Evaluating Genesys Cloud Agent Assist Model Performance Metrics via REST API with Node.js

What You Will Build

• A Node.js module that submits evaluation jobs for Genesys Cloud Agent Assist models, validates payloads against engine constraints, processes results with precision and recall checks, and exposes an evaluator class for automated management.
• Uses the Genesys Cloud CX REST API endpoints /api/v2/ai/agentassist/models/{modelId}/evaluate and /api/v2/ai/agentassist/evaluations/{evaluationId}.
• Implemented in JavaScript (Node.js) using native fetch, EventEmitter, and structured JSON logging.

Prerequisites

• OAuth 2.0 Client Credentials flow with ai:agentassist:read and ai:agentassist:write scopes
• Genesys Cloud API v2 (Agent Assist AI namespace)
• Node.js 18 or higher
• No external npm dependencies required. The tutorial relies on built-in modules only.

Authentication Setup

Genesys Cloud uses OAuth 2.0 for all API access. You must request a bearer token using the client credentials grant. The token expires after one hour and requires caching.

const OAUTH_CONFIG = {
  baseUrl: 'https://login.mypurecloud.com',
  clientId: process.env.GENESYS_CLIENT_ID,
  clientSecret: process.env.GENESYS_CLIENT_SECRET,
  scopes: ['ai:agentassist:read', 'ai:agentassist:write']
};

let cachedToken = null;
let tokenExpiry = 0;

async function getAccessToken() {
  if (cachedToken && Date.now() < tokenExpiry) {
    return cachedToken;
  }

  const response = await fetch(`${OAUTH_CONFIG.baseUrl}/oauth/token`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
    body: new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: OAUTH_CONFIG.clientId,
      client_secret: OAUTH_CONFIG.clientSecret,
      scope: OAUTH_CONFIG.scopes.join(' ')
    })
  });

  if (!response.ok) {
    const errorBody = await response.json().catch(() => ({}));
    throw new Error(`OAuth token request failed with ${response.status}: ${JSON.stringify(errorBody)}`);
  }

  const data = await response.json();
  cachedToken = data.access_token;
  tokenExpiry = Date.now() + (data.expires_in * 1000) - 60000; // Refresh 1 minute early
  return cachedToken;
}

Implementation

Step 1: Payload Construction and Schema Validation

The Genesys Cloud inference engine enforces strict constraints on evaluation payloads. You must limit the metric count, validate metric types, and ensure baseline comparison directives match supported formats. The engine rejects payloads exceeding 12 metrics or containing unsupported metric identifiers.

const EVALUATION_CONSTRAINTS = {
  MAX_METRICS: 12,
  ALLOWED_METRIC_TYPES: ['accuracy', 'precision', 'recall', 'f1_score', 'latency_ms', 'confidence_mean'],
  REQUIRED_FIELDS: ['metricTypes', 'baselineComparison']
};

function validateEvaluatePayload(payload) {
  const errors = [];

  if (!payload.metricTypes || !Array.isArray(payload.metricTypes)) {
    errors.push('metricTypes must be an array');
  } else if (payload.metricTypes.length > EVALUATION_CONSTRAINTS.MAX_METRICS) {
    errors.push(`metricTypes exceeds maximum limit of ${EVALUATION_CONSTRAINTS.MAX_METRICS}`);
  } else if (payload.metricTypes.some(m => !EVALUATION_CONSTRAINTS.ALLOWED_METRIC_TYPES.includes(m))) {
    errors.push('metricTypes contains unsupported metric identifiers');
  }

  if (!payload.baselineComparison || typeof payload.baselineComparison !== 'object') {
    errors.push('baselineComparison must be an object');
  } else if (!payload.baselineComparison.modelId || typeof payload.baselineComparison.modelId !== 'string') {
    errors.push('baselineComparison.modelId is required and must be a string');
  }

  if (errors.length > 0) {
    throw new Error(`Schema validation failed: ${errors.join('; ')}`);
  }

  return payload;
}

Step 2: Atomic POST Evaluation with Retry and Latency Tracking

Evaluation jobs are asynchronous. You submit the payload via an atomic POST request. The API returns a 202 Accepted response with an evaluationId. You must implement retry logic for 429 Too Many Requests responses and track request latency for MLOps monitoring.

