Managing Genesys AI Models via AI API with TypeScript

What You Will Build

• You will build a TypeScript model manager that creates, validates, versions, and deploys Genesys AI models, runs real-time and batch inference, tracks metrics via webhooks, and maintains audit logs for enterprise orchestration.
• You will use the Genesys Cloud AI API (/api/v2/ai/) and the @genesyscloud/api-client SDK for token management.
• The tutorial covers TypeScript with axios, zod, and modern async/await patterns.

Prerequisites

• Service account with OAuth scopes: ai:model:read, ai:model:write, ai:model:execute, ai:metrics:read, ai:webhooks:write
• Genesys Cloud API version: v2
• Node.js 18+ with TypeScript 5+
• External dependencies: npm install axios zod @genesyscloud/api-client dotenv

Authentication Setup

The Genesys Cloud TypeScript SDK handles OAuth2 client credentials flow and automatic token refresh. You initialize the ApiClient once, then extract the bearer token for downstream HTTP calls.

import { ApiClient } from '@genesyscloud/api-client';
import * as dotenv from 'dotenv';

dotenv.config();

export async function initGenesysClient(): Promise<ApiClient> {
  const client = new ApiClient();
  await client.init({
    clientId: process.env.GENESYS_CLIENT_ID!,
    clientSecret: process.env.GENESYS_CLIENT_SECRET!,
    environment: process.env.GENESYS_ENVIRONMENT || 'mypurecloud.com',
    debug: false
  });
  return client;
}

// Token retrieval helper for axios integration
export async function getBearerToken(client: ApiClient): Promise<string> {
  const auth = await client.getAccessToken();
  return `Bearer ${auth.access_token}`;
}

The SDK caches tokens in memory and refreshes them before expiration. You will use getBearerToken to attach headers to all AI API requests.

Implementation

Step 1: Construct and Validate Model Configuration Payloads

You must validate model configurations against compute resource limits and policy constraints before submission. The payload defines training dataset locations, hyperparameters, evaluation metrics, and compute boundaries.

Required OAuth Scope: ai:model:write

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

const ModelConfigSchema = z.object({
  name: z.string().min(3),
  description: z.string().optional(),
  type: z.enum(['conversation-insights', 'custom-nlp', 'intent-classification']),
  trainingData: z.object({
    uri: z.string().url(),
    format: z.enum(['jsonl', 'csv', 'parquet'])
  }),
  hyperparameters: z.object({
    learningRate: z.number().min(0.0001).max(0.1),
    epochs: z.number().int().min(1).max(500),
    batchSize: z.number().int().min(8).max(256)
  }),
  evaluationMetrics: z.array(z.enum(['accuracy', 'f1_score', 'precision', 'recall', 'latency_p95'])),
  computeConstraints: z.object({
    maxCpuCores: z.number().int().max(32),
    maxMemoryGb: z.number().int().max(128),
    maxTrainingHours: z.number().int().max(72)
  }),
  policyConstraints: z.object({
    maxDataSensitivity: z.enum(['low', 'medium', 'high']),
    requireEncryptionAtRest: z.boolean(),
    allowedRegions: z.array(z.string()).min(1)
  })
});

export type ModelConfig = z.infer<typeof ModelConfigSchema>;

export async function createModel(client: ApiClient, config: ModelConfig) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models`;

  try {
    const response = await axios.post(url, config, {
      headers: {
        Authorization: token,
        'Content-Type': 'application/json',
        'Accept': 'application/json'
      }
    });

    console.log('Model created:', response.data);
    return response.data;
  } catch (error: any) {
    if (error.response?.status === 400) {
      console.error('Validation failed:', error.response.data.errors);
      throw new Error('Model configuration failed schema or policy validation.');
    }
    throw error;
  }
}

HTTP Request Cycle:

• Method: POST
• Path: /api/v2/ai/models
• Headers: Authorization: Bearer <token>, Content-Type: application/json
• Request Body: Matches ModelConfigSchema
• Expected Response: 201 Created with { id: "abc-123", status: "initializing", createdDate: "2024-01-15T10:00:00Z" }

Step 2: Handle Model Lifecycle and A/B Testing Version Management

Version management controls model iterations. You attach A/B testing hooks by defining traffic split percentages during version creation. The API routes inference requests based on the split configuration.

Required OAuth Scope: ai:model:write

export async function createModelVersion(client: ApiClient, modelId: string, versionName: string, trafficSplit: number) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions`;

  const payload = {
    name: versionName,
    status: 'staged',
    abTesting: {
      enabled: true,
      trafficPercentage: trafficSplit,
      comparisonVersionId: null // Set dynamically during rollout
    },
    metadata: {
      createdBy: 'api-orchestrator',
      environment: 'production'
    }
  };

  try {
    const response = await axios.post(url, payload, {
      headers: {
        Authorization: token,
        'Content-Type': 'application/json'
      }
    });

    console.log('Version created:', response.data);
    return response.data;
  } catch (error: any) {
    if (error.response?.status === 409) {
      throw new Error('Version name already exists for this model.');
    }
    throw error;
  }
}

export async function deployVersion(client: ApiClient, modelId: string, versionId: string) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/deploy`;

  await axios.post(url, {}, {
    headers: { Authorization: token }
  });
}

HTTP Request Cycle:

