Executing Genesys Cloud LLM Gateway Evaluation Runs via REST API with Node.js

How-To-Articles
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Executing Genesys Cloud LLM Gateway Evaluation Runs via REST API with Node.js

What You Will Build

  • A production-grade Node.js executor that initiates, validates, and tracks LLM Gateway evaluation runs in Genesys Cloud.
  • Uses the /api/v2/ai/llm/evaluations REST endpoint with direct HTTP requests and strict schema validation.
  • Covers TypeScript/Node.js with automated metric aggregation, privacy masking pipelines, webhook synchronization, and governance audit logging.

Prerequisites

  • OAuth2 client credentials grant type registered in Genesys Cloud
  • Required scopes: ai:read, ai:write, ai:evaluation:run, webhook:write, analytics:read
  • Node.js 18 LTS or higher
  • Dependencies: axios, zod, express, dotenv, uuid
  • Genesys Cloud environment with AI/LLM Gateway and evaluation features enabled

Authentication Setup

Genesys Cloud requires OAuth2 client credentials authentication for server-to-server API access. The following implementation caches the access token and refreshes it automatically when it expires. The token lifecycle is managed outside the request flow to prevent unnecessary network overhead.

import axios, { AxiosInstance, AxiosRequestConfig } from 'axios';
import dotenv from 'dotenv';

dotenv.config();

const GENESYS_BASE_URL = process.env.GENESYS_BASE_URL || 'https://api.mypurecloud.com';
const OAUTH_TOKEN_URL = `${GENESYS_BASE_URL}/oauth/token`;

interface TokenCache {
  accessToken: string;
  expiresAt: number;
}

let tokenCache: TokenCache | null = null;

async function getAccessToken(): Promise<string> {
  const now = Date.now();
  
  if (tokenCache && tokenCache.expiresAt > now + 60_000) {
    return tokenCache.accessToken;
  }

  try {
    const response = await axios.post(
      OAUTH_TOKEN_URL,
      '',
      {
        auth: {
          username: process.env.GENESYS_CLIENT_ID!,
          password: process.env.GENESYS_CLIENT_SECRET!,
        },
        params: { grant_type: 'client_credentials' },
        headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
        timeout: 10_000,
      }
    );

    const data = response.data as { access_token: string; expires_in: number };
    tokenCache = {
      accessToken: data.access_token,
      expiresAt: now + (data.expires_in * 1000),
    };

    return data.access_token;
  } catch (error) {
    if (axios.isAxiosError(error)) {
      console.error('OAuth token request failed:', error.response?.data || error.message);
    }
    throw new Error('Authentication failed. Verify client credentials and scopes.');
  }
}

function createAuthenticatedClient(): AxiosInstance {
  const client = axios.create({
    baseURL: GENESYS_BASE_URL,
    headers: { 'Content-Type': 'application/json' },
  });

  client.interceptors.request.use(async (config) => {
    const token = await getAccessToken();
    config.headers.Authorization = `Bearer ${token}`;
    return config;
  });

  return client;
}

Implementation

Step 1: Schema Validation and Constraint Verification

Before initiating an evaluation run, you must validate the payload structure and enforce environment constraints. Genesys Cloud enforces dataset size limits and concurrent evaluation caps to protect gateway performance. The following code uses Zod for strict runtime validation and queries the active evaluation queue to verify concurrency limits.

Required Scope: ai:read, ai:write

import { z } from 'zod';
import axios, { AxiosInstance } from 'axios';

const DatasetMatrixSchema = z.array(
  z.object({
    prompt: z.string().min(1),
    expectedOutput: z.string().min(1),
    metadata: z.record(z.string(), z.unknown()).optional(),
  })
);

const MetricDirectivesSchema = z.object({
  accuracy: z.boolean().optional().default(true),
  latency: z.boolean().optional().default(true),
  hallucination_rate: z.boolean().optional().default(false),
  toxicity_score: z.boolean().optional().default(false),
});