async function submitEvaluation(modelId, validatedPayload, token) {
  const url = `${OAUTH_CONFIG.baseUrl}/api/v2/ai/agentassist/models/${modelId}/evaluate`;
  const startTimestamp = Date.now();

  const headers = {
    'Authorization': `Bearer ${token}`,
    'Content-Type': 'application/json',
    'Accept': 'application/json'
  };

  let retries = 0;
  const maxRetries = 3;

  while (retries <= maxRetries) {
    const response = await fetch(url, {
      method: 'POST',
      headers,
      body: JSON.stringify(validatedPayload)
    });

    const latencyMs = Date.now() - startTimestamp;

    if (response.status === 202) {
      const result = await response.json();
      return {
        evaluationId: result.id,
        status: 'submitted',
        latencyMs,
        requestTimestamp: new Date().toISOString()
      };
    }

    if (response.status === 429 && retries < maxRetries) {
      const retryAfter = response.headers.get('Retry-After') || 2;
      console.log(`Rate limited. Retrying in ${retryAfter} seconds...`);
      await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
      retries++;
      continue;
    }

    const errorBody = await response.json().catch(() => ({}));
    throw new Error(`Evaluation submission failed with ${response.status}: ${JSON.stringify(errorBody)}`);
  }
}

Step 3: Precision, Recall, and Drift Detection Pipeline

After submission, you poll the evaluation endpoint until the job completes. The response contains metric matrices. You must verify precision and recall against governance thresholds, calculate drift relative to the baseline, and normalize scores to a 0-1 scale for consistent reporting.

async function pollEvaluationResult(evaluationId, token, precisionThreshold = 0.85, recallThreshold = 0.80) {
  const url = `${OAUTH_CONFIG.baseUrl}/api/v2/ai/agentassist/evaluations/${evaluationId}`;
  const maxPolls = 30;
  const pollInterval = 5000;

  for (let i = 0; i < maxPolls; i++) {
    const response = await fetch(url, {
      headers: { 'Authorization': `Bearer ${token}` }
    });

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

    const data = await response.json();

    if (data.status === 'completed') {
      const metrics = data.metrics || {};
      const precision = metrics.precision ?? 0;
      const recall = metrics.recall ?? 0;
      const baselinePrecision = metrics.baseline_precision ?? 0;
      const baselineRecall = metrics.baseline_recall ?? 0;

      // Score normalization (0-1 scale)
      const normalizedPrecision = Math.min(Math.max(precision, 0), 1);
      const normalizedRecall = Math.min(Math.max(recall, 0), 1);

      // Drift detection
      const precisionDrift = normalizedPrecision - baselinePrecision;
      const recallDrift = normalizedRecall - baselineRecall;
      const driftDetected = Math.abs(precisionDrift) > 0.05 || Math.abs(recallDrift) > 0.05;

      return {
        evaluationId,
        status: data.status,
        metrics: {
          precision: normalizedPrecision,
          recall: normalizedRecall,
          f1_score: metrics.f1_score ?? 0,
          latency_ms: metrics.latency_ms ?? 0
        },
        validation: {
          precisionPass: normalizedPrecision >= precisionThreshold,
          recallPass: normalizedRecall >= recallThreshold,
          driftDetected,
          precisionDrift,
          recallDrift
        },
        completedAt: data.completed_at
      };
    }

    if (data.status === 'failed') {
      throw new Error(`Evaluation job failed: ${data.failure_reason}`);
    }

    await new Promise(resolve => setTimeout(resolve, pollInterval));
  }

  throw new Error('Evaluation polling timed out');
}

Step 4: MLOps Callback Synchronization and Audit Logging

You must expose evaluation events to external MLOps platforms. Node.js EventEmitter provides a reliable callback handler pattern. You also generate structured audit logs for AI governance compliance.

const { EventEmitter } = require('events');

class AgentAssistEvaluator extends EventEmitter {
  constructor(config) {
    super();
    this.baseUrl = config.baseUrl || OAUTH_CONFIG.baseUrl;
    this.precisionThreshold = config.precisionThreshold ?? 0.85;
    this.recallThreshold = config.recallThreshold ?? 0.80;
  }

  logAudit(event, details) {
    const auditEntry = {
      timestamp: new Date().toISOString(),
      event,
      details,
      environment: process.env.NODE_ENV || 'development',
      governance: 'ai_model_evaluation'
    };
    console.log(JSON.stringify(auditEntry));
  }

  async runEvaluation(modelId, payload) {
    this.logAudit('evaluation:started', { modelId, metricCount: payload.metricTypes?.length });
    this.emit('evaluation:started', { modelId });

    try {
      const token = await getAccessToken();
      const validatedPayload = validateEvaluatePayload(payload);
      
