Evaluating Cognigy.AI Conversation Flow A/B Tests via REST API with Node.js

What You Will Build

• Build a Node.js evaluation module that programmatically submits structured A/B test evaluation payloads to Cognigy.AI.
• Use the Cognigy.AI REST API v1 for metric aggregation, statistical validation, and webhook synchronization.
• Implement the solution in JavaScript/Node.js with strict schema validation, retry logic, and audit logging.

Prerequisites

• OAuth 2.0 Client Credentials flow with ab-tests:read, ab-tests:write, analytics:read, webhooks:write scopes.
• Cognigy.AI API v1.
• Node.js 18 or higher.
• External dependencies: axios, zod, uuid, crypto (Node.js built-in).

Authentication Setup

Cognigy.AI uses OAuth 2.0 Client Credentials for server-to-server API access. The authentication handler caches tokens and refreshes them before expiration to prevent 401 interruptions during batch evaluations.

const axios = require('axios');

class CognigyAuth {
  constructor(clientId, clientSecret, baseUrl) {
    this.clientId = clientId;
    this.clientSecret = clientSecret;
    this.baseUrl = baseUrl;
    this.token = null;
    this.expiresAt = 0;
  }

  async getAccessToken() {
    if (this.token && Date.now() < this.expiresAt) {
      return this.token;
    }

    const params = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: this.clientId,
      client_secret: this.clientSecret
    });

    const response = await axios.post(`${this.baseUrl}/oauth/token`, params, {
      headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
      timeout: 5000
    });

    this.token = response.data.access_token;
    this.expiresAt = Date.now() + (response.data.expires_in * 1000) - 60000;
    return this.token;
  }
}

The endpoint /oauth/token returns a JSON payload containing access_token, expires_in, and token_type. The handler subtracts sixty seconds from the expiration window to prevent boundary race conditions during concurrent API calls.

Implementation

Step 1: Construct Evaluate Payloads and Validate Against Analytics Constraints

The evaluation payload must reference the test campaign ID, define variant metric matrices, and specify a significance directive. Cognigy.AI analytics engines enforce maximum sample size limits to prevent memory exhaustion during aggregation. The schema validation step rejects malformed inputs before network transmission.

const { z } = require('zod');

const MAX_SAMPLE_SIZE_LIMIT = 50000;

const EvaluatePayloadSchema = z.object({
  testCampaignId: z.string().uuid(),
  variants: z.array(z.object({
    variantId: z.string(),
    currentSampleSize: z.number().int().min(0),
    conversionCount: z.number().int().min(0),
    metricValue: z.number()
  })).min(2).max(4),
  significanceLevel: z.number().min(0.01).max(0.05),
  maxSampleSize: z.number().int().max(MAX_SAMPLE_SIZE_LIMIT),
  winnerDeclarationTrigger: z.boolean().default(true)
});

function validateAndConstructPayload(rawPayload) {
  const validation = EvaluatePayloadSchema.safeParse(rawPayload);
  
  if (!validation.success) {
    const errors = validation.error.errors.map(e => `${e.path.join('.')}: ${e.message}`).join('; ');
    throw new Error(`Schema validation failed: ${errors}`);
  }

  const payload = validation.data;

  // Analytics engine constraint check
  const totalRequestedSample = payload.variants.reduce((sum, v) => sum + v.currentSampleSize, 0);
  if (totalRequestedSample > MAX_SAMPLE_SIZE_LIMIT) {
    throw new Error(`Total sample size ${totalRequestedSample} exceeds analytics engine limit of ${MAX_SAMPLE_SIZE_LIMIT}`);
  }

  return payload;
}

The zod schema enforces UUID format for campaign references, restricts variant counts to two through four, and caps the significance level at 0.05. The secondary constraint check prevents the analytics engine from rejecting the request due to oversized aggregation matrices.

Step 2: Atomic PUT Operations for Metric Aggregation and Winner Declaration

Metric aggregation requires an atomic PUT operation to ensure Cognigy.AI processes the variant matrix as a single transaction. The client implements exponential backoff for 429 rate limit responses and verifies the response format before proceeding.

async function submitEvaluation(baseUrl, token, payload, retries = 3) {
  const url = `${baseUrl}/api/v1/ab-tests/${payload.testCampaignId}/evaluate`;
  
  for (let attempt = 0; attempt < retries; attempt++) {
    try {
      const startTime = performance.now();
      
      const response = await axios.put(url, payload, {
        headers: {
          'Authorization': `Bearer ${token}`,
          'Content-Type': 'application/json',
          'X-Correlation-ID': require('uuid').v4()
        },
        timeout: 15000
      });

      const latencyMs = performance.now() - startTime;
      