• Method: POST
• Path: /api/v2/ai/models/{modelId}/versions
• Request Body: { name: "v2.1", status: "staged", abTesting: { enabled: true, trafficPercentage: 20 } }
• Expected Response: 201 Created with { id: "ver-xyz", modelId: "abc-123", status: "staged", abTestingConfig: { ... } }

Step 3: Implement Real-Time and Batch Inference with Latency Optimization

Real-time inference requires strict timeout handling and connection pooling. Batch inference processes queued payloads asynchronously. You implement retry logic for 429 rate limits to prevent cascade failures.

Required OAuth Scope: ai:model:execute

import axios, { AxiosInstance } from 'axios';

async function retryOnRateLimit(request: () => Promise<any>, maxRetries: number = 3): Promise<any> {
  let attempt = 0;
  while (true) {
    try {
      return await request();
    } catch (error: any) {
      if (error.response?.status === 429 && attempt < maxRetries) {
        const retryAfter = error.response.headers['retry-after'] || Math.pow(2, attempt);
        console.log(`Rate limited. Retrying in ${retryAfter}s...`);
        await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
        attempt++;
      } else {
        throw error;
      }
    }
  }
}

export async function runRealTimeInference(client: ApiClient, modelId: string, versionId: string, inputData: any) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/inference`;

  const axiosInstance = axios.create({
    timeout: 3000, // 3s latency budget for real-time
    headers: { Authorization: token, 'Content-Type': 'application/json' }
  });

  const response = await retryOnRateLimit(async () => 
    axiosInstance.post(url, { inputs: [inputData] })
  );

  return response.data.predictions[0];
}

export async function runBatchInference(client: ApiClient, modelId: string, versionId: string, datasetUri: string) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/batch-inference`;

  const response = await retryOnRateLimit(async () =>
    axios.post(url, { datasetUri, outputFormat: 'jsonl' }, {
      headers: { Authorization: token, 'Content-Type': 'application/json' }
    })
  );

  return response.data;
}

HTTP Request Cycle:

• Method: POST
• Path: /api/v2/ai/models/{modelId}/versions/{versionId}/inference
• Request Body: { inputs: [{ text: "customer call transcript segment" }] }
• Expected Response: 200 OK with { predictions: [{ confidence: 0.94, label: "sentiment_negative" }], latencyMs: 245 }

Step 4: Synchronize Metrics, Track Costs, and Generate Audit Logs

You synchronize model metrics with external dashboards by configuring webhooks on the version endpoint. You track training cost and inference accuracy via the metrics endpoint. Audit logs provide compliance verification with pagination support.

Required OAuth Scopes: ai:webhooks:write, ai:metrics:read

export async function registerMetricsWebhook(client: ApiClient, modelId: string, versionId: string, callbackUrl: string) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/webhooks`;

  await axios.post(url, {
    eventTypes: ['model.metrics.updated', 'model.training.completed'],
    callbackUrl,
    headers: { 'X-External-Auth': process.env.EXTERNAL_DASHBOARD_TOKEN || '' }
  }, {
    headers: { Authorization: token, 'Content-Type': 'application/json' }
  });
}

export async function fetchModelMetrics(client: ApiClient, modelId: string, versionId: string, startDate: string, endDate: string) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/metrics`;

  const params = new URLSearchParams({ startDate, endDate, interval: '1h' });
  const response = await axios.get(`${url}?${params}`, { headers: { Authorization: token } });
  return response.data;
}

export async function fetchAuditLogs(client: ApiClient, modelId: string, versionId: string, pageSize: number = 50) {
  const token = await getBearerToken(client);
  const env = client.getEnvironment() || 'mypurecloud.com';
  const url = `https://${env}/api/v2/ai/models/${modelId}/versions/${versionId}/audit-logs`;
  
  let allLogs: any[] = [];
  let nextPageToken: string | null = null;

  do {
    const params = new URLSearchParams({ pageSize: pageSize.toString() });
    if (nextPageToken) params.append('pageToken', nextPageToken);

    const response = await axios.get(`${url}?${params}`, { headers: { Authorization: token } });
    allLogs = allLogs.concat(response.data.entities);
    nextPageToken = response.data.nextPageToken || null;
  } while (nextPageToken);

  return allLogs;
}

HTTP Request Cycle:

• Method: GET
• Path: /api/v2/ai/models/{modelId}/versions/{versionId}/audit-logs
• Headers: Authorization: Bearer <token>
• Expected Response: 200 OK with { entities: [{ action: "version_deployed", actor: "api-orchestrator", timestamp: "2024-01-15T12:00:00Z", details: { ... } }], nextPageToken: "abc123" }

Complete Working Example

The following module combines authentication, validation, lifecycle management, inference, and monitoring into a single enterprise model manager. Replace environment variables with your credentials before execution.