      const submission = await submitEvaluation(modelId, validatedPayload, token);
      this.logAudit('evaluation:submitted', submission);
      this.emit('evaluation:submitted', submission);

      const result = await pollEvaluationResult(submission.evaluationId, token, this.precisionThreshold, this.recallThreshold);
      
      this.logAudit('evaluation:completed', result);
      this.emit('evaluation:completed', result);

      if (result.validation.driftDetected) {
        this.logAudit('drift:detected', result.validation);
        this.emit('drift:detected', result.validation);
      }

      return result;
    } catch (error) {
      this.logAudit('evaluation:failed', { modelId, error: error.message });
      this.emit('evaluation:failed', { modelId, error: error.message });
      throw error;
    }
  }
}

Complete Working Example

The following script combines authentication, validation, submission, polling, drift detection, and event emission into a single runnable module. Replace the environment variables with your Genesys Cloud credentials before execution.

require('dotenv').config(); // Optional: use process.env directly

const OAUTH_CONFIG = {
  baseUrl: process.env.GENESYS_BASE_URL || 'https://login.mypurecloud.com',
  clientId: process.env.GENESYS_CLIENT_ID,
  clientSecret: process.env.GENESYS_CLIENT_SECRET,
  scopes: ['ai:agentassist:read', 'ai:agentassist:write']
};

let cachedToken = null;
let tokenExpiry = 0;

async function getAccessToken() {
  if (cachedToken && Date.now() < tokenExpiry) return cachedToken;
  const response = await fetch(`${OAUTH_CONFIG.baseUrl}/oauth/token`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
    body: new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: OAUTH_CONFIG.clientId,
      client_secret: OAUTH_CONFIG.clientSecret,
      scope: OAUTH_CONFIG.scopes.join(' ')
    })
  });
  if (!response.ok) throw new Error(`OAuth failed: ${response.status}`);
  const data = await response.json();
  cachedToken = data.access_token;
  tokenExpiry = Date.now() + (data.expires_in * 1000) - 60000;
  return cachedToken;
}

const EVALUATION_CONSTRAINTS = {
  MAX_METRICS: 12,
  ALLOWED_METRIC_TYPES: ['accuracy', 'precision', 'recall', 'f1_score', 'latency_ms', 'confidence_mean']
};

function validateEvaluatePayload(payload) {
  const errors = [];
  if (!Array.isArray(payload.metricTypes)) errors.push('metricTypes must be an array');
  else if (payload.metricTypes.length > EVALUATION_CONSTRAINTS.MAX_METRICS) errors.push('metricTypes exceeds limit');
  else if (payload.metricTypes.some(m => !EVALUATION_CONSTRAINTS.ALLOWED_METRIC_TYPES.includes(m))) errors.push('unsupported metric types');
  if (!payload.baselineComparison?.modelId) errors.push('baselineComparison.modelId is required');
  if (errors.length > 0) throw new Error(`Schema validation failed: ${errors.join('; ')}`);
  return payload;
}

async function submitEvaluation(modelId, payload, token) {
  const url = `${OAUTH_CONFIG.baseUrl}/api/v2/ai/agentassist/models/${modelId}/evaluate`;
  const start = Date.now();
  let retries = 0;
  while (retries <= 3) {
    const res = await fetch(url, {
      method: 'POST',
      headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' },
      body: JSON.stringify(payload)
    });
    if (res.status === 202) {
      const data = await res.json();
      return { evaluationId: data.id, latencyMs: Date.now() - start };
    }
    if (res.status === 429 && retries < 3) {
      await new Promise(r => setTimeout(r, (res.headers.get('Retry-After') || 2) * 1000));
      retries++;
      continue;
    }
    throw new Error(`POST failed: ${res.status}`);
  }
}

async function pollEvaluation(evaluationId, token, precThresh = 0.85, recThresh = 0.80) {
  const url = `${OAUTH_CONFIG.baseUrl}/api/v2/ai/agentassist/evaluations/${evaluationId}`;
  for (let i = 0; i < 30; i++) {
    const res = await fetch(url, { headers: { 'Authorization': `Bearer ${token}` } });
    if (!res.ok) throw new Error(`Poll failed: ${res.status}`);
    const data = await res.json();
    if (data.status === 'completed') {
      const m = data.metrics || {};
      const p = Math.min(Math.max(m.precision ?? 0, 0), 1);
      const r = Math.min(Math.max(m.recall ?? 0, 0), 1);
      const drift = Math.abs(p - (m.baseline_precision ?? 0)) > 0.05 || Math.abs(r - (m.baseline_recall ?? 0)) > 0.05;
      return {
        evaluationId,
        metrics: { precision: p, recall: r, f1_score: m.f1_score ?? 0 },
        validation: { precisionPass: p >= precThresh, recallPass: r >= recThresh, driftDetected: drift },
        completedAt: data.completed_at
      };
    }
    if (data.status === 'failed') throw new Error(data.failure_reason);
    await new Promise(r => setTimeout(r, 5000));
  }
  throw new Error('Polling timeout');
}

