Managing NICE Cognigy.AI LLM Token Usage via REST API with Node.js

What You Will Build

• A Node.js module that configures LLM token quotas, validates budgets against gateway constraints, applies changes atomically, triggers cost tracking, syncs with finance dashboards, tracks latency and adherence, generates audit logs, and exposes an automated token manager.
• This uses the NICE Cognigy.AI REST API version 1.0 for LLM usage quota management and model metadata retrieval.
• The implementation covers Node.js 18+ with axios, zod, and native crypto.

Prerequisites

• OAuth client type: Confidential client (Client Credentials Grant)
• Required scopes: llm:read, llm:write, usage:read, quota:manage, webhook:write
• API version: Cognigy.AI REST API v1 (/v1/)
• Language/runtime: Node.js 18+ (ESM modules)
• External dependencies: axios, zod, dotenv
• Environment variables: COGNIGY_BASE_URL, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET, FINANCE_WEBHOOK_URL, MAX_TOKEN_QUOTA

Authentication Setup

Cognigy.AI uses standard OAuth 2.0 Client Credentials flow. The token endpoint resides at https://auth.cognigy.ai/oauth/token. You must cache the token and refresh it before expiration to avoid 401 interruptions during quota updates.

import axios from 'axios';

const AUTH_URL = 'https://auth.cognigy.ai/oauth/token';

export async function acquireAuthToken(clientId, clientSecret) {
  const payload = new URLSearchParams({
    grant_type: 'client_credentials',
    client_id: clientId,
    client_secret: clientSecret,
    scope: 'llm:read llm:write usage:read quota:manage webhook:write'
  });

  const response = await axios.post(AUTH_URL, payload, {
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
  });

  if (response.status !== 200) {
    throw new Error(`Authentication failed with status ${response.status}`);
  }

  return {
    token: response.data.access_token,
    expiresIn: response.data.expires_in,
    issuedAt: Date.now()
  };
}

Expected Response:

{
  "access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
  "token_type": "Bearer",
  "expires_in": 3600,
  "scope": "llm:read llm:write usage:read quota:manage webhook:write"
}

Implementation

Step 1: Schema Validation & Payload Construction

You must construct management payloads that reference specific model IDs, define token budget matrices, and include quota enforcement directives. The Cognigy.AI gateway rejects malformed quota objects. Use zod to enforce strict schema validation before transmission.

import { z } from 'zod';

const EnforcementDirectiveSchema = z.enum(['STRICT', 'WARN', 'BYPASS']);
const BudgetMatrixSchema = z.object({
  dailyLimit: z.number().int().positive(),
  monthlyLimit: z.number().int().positive(),
  perRequestLimit: z.number().int().positive(),
  currency: z.enum(['USD', 'EUR', 'GBP']).default('USD')
});

const QuotaPayloadSchema = z.object({
  modelId: z.string().uuid(),
  tenantId: z.string().uuid(),
  budgetMatrix: BudgetMatrixSchema,
  enforcementDirective: EnforcementDirectiveSchema,
  metadata: z.object({
    costCenter: z.string(),
    environment: z.enum(['production', 'staging', 'development'])
  })
});

export function validateQuotaPayload(rawPayload) {
  const result = QuotaPayloadSchema.safeParse(rawPayload);
  if (!result.success) {
    const errors = result.error.errors.map(err => `${err.path.join('.')}: ${err.message}`);
    throw new Error(`Schema validation failed: ${errors.join(', ')}`);
  }
  return result.data;
}

Step 2: Usage Anomaly Detection & Model Complexity Verification

Before applying quotas, you must verify that the requested budget aligns with historical usage patterns and model complexity tiers. The AI gateway enforces maximum quota level limits to prevent service interruption failures. This pipeline checks for statistical anomalies and validates model architecture constraints.

export function detectUsageAnomaly(currentUsage, historicalDailyAverages) {
  const mean = historicalDailyAverages.reduce((a, b) => a + b, 0) / historicalDailyAverages.length;
  const variance = historicalDailyAverages.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / historicalDailyAverages.length;
  const stdDev = Math.sqrt(variance);
  
  if (stdDev === 0) return false;
  
  const zScore = (currentUsage - mean) / stdDev;
  return Math.abs(zScore) > 2.5;
}

export async function verifyModelComplexity(modelId, axiosInstance, maxQuotaLimit) {
  const response = await axiosInstance.get(`/llm/models/${modelId}`);
  const model = response.data;
  
  const complexityMatrix = {
    'gpt-4': { tier: 'HIGH', maxTokens: 100000 },
    'gpt-3.5-turbo': { tier: 'MEDIUM', maxTokens: 50000 },
    'claude-3-opus': { tier: 'HIGH', maxTokens: 120000 },
    'claude-3-sonnet': { tier: 'MEDIUM', maxTokens: 80000 }
  };

  const config = complexityMatrix[model.providerModelId] || { tier: 'LOW', maxTokens: 30000 };
  
  if (model.providerModelId.includes('custom') && config.tier !== 'LOW') {
    throw new Error(`Custom models require LOW tier validation before quota assignment.`);
  }

  if (maxQuotaLimit > config.maxTokens) {
    throw new Error(`Quota ${maxQuotaLimit} exceeds gateway maximum ${config.maxTokens} for ${model.providerModelId}`);
  }

  return { verified: true, tier: config.tier, maxAllowed: config.maxTokens };
}

Step 3: Atomic PUT Operation & Format Verification

Quota updates must be applied atomically via PUT to /v1/llm/usage-quotas/{quotaId}. The gateway requires format verification and returns a 200 OK with the updated quota object. You must implement retry logic for 429 Too Many Requests to handle rate-limit cascades.