const EvaluationPayloadSchema = z.object({
  evaluationId: z.string().uuid(),
  datasetMatrix: DatasetMatrixSchema,
  metricCalculationDirectives: MetricDirectivesSchema,
  privacyMasking: z.object({
    enabled: z.boolean(),
    patterns: z.array(z.enum(['PII', 'PHI', 'CCN', 'EMAIL', 'PHONE'])),
  }),
  webhookUrl: z.string().url(),
});

type EvaluationPayload = z.infer<typeof EvaluationPayloadSchema>;

const MAX_DATASET_SIZE = 5000;
const MAX_CONCURRENT_EVALUATIONS = 10;

async function validateConstraints(
  payload: EvaluationPayload,
  client: AxiosInstance
): Promise<void> {
  // Validate dataset size constraint
  if (payload.datasetMatrix.length > MAX_DATASET_SIZE) {
    throw new Error(
      `Dataset matrix exceeds maximum size of ${MAX_DATASET_SIZE} records.`
    );
  }

  // Verify concurrent evaluation limits via pagination
  try {
    const runningEvals = await client.get('/api/v2/ai/llm/evaluations', {
      params: { status: 'RUNNING', limit: 100 },
    });

    const totalCount = runningEvals.data.total || runningEvals.data.entities?.length || 0;
    
    if (totalCount >= MAX_CONCURRENT_EVALUATIONS) {
      throw new Error(
        `Concurrent evaluation limit reached. Active runs: ${totalCount}. Limit: ${MAX_CONCURRENT_EVALUATIONS}.`
      );
    }
  } catch (error) {
    if (axios.isAxiosError(error) && error.response?.status === 401) {
      throw new Error('Failed to query concurrent evaluations. Token expired or missing ai:read scope.');
    }
    throw error;
  }
}

Step 2: Payload Construction and Atomic POST Initiation

Evaluation runs must be initiated through a single atomic POST operation. The request body contains the evaluation identifier, dataset matrix, metric directives, and privacy masking configuration. Genesys Cloud returns a synchronous 201 Created response with the execution tracking identifier. The following implementation includes automatic retry logic for 429 Too Many Requests responses to prevent cascading failures during high-load periods.

Required Scope: ai:evaluation:run, webhook:write

import { v4 as uuidv4 } from 'uuid';

interface ExecutionResponse {
  id: string;
  status: 'QUEUED' | 'RUNNING' | 'COMPLETED' | 'FAILED';
  startTime: string;
  completionTime: string | null;
  metricsUrl: string;
  auditLogUrl: string;
}

async function initiateEvaluationRun(
  payload: EvaluationPayload,
  client: AxiosInstance
): Promise<ExecutionResponse> {
  const maxRetries = 3;
  let attempt = 0;

  while (attempt < maxRetries) {
    try {
      const response = await client.post<ExecutionResponse>(
        '/api/v2/ai/llm/evaluations',
        payload,
        {
          headers: {
            'Idempotency-Key': uuidv4(),
            'X-Genesys-Evaluation-Version': '2.1',
          },
          timeout: 30_000,
        }
      );

      return response.data;
    } catch (error) {
      if (axios.isAxiosError(error)) {
        const status = error.response?.status;
        
        if (status === 429 && attempt < maxRetries - 1) {
          const retryAfter = error.response?.headers['retry-after'] 
            ? parseInt(error.response.headers['retry-after'], 10) 
            : Math.pow(2, attempt) * 1000;
          console.warn(`Rate limited. Retrying in ${retryAfter}ms...`);
          await new Promise((resolve) => setTimeout(resolve, retryAfter));
          attempt++;
          continue;
        }

        if (status === 400) {
          throw new Error(`Payload format verification failed: ${error.response.data.message}`);
        }
        if (status === 403) {
          throw new Error('Insufficient permissions. Verify ai:evaluation:run scope.');
        }
        if (status === 409) {
          throw new Error('Duplicate evaluation ID detected. Generate a new UUID.');
        }
      }
      throw error;
    }
  }
  throw new Error('Max retry attempts exceeded for evaluation initiation.');
}

Step 3: Execution Validation and Metric Aggregation Triggers

Once the evaluation run is queued, Genesys Cloud validates the metric schema and triggers automatic aggregation pipelines. You must verify that the requested metrics align with the dataset structure. The following code demonstrates how to validate metric schema compatibility and force early aggregation triggers for latency-critical MLOps workflows.

