Replace the placeholder credentials and webhook URL with your environment values.

import { LlmGatewayLatencyEvaluator } from './evaluator';

async function runLatencyEvaluation() {
  const evaluator = new LlmGatewayLatencyEvaluator(
    'mycompany',
    'your_client_id',
    'your_client_secret',
    'https://hooks.example.com/observability/latency'
  );

  const payload = {
    requestId: `eval-${Date.now()}-${Math.floor(Math.random() * 10000)}`,
    model: 'gpt-4-1106-preview',
    tokenMatrix: {
      input: 1024,
      output: 512
    },
    thresholdDirective: {
      maxLatencyMs: 2500,
      maxWindowMs: 30000
    },
    constraints: {
      maxTokens: 2048,
      temperature: 0.7
    }
  };

  try {
    console.log('Submitting LLM Gateway evaluation...');
    const result = await evaluator.evaluate(payload);
    console.log('Evaluation completed:', result);

    const metrics = evaluator.getMetrics();
    console.log('Performance Metrics:', JSON.stringify(metrics, null, 2));

    console.log('Fetching evaluation history (page 1)...');
    const history = await evaluator.getEvaluationHistory(1, 10);
    console.log('History retrieved:', history.length, 'records');

  } catch (error) {
    console.error('Evaluation pipeline failed:', error);
  }
}

runLatencyEvaluation();

Common Errors & Debugging

Error: HTTP 401 Unauthorized

• Cause: The OAuth token is expired, malformed, or missing required scopes.
• Fix: Verify that ai:llm-gateway:read ai:llm-gateway:write are included in the token request scope. Ensure the AuthManager cache expiration buffer accounts for clock drift.
• Code Fix: The AuthManager class already implements a 10-second expiration buffer. If failures persist, log the token response scope and compare it against your client configuration.

Error: HTTP 403 Forbidden

• Cause: The OAuth client lacks permission to access the LLM Gateway evaluation endpoint, or the organization has disabled AI evaluation features.
• Fix: Contact your Genesys Cloud administrator to verify the ai:llm-gateway scopes are granted to the client credentials. Confirm that the evaluation feature is enabled in the organization settings.

Error: HTTP 429 Too Many Requests

• Cause: The API rate limit has been exceeded. Genesys Cloud enforces per-client and per-tenant limits.
• Fix: The requestWithRetry utility handles automatic exponential backoff. If failures continue, reduce the evaluation submission frequency or implement a token bucket rate limiter in your application layer.
• Code Fix: The retry wrapper already implements backoff with baseDelayMs * Math.pow(2, attempt). Increase maxRetries to 5 if your workload requires higher resilience.

Error: HTTP 400 Bad Request (Schema Validation)

• Cause: The evaluation payload violates inference engine constraints or exceeds maximum measurement window limits.
• Fix: Verify that tokenMatrix.input and tokenMatrix.output fall within the validated ranges. Ensure thresholdDirective.maxWindowMs does not exceed the platform limit of 60000ms.
• Code Fix: The validatePayload function enforces these constraints before submission. Adjust the ValidationRule object to match your specific model configuration.

Error: HTTP 5xx Internal Server Error

• Cause: Temporary Genesys Cloud infrastructure failure or inference engine overload.
• Fix: Implement circuit breaker logic for consecutive 5xx responses. The requestWithRetry function currently retries 429s only. Extend it to retry 5xx responses with a longer backoff interval if your SLA permits.

Official References

• Genesys Cloud OAuth 2.0 Documentation
• Genesys Cloud LLM Gateway API Reference
• Genesys Cloud JavaScript SDK Documentation
• Genesys Cloud Rate Limiting Guidelines