import { ApiClient } from '@genesyscloud/api-client';
import * as dotenv from 'dotenv';
import { initGenesysClient, getBearerToken } from './auth';
import { createModel, createModelVersion, deployVersion } from './model-lifecycle';
import { runRealTimeInference, runBatchInference } from './inference';
import { registerMetricsWebhook, fetchModelMetrics, fetchAuditLogs } from './monitoring';

dotenv.config();

class GenesysAIModelManager {
  private client: ApiClient;
  private modelId: string | null = null;
  private versionId: string | null = null;

  constructor() {
    this.client = {} as ApiClient;
  }

  async initialize() {
    this.client = await initGenesysClient();
    console.log('Genesys API client initialized.');
  }

  async provisionModel(config: any) {
    const model = await createModel(this.client, config);
    this.modelId = model.id;
    console.log('Model provisioned:', this.modelId);
    return model;
  }

  async stageAndDeployVersion(versionName: string, trafficSplit: number) {
    if (!this.modelId) throw new Error('Model not provisioned.');
    const version = await createModelVersion(this.client, this.modelId, versionName, trafficSplit);
    this.versionId = version.id;
    await deployVersion(this.client, this.modelId, this.versionId);
    console.log('Version deployed:', this.versionId);
    return version;
  }

  async executeInference(inputData: any) {
    if (!this.modelId || !this.versionId) throw new Error('Model or version not ready.');
    const prediction = await runRealTimeInference(this.client, this.modelId, this.versionId, inputData);
    return prediction;
  }

  async setupMonitoring(callbackUrl: string) {
    if (!this.modelId || !this.versionId) throw new Error('Model or version not ready.');
    await registerMetricsWebhook(this.client, this.modelId, this.versionId, callbackUrl);
    console.log('Metrics webhook registered.');
  }

  async generateComplianceReport(startDate: string, endDate: string) {
    if (!this.modelId || !this.versionId) throw new Error('Model or version not ready.');
    const metrics = await fetchModelMetrics(this.client, this.modelId, this.versionId, startDate, endDate);
    const logs = await fetchAuditLogs(this.client, this.modelId, this.versionId);
    return { metrics, auditTrail: logs };
  }
}

async function main() {
  const manager = new GenesysAIModelManager();
  await manager.initialize();

  const config = {
    name: 'Customer-Sentiment-Classifier',
    type: 'custom-nlp',
    trainingData: { uri: 'https://storage.example.com/training-data.jsonl', format: 'jsonl' },
    hyperparameters: { learningRate: 0.001, epochs: 50, batchSize: 32 },
    evaluationMetrics: ['accuracy', 'f1_score', 'latency_p95'],
    computeConstraints: { maxCpuCores: 16, maxMemoryGb: 64, maxTrainingHours: 24 },
    policyConstraints: { maxDataSensitivity: 'medium', requireEncryptionAtRest: true, allowedRegions: ['us-east-1'] }
  };

  await manager.provisionModel(config);
  await manager.stageAndDeployVersion('v1.0-production', 100);
  await manager.setupMonitoring('https://dashboard.example.com/webhooks/genesys-ai');

  const result = await manager.executeInference({ text: 'The service was incredibly slow and unhelpful.' });
  console.log('Inference result:', result);

  const report = await manager.generateComplianceReport('2024-01-01T00:00:00Z', '2024-01-31T23:59:59Z');
  console.log('Compliance report generated. Audit entries:', report.auditTrail.length);
}

main().catch(console.error);

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired or missing OAuth token, or incorrect clientId/clientSecret.
• Fix: Verify the service account credentials. Ensure the @genesyscloud/api-client token cache is not corrupted. Restart the process to force a fresh token exchange.
• Code Fix: The initGenesysClient() function handles token exchange. Add explicit error logging if client.init() fails.

Error: 403 Forbidden

• Cause: Missing OAuth scope on the service account or policy constraint violation.
• Fix: Add ai:model:write, ai:model:execute, or ai:metrics:read to the service account in the Genesys Cloud admin console. Verify computeConstraints and policyConstraints match tenant limits.
• Code Fix: Check error.response.data.errors for scope or policy messages. Adjust payload accordingly.

Error: 429 Too Many Requests

• Cause: Exceeding API rate limits for inference or metric retrieval.
• Fix: Implement exponential backoff. The retryOnRateLimit wrapper handles this automatically by reading the Retry-After header or applying a 2^attempt delay.
• Code Fix: Ensure all API calls route through retryOnRateLimit. Increase maxRetries if volume spikes are expected.

Error: 503 Service Unavailable

• Cause: Genesys Cloud AI compute cluster is at capacity or training jobs are queued.
• Fix: Reduce computeConstraints in the model payload. Schedule training during off-peak hours. Implement polling with jitter for asynchronous batch jobs.
• Code Fix: Wrap long-running operations in a retry loop with a 15-second base delay and 2-second jitter.

Official References

• Genesys Cloud AI Models API Documentation
• Genesys Cloud API Client SDK (TypeScript)
• OAuth2 Authorization Guide
• AI Inference and Batch Processing Endpoints