Required Scope: ai:read, analytics:read

async function validateMetricSchema(
  payload: EvaluationPayload,
  executionId: string,
  client: AxiosInstance
): Promise<void> {
  try {
    const validationResponse = await client.post(
      `/api/v2/ai/llm/evaluations/${executionId}/validate-metrics`,
      {
        directives: payload.metricCalculationDirectives,
        datasetSampleSize: Math.min(payload.datasetMatrix.length, 50),
      },
      { timeout: 15_000 }
    );

    const result = validationResponse.data as { compatible: boolean; warnings: string[] };
    
    if (!result.compatible) {
      throw new Error(
        `Metric schema mismatch: ${result.warnings.join(', ')}. Adjust dataset matrix or disable incompatible directives.`
      );
    }

    // Trigger automatic metric aggregation pipeline
    await client.post(
      `/api/v2/ai/llm/evaluations/${executionId}/trigger-aggregation`,
      { mode: 'async', priority: 'high' }
    );
  } catch (error) {
    if (axios.isAxiosError(error)) {
      console.error('Metric validation failed:', error.response?.data || error.message);
    }
    throw error;
  }
}

Step 4: Webhook Synchronization and Audit Logging

Evaluation completion events are delivered asynchronously to your registered webhook URL. The following Express route parses the completion payload, calculates execution latency, extracts metric accuracy rates, and generates a governance-compliant audit log. This synchronization pattern aligns evaluation results with external MLOps dashboards.

Required Scope: webhook:write, ai:read

import express, { Request, Response } from 'express';

interface WebhookPayload {
  evaluationId: string;
  status: 'COMPLETED' | 'FAILED';
  startTime: string;
  completionTime: string;
  metrics: {
    accuracy: number;
    latency_p95_ms: number;
    hallucination_rate: number;
  };
  privacyMaskingApplied: boolean;
}

const auditLogs: Record<string, any[]> = {};

function calculateExecutionLatency(startTime: string, completionTime: string): number {
  const start = new Date(startTime).getTime();
  const end = new Date(completionTime).getTime();
  return end - start;
}

export function createWebhookRouter(): express.Router {
  const router = express.Router();

  router.post('/genesys/eval-completion', (req: Request, res: Response) => {
    try {
      const payload = req.body as WebhookPayload;
      
      if (!payload.evaluationId || !payload.status) {
        res.status(400).json({ error: 'Invalid webhook payload structure.' });
        return;
      }

      const latencyMs = calculateExecutionLatency(payload.startTime, payload.completionTime);
      const accuracyRate = payload.metrics.accuracy;

      // Generate governance compliance audit log
      const auditEntry = {
        timestamp: new Date().toISOString(),
        evaluationId: payload.evaluationId,
        status: payload.status,
        executionLatencyMs: latencyMs,
        metricAccuracyRate: accuracyRate,
        privacyMaskingVerified: payload.privacyMaskingApplied,
        webhookSource: 'genesys-cloud-llm-gateway',
        complianceTag: 'AI_EVAL_V2',
      };

      if (!auditLogs[payload.evaluationId]) {
        auditLogs[payload.evaluationId] = [];
      }
      auditLogs[payload.evaluationId].push(auditEntry);

      console.log(`[AUDIT] Evaluation ${payload.evaluationId} processed. Latency: ${latencyMs}ms. Accuracy: ${accuracyRate}`);

      // Synchronize with external MLOps dashboard via side-effect
      // In production, this would call your metrics ingestion pipeline
      res.status(200).json({ acknowledged: true, logId: auditEntry.timestamp });
    } catch (error) {
      console.error('Webhook processing failed:', error);
      res.status(500).json({ error: 'Internal processing error.' });
    }
  });

  return router;
}

Complete Working Example

The following module combines authentication, validation, execution, and webhook handling into a single executable class. Configure environment variables and run the script to initiate a full evaluation lifecycle.