      // Format verification
      if (!response.data || typeof response.data.status !== 'string') {
        throw new Error('Invalid response format from analytics engine');
      }

      return {
        success: true,
        data: response.data,
        latencyMs,
        winnerDeclared: response.data.winnerDeclared || false
      };

    } catch (error) {
      if (error.response?.status === 429 && attempt < retries - 1) {
        const retryAfter = parseInt(error.response.headers['retry-after'] || '2', 10);
        await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
        continue;
      }
      
      if (error.response?.status === 400) {
        throw new Error(`Validation rejected by server: ${JSON.stringify(error.response.data)}`);
      }
      
      throw error;
    }
  }
}

The endpoint /api/v1/ab-tests/{testCampaignId}/evaluate accepts the validated payload and returns a JSON object containing status, winnerDeclared, and aggregationTimestamp. The retry loop respects the Retry-After header and aborts after three attempts to prevent indefinite blocking.

Step 3: Statistical Power Checking and Bias Detection Verification

Reliable flow optimization requires statistical power verification and traffic bias detection. The pipeline calculates whether the current sample size meets the minimum detectable effect threshold and verifies that traffic distribution matches the expected split ratio.

function calculateStatisticalPower(variants, significanceLevel, maxSampleSize) {
  const alpha = significanceLevel;
  const zAlpha = 1.96;
  const zBeta = 0.84;
  const minimumEffectSize = 0.1;
  
  const requiredSampleSize = Math.ceil(2 * Math.pow((zAlpha + zBeta) / minimumEffectSize, 2));
  const currentTotalSample = variants.reduce((sum, v) => sum + v.currentSampleSize, 0);
  
  const powerRatio = Math.min(1, currentTotalSample / requiredSampleSize);
  const estimatedPower = powerRatio * 0.8;
  
  return {
    requiredSampleSize,
    currentTotalSample,
    estimatedPower: Math.round(estimatedPower * 100) / 100,
    isPowerSufficient: estimatedPower >= 0.8
  };
}

function detectTrafficBias(variants, tolerance = 0.05) {
  const total = variants.reduce((sum, v) => sum + v.currentSampleSize, 0);
  if (total === 0) {
    return { isBiased: false, reason: null };
  }

  const expectedShare = 1 / variants.length;
  const actualShares = variants.map(v => v.currentSampleSize / total);
  
  const deviations = actualShares.map(share => Math.abs(share - expectedShare));
  const maxDeviation = Math.max(...deviations);
  
  return {
    isBiased: maxDeviation > tolerance,
    maxDeviation: Math.round(maxDeviation * 100) / 100,
    reason: maxDeviation > tolerance 
      ? `Traffic distribution deviates ${Math.round(maxDeviation * 100)}% from expected split` 
      : null
  };
}

The power calculation uses a standard normal approximation for proportion tests. The bias detection pipeline flags evaluations when any variant receives more than five percent deviation from the expected traffic share. Skewed traffic distribution invalidates statistical significance and triggers a warning before submission.

Step 4: Webhook Synchronization, Latency Tracking, and Audit Logging

External BI tools require synchronized evaluation events. The module posts structured reports to Cognigy.AI webhook endpoints, tracks variant differentiation success rates, and generates governance-compliant audit logs with cryptographic payload hashes.

const crypto = require('crypto');
const { v4: uuidv4 } = require('uuid');

async function syncEvaluationWebhook(baseUrl, token, evaluationResult, payload) {
  const webhookPayload = {
    campaignId: payload.testCampaignId,
    timestamp: new Date().toISOString(),
    winnerDeclared: evaluationResult.winnerDeclared,
    variantDifferentiation: calculateVariantDifferentiation(payload.variants),
    latencyMs: evaluationResult.latencyMs,
    statisticalMetrics: evaluationResult.powerAnalysis
  };

  await axios.post(`${baseUrl}/api/v1/webhooks/evaluation-report`, webhookPayload, {
    headers: {
      'Authorization': `Bearer ${token}`,
      'Content-Type': 'application/json'
    }
  });
}

function calculateVariantDifferentiation(variants) {
  const metricValues = variants.map(v => v.metricValue);
  const maxMetric = Math.max(...metricValues);
  const minMetric = Math.min(...metricValues);
  const range = maxMetric - minMetric;
  const baseMetric = minMetric > 0 ? minMetric : 1;
  
  return {
    differentiationRate: Math.round((range / baseMetric) * 100) / 100,
    highestVariantId: variants.find(v => v.metricValue === maxMetric)?.variantId,
    isSignificant: range > 0.05
  };
}

function generateAuditLog(action, payload, result, latencyMs) {
  const payloadHash = crypto.createHash('sha256').update(JSON.stringify(payload)).digest('hex');
  
  return {
    timestamp: new Date().toISOString(),
    correlationId: uuidv4(),
    action,
    payloadHash,
    resultStatus: result?.data?.status || 'unknown',
    latencyMs: Math.round(latencyMs * 100) / 100,
    governanceTag: 'ai-flow-optimization',
    complianceLevel: 'high'
  };
}

The differentiation calculation measures the spread between variant metric values to quantify optimization impact. The audit log generator creates immutable records with SHA-256 payload hashes for governance compliance. Webhook synchronization ensures external BI pipelines receive real-time evaluation status updates.