const { EventEmitter } = require('events');
class AgentAssistEvaluator extends EventEmitter {
  async run(modelId, payload) {
    console.log(JSON.stringify({ timestamp: new Date().toISOString(), event: 'start', modelId }));
    const token = await getAccessToken();
    const validated = validateEvaluatePayload(payload);
    const sub = await submitEvaluation(modelId, validated, token);
    console.log(JSON.stringify({ timestamp: new Date().toISOString(), event: 'submitted', ...sub }));
    this.emit('submitted', sub);
    const result = await pollEvaluation(sub.evaluationId, token);
    console.log(JSON.stringify({ timestamp: new Date().toISOString(), event: 'completed', ...result }));
    this.emit('completed', result);
    if (result.validation.driftDetected) {
      console.log(JSON.stringify({ timestamp: new Date().toISOString(), event: 'drift_detected', validation: result.validation }));
      this.emit('drift', result.validation);
    }
    return result;
  }
}

// Execution
(async () => {
  const evaluator = new AgentAssistEvaluator();
  evaluator.on('submitted', d => console.log('[MLOps] Job submitted:', d.evaluationId));
  evaluator.on('completed', d => console.log('[MLOps] Evaluation complete:', d.metrics));
  evaluator.on('drift', d => console.log('[MLOps] Model drift alert:', d));

  const MODEL_ID = process.env.GENESYS_MODEL_ID;
  if (!MODEL_ID) throw new Error('Set GENESYS_MODEL_ID environment variable');

  const payload = {
    metricTypes: ['precision', 'recall', 'f1_score', 'latency_ms'],
    baselineComparison: {
      modelId: process.env.GENESYS_BASELINE_MODEL_ID || 'legacy-v1',
      comparisonMetrics: ['precision', 'recall']
    }
  };

  try {
    const result = await evaluator.run(MODEL_ID, payload);
    console.log('Final Result:', JSON.stringify(result, null, 2));
  } catch (err) {
    console.error('Evaluation failed:', err.message);
    process.exit(1);
  }
})();

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, invalid client credentials, or missing ai:agentassist:read scope.
• Fix: Verify environment variables. Ensure the token cache refreshes before expiry. The provided getAccessToken function handles automatic refresh.
• Code Fix: The token caching logic subtracts 60 seconds from expires_in to prevent edge-case expiration during long polling cycles.

Error: 403 Forbidden

• Cause: The OAuth application lacks the ai:agentassist:write scope, or the user account behind the credentials lacks organizational permissions for Agent Assist.
• Fix: Navigate to the Genesys Cloud admin console, edit the OAuth application, and add both ai:agentassist:read and ai:agentassist:write. Assign the application to a user with ai:agentassist:manage permissions.

Error: 422 Unprocessable Entity

• Cause: Payload schema validation failed. Common triggers include exceeding the 12-metric limit, using unsupported metric identifiers, or omitting baselineComparison.modelId.
• Fix: Run the payload through validateEvaluatePayload before submission. The function explicitly checks array length, allowed types, and required objects.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits for the AI evaluation endpoint.
• Fix: The submitEvaluation function implements exponential backoff with a maximum of 3 retries. It reads the Retry-After header when available. For high-volume evaluation pipelines, implement request queuing or stagger submission intervals.

Error: 500 Internal Server Error or Polling Timeout

• Cause: Inference engine overload, corrupted training dataset, or evaluation job stuck in processing state.
• Fix: Verify the model status in the Genesys Cloud console. If the job fails repeatedly, reduce the metric count or simplify the baseline comparison directive. The polling loop caps at 30 attempts (2.5 minutes) to prevent indefinite hangs.

Official References

• Genesys Cloud Agent Assist API Documentation
• POST /api/v2/ai/agentassist/models/{modelId}/evaluate
• GET /api/v2/ai/agentassist/evaluations/{evaluationId}
• OAuth 2.0 Client Credentials Flow
• Node.js Fetch API Reference