import { createAuthenticatedClient } from './auth';
import { validateConstraints, initiateEvaluationRun, validateMetricSchema } from './evaluation';
import { createWebhookRouter } from './webhook';
import { EvaluationPayloadSchema } from './schema';
import express from 'express';
import { v4 as uuidv4 } from 'uuid';

class LlmEvaluationExecutor {
  private client: any;
  private auditStore: Map<string, any[]> = new Map();

  constructor() {
    this.client = createAuthenticatedClient();
  }

  async runEvaluation(payload: Record<string, any>): Promise<any> {
    // 1. Schema validation
    const parsed = EvaluationPayloadSchema.parse(payload);

    // 2. Constraint verification
    await validateConstraints(parsed, this.client);

    // 3. Atomic initiation
    const execution = await initiateEvaluationRun(parsed, this.client);
    console.log(`Evaluation initiated. ID: ${execution.id} | Status: ${execution.status}`);

    // 4. Metric validation and aggregation trigger
    await validateMetricSchema(parsed, execution.id, this.client);

    return execution;
  }

  getAuditLogs(evaluationId: string): any[] {
    return this.auditStore.get(evaluationId) || [];
  }
}

// Application bootstrap
async function main() {
  const app = express();
  app.use(express.json());
  app.use(createWebhookRouter());

  const executor = new LlmEvaluationExecutor();

  const testPayload = {
    evaluationId: uuidv4(),
    datasetMatrix: [
      { prompt: 'Classify intent: I want to cancel my subscription', expectedOutput: 'cancel_subscription' },
      { prompt: 'What is my order status?', expectedOutput: 'check_order_status' },
      { prompt: 'Reset my password please', expectedOutput: 'reset_password' },
    ],
    metricCalculationDirectives: { accuracy: true, latency: true, hallucination_rate: true },
    privacyMasking: { enabled: true, patterns: ['PII', 'PHI', 'EMAIL'] },
    webhookUrl: 'https://mlops.example.com/genesys/eval-completion',
  };

  try {
    const result = await executor.runEvaluation(testPayload);
    console.log('Execution complete:', result);
  } catch (error) {
    console.error('Evaluation pipeline failed:', error);
    process.exit(1);
  }

  app.listen(3000, () => console.log('Evaluation executor listening on port 3000'));
}

main();

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: The OAuth token expired during a long-running evaluation pipeline or the client credentials lack the required scopes.
  • Fix: Verify the ai:evaluation:run and ai:read scopes are attached to the OAuth client. Implement token refresh logic before each request. The provided getAccessToken function handles automatic rotation.

Error: 400 Bad Request

  • Cause: Payload structure violates the Genesys Cloud evaluation schema. Common triggers include missing expectedOutput fields in the dataset matrix or invalid privacy masking pattern enums.
  • Fix: Run the payload through the Zod schema before transmission. Ensure all metric directives use boolean values and privacy patterns match the exact enum values provided by the API.

Error: 409 Conflict

  • Cause: Duplicate evaluationId submitted within the retention window, or concurrent evaluation limit exceeded.
  • Fix: Generate a fresh UUID for each execution run. Query /api/v2/ai/llm/evaluations?status=RUNNING to verify active capacity before submission. Implement a queueing mechanism if your workload exceeds the gateway limit.

Error: 429 Too Many Requests

  • Cause: Rate limiting triggered by rapid polling or concurrent evaluation initiations.
  • Fix: The initiateEvaluationRun function includes exponential backoff retry logic. Respect the Retry-After header when present. Throttle webhook callback processing if your MLOps dashboard becomes a bottleneck.

Error: 500 Internal Server Error

  • Cause: Genesys Cloud backend evaluation engine failure, often caused by malformed dataset matrices or unsupported metric combinations.
  • Fix: Validate metric compatibility using the /validate-metrics endpoint before submission. Reduce dataset size to isolate problematic prompts. Contact Genesys Cloud support with the execution ID if the error persists across multiple payloads.

Official References