Complete Working Example

The following module integrates authentication, validation, statistical verification, API submission, webhook synchronization, and audit logging into a single executable class.

const axios = require('axios');
const { z } = require('zod');
const crypto = require('crypto');
const { v4: uuidv4 } = require('uuid');

class CognigyABTestEvaluator {
  constructor(clientId, clientSecret, baseUrl) {
    this.auth = {
      clientId,
      clientSecret,
      baseUrl,
      token: null,
      expiresAt: 0
    };
  }

  async getAccessToken() {
    if (this.auth.token && Date.now() < this.auth.expiresAt) {
      return this.auth.token;
    }

    const params = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: this.auth.clientId,
      client_secret: this.auth.clientSecret
    });

    const response = await axios.post(`${this.auth.baseUrl}/oauth/token`, params, {
      headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
      timeout: 5000
    });

    this.auth.token = response.data.access_token;
    this.auth.expiresAt = Date.now() + (response.data.expires_in * 1000) - 60000;
    return this.auth.token;
  }

  async evaluateTest(rawPayload) {
    const token = await this.getAccessToken();
    const startTime = performance.now();

    // Step 1: Schema validation
    const MAX_SAMPLE_SIZE_LIMIT = 50000;
    const EvaluatePayloadSchema = z.object({
      testCampaignId: z.string().uuid(),
      variants: z.array(z.object({
        variantId: z.string(),
        currentSampleSize: z.number().int().min(0),
        conversionCount: z.number().int().min(0),
        metricValue: z.number()
      })).min(2).max(4),
      significanceLevel: z.number().min(0.01).max(0.05),
      maxSampleSize: z.number().int().max(MAX_SAMPLE_SIZE_LIMIT),
      winnerDeclarationTrigger: z.boolean().default(true)
    });

    const validation = EvaluatePayloadSchema.safeParse(rawPayload);
    if (!validation.success) {
      throw new Error(`Schema validation failed: ${validation.error.errors.map(e => `${e.path.join('.')}: ${e.message}`).join('; ')}`);
    }

    const payload = validation.data;
    const totalSample = payload.variants.reduce((sum, v) => sum + v.currentSampleSize, 0);
    if (totalSample > MAX_SAMPLE_SIZE_LIMIT) {
      throw new Error(`Total sample size ${totalSample} exceeds analytics engine limit of ${MAX_SAMPLE_SIZE_LIMIT}`);
    }

    // Step 3: Statistical verification
    const alpha = payload.significanceLevel;
    const zAlpha = 1.96;
    const zBeta = 0.84;
    const requiredSampleSize = Math.ceil(2 * Math.pow((zAlpha + zBeta) / 0.1, 2));
    const estimatedPower = Math.min(0.8, (totalSample / requiredSampleSize) * 0.8);

    const expectedShare = 1 / payload.variants.length;
    const maxDeviation = Math.max(...payload.variants.map(v => Math.abs((v.currentSampleSize / totalSample) - expectedShare)));
    const isBiased = maxDeviation > 0.05;

    if (estimatedPower < 0.8) {
      console.warn(`Warning: Statistical power is ${estimatedPower.toFixed(2)}. Consider increasing sample size.`);
    }
    if (isBiased) {
      throw new Error(`Bias detected: Traffic distribution deviates ${Math.round(maxDeviation * 100)}% from expected split`);
    }

    const powerAnalysis = { requiredSampleSize, currentTotalSample: totalSample, estimatedPower: Math.round(estimatedPower * 100) / 100 };

    // Step 2: Atomic PUT submission
    const url = `${this.auth.baseUrl}/api/v1/ab-tests/${payload.testCampaignId}/evaluate`;
    let evaluationResult;

    for (let attempt = 0; attempt < 3; attempt++) {
      try {
        const apiStart = performance.now();
        const response = await axios.put(url, payload, {
          headers: {
            'Authorization': `Bearer ${token}`,
            'Content-Type': 'application/json',
            'X-Correlation-ID': uuidv4()
          },
          timeout: 15000
        });
        evaluationResult = {
          success: true,
          data: response.data,
          latencyMs: performance.now() - apiStart,
          winnerDeclared: response.data.winnerDeclared || false,
          powerAnalysis
        };
        break;
      } catch (error) {
        if (error.response?.status === 429 && attempt < 2) {
          const retryAfter = parseInt(error.response.headers['retry-after'] || '2', 10);
          await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
          continue;
        }
        throw error;
      }
    }

    // Step 4: Webhook sync and audit
    const metricValues = payload.variants.map(v => v.metricValue);
    const range = Math.max(...metricValues) - Math.min(...metricValues);
    const differentiationRate = Math.round((range / (Math.min(...metricValues) || 1)) * 100) / 100;

    await axios.post(`${this.auth.baseUrl}/api/v1/webhooks/evaluation-report`, {
      campaignId: payload.testCampaignId,
      timestamp: new Date().toISOString(),
      winnerDeclared: evaluationResult.winnerDeclared,
      variantDifferentiation: {
        differentiationRate,
        highestVariantId: payload.variants.find(v => v.metricValue === Math.max(...metricValues))?.variantId,
        isSignificant: range > 0.05
      },
      latencyMs: evaluationResult.latencyMs,
      statisticalMetrics: evaluationResult.powerAnalysis
    }, {
      headers: { 'Authorization': `Bearer ${token}`, 'Content-Type': 'application/json' }
    });

    const auditLog = {
      timestamp: new Date().toISOString(),
      correlationId: uuidv4(),
      action: 'ab_test_evaluation',
      payloadHash: crypto.createHash('sha256').update(JSON.stringify(payload)).digest('hex'),
      resultStatus: evaluationResult.data?.status || 'unknown',
      latencyMs: Math.round((performance.now() - startTime) * 100) / 100,
      governanceTag: 'ai-flow-optimization',
      complianceLevel: 'high'
    };

    return { evaluationResult, auditLog };
  }
}

// Execution example
async function runEvaluation() {
  const evaluator = new CognigyABTestEvaluator('your_client_id', 'your_client_secret', 'https://your-tenant.cognigy.com');
  
  const testPayload = {
    testCampaignId: '550e8400-e29b-41d4-a716-446655440000',
    variants: [
      { variantId: 'flow_v1', currentSampleSize: 4500, conversionCount: 1800, metricValue: 0.40 },
      { variantId: 'flow_v2', currentSampleSize: 4600, conversionCount: 2300, metricValue: 0.50 }
    ],
    significanceLevel: 0.05,
    maxSampleSize: 10000,
    winnerDeclarationTrigger: true
  };

  try {
    const result = await evaluator.evaluateTest(testPayload);
    console.log('Evaluation completed:', JSON.stringify(result, null, 2));
  } catch (error) {
    console.error('Evaluation failed:', error.message);
  }
}

runEvaluation();

Common Errors & Debugging

Error: 400 Bad Request

• What causes it: The payload violates Cognigy.AI schema constraints, exceeds the maximum sample size limit, or contains invalid UUID formats for campaign references.
• How to fix it: Verify the testCampaignId matches UUID v4 format. Ensure maxSampleSize does not exceed 50000. Check that variant arrays contain between two and four elements.
• Code showing the fix: The EvaluatePayloadSchema validation block throws explicit error messages with field paths. Review the error string to identify the exact parameter causing rejection.

Error: 401 Unauthorized

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the registered application lacks the ab-tests:write scope.
• How to fix it: Refresh the token using the authentication handler. Verify the OAuth client registration in the Cognigy.AI admin portal includes all required scopes.
• Code showing the fix: The getAccessToken() method automatically refreshes tokens when Date.now() >= this.auth.expiresAt. Ensure the client ID and secret match the registered server-to-server application.

Error: 429 Too Many Requests

• What causes it: The analytics engine rate limit is reached during concurrent evaluation submissions or bulk metric aggregation.
• How to fix it: Implement exponential backoff. The submission loop reads the Retry-After header and pauses execution before retrying.
• Code showing the fix: The submitEvaluation retry loop checks error.response?.status === 429 and delays using setTimeout with the header value or a two-second default.

Error: 500 Internal Server Error

• What causes it: The analytics engine encounters a timeout during variant matrix aggregation or the significance directive conflicts with internal calculation constraints.
• How to fix it: Reduce the maxSampleSize parameter. Verify metric values fall within expected numerical ranges. Retry after a brief delay to allow engine recovery.
• Code showing the fix: Catch 5xx responses in the submission loop and trigger a manual retry with a fixed delay if the automatic retry count is exhausted.

Official References

• Cognigy.AI REST API v1 Documentation
• Cognigy.AI OAuth 2.0 Client Credentials Flow
• Cognigy.AI A/B Testing and Analytics Endpoints
• Cognigy.AI Webhook Integration Guide