import crypto from 'crypto';

export async function applyQuotaAtomically(axiosInstance, quotaId, validatedPayload, retryCount = 3) {
  const url = `/llm/usage-quotas/${quotaId}`;
  
  for (let attempt = 1; attempt <= retryCount; attempt++) {
    try {
      const response = await axiosInstance.put(url, validatedPayload, {
        headers: {
          'Content-Type': 'application/json',
          'Idempotency-Key': crypto.randomUUID()
        }
      });

      if (response.status === 200) {
        return {
          success: true,
          data: response.data,
          latencyMs: response.headers['x-response-time-ms'] || 0
        };
      }
    } catch (error) {
      if (error.response?.status === 429 && attempt < retryCount) {
        const retryAfter = error.response.headers['retry-after'] || 2;
        await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
        continue;
      }
      throw error;
    }
  }
}

HTTP Request/Response Cycle:

• Method: PUT
• Path: /v1/llm/usage-quotas/550e8400-e29b-41d4-a716-446655440000
• Headers: Authorization: Bearer <token>, Content-Type: application/json, Idempotency-Key: <uuid>
• Request Body:

{
  "modelId": "f47ac10b-58cc-4372-a567-0e02b2c3d479",
  "tenantId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "budgetMatrix": {
    "dailyLimit": 25000,
    "monthlyLimit": 600000,
    "perRequestLimit": 8000,
    "currency": "USD"
  },
  "enforcementDirective": "STRICT",
  "metadata": {
    "costCenter": "AI-OPS-01",
    "environment": "production"
  }
}

• Response Body:

{
  "id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "ACTIVE",
  "updatedAt": "2024-05-15T14:32:00Z",
  "appliedBy": "service-account",
  "budgetMatrix": {
    "dailyLimit": 25000,
    "monthlyLimit": 600000,
    "perRequestLimit": 8000,
    "currency": "USD"
  },
  "enforcementDirective": "STRICT",
  "auditTrail": {
    "version": 4,
    "checksum": "a1b2c3d4e5f6"
  }
}

Step 4: Webhook Synchronization & Audit Logging

You must synchronize quota changes with external finance dashboards via webhook callbacks. This ensures alignment between AI gateway constraints and organizational cost governance. The system records audit logs with checksums for compliance.

export async function syncFinanceWebhook(webhookUrl, quotaData) {
  const payload = {
    event: 'QUOTA_UPDATED',
    timestamp: new Date().toISOString(),
    data: {
      quotaId: quotaData.id,
      modelId: quotaData.modelId,
      dailyBudget: quotaData.budgetMatrix.dailyLimit,
      enforcement: quotaData.enforcementDirective,
      costCenter: quotaData.metadata.costCenter
    }
  };

  await axios.post(webhookUrl, payload, {
    headers: { 'Content-Type': 'application/json' },
    timeout: 5000
  });
}

export function generateAuditLog(action, payload, response, latencyMs) {
  const checksum = crypto.createHash('sha256').update(JSON.stringify(payload)).digest('hex').slice(0, 16);
  return {
    timestamp: new Date().toISOString(),
    action,
    quotaId: payload.id || 'pending',
    payloadChecksum: checksum,
    status: response.success ? 'COMMITTED' : 'FAILED',
    latencyMs,
    governanceTag: 'COST_CONTROL_V1'
  };
}

Step 5: Latency Tracking & Budget Adherence Calculation

Track management latency and budget adherence rates to evaluate token efficiency. The manager calculates adherence by comparing applied quotas against actual consumption metrics retrieved from /v1/usage/llm/consumption.

export async function calculateBudgetAdherence(axiosInstance, modelId) {
  const response = await axiosInstance.get(`/usage/llm/consumption`, {
    params: { modelId, timeframe: '30d' }
  });

  const consumption = response.data.totalTokensUsed;
  const quota = response.data.currentQuota;
  
  const adherenceRate = quota > 0 ? (consumption / quota) * 100 : 0;
  const efficiencyScore = adherenceRate <= 100 ? 100 - adherenceRate : 0;

  return {
    modelId,
    tokensUsed: consumption,
    quotaLimit: quota,
    adherenceRate: parseFloat(adherenceRate.toFixed(2)),
    efficiencyScore: parseFloat(efficiencyScore.toFixed(2)),
    status: adherenceRate > 95 ? 'CRITICAL' : adherenceRate > 80 ? 'WARNING' : 'HEALTHY'
  };
}

Complete Working Example

The following module combines all components into a production-ready token manager. It handles authentication, validation, anomaly detection, atomic updates, webhook sync, audit logging, and adherence tracking.

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

dotenv.config();

const BASE_URL = process.env.COGNIGY_BASE_URL || 'https://us-east-1.api.cognigy.ai/v1';
const AUTH_URL = 'https://auth.cognigy.ai/oauth/token';
const FINANCE_WEBHOOK = process.env.FINANCE_WEBHOOK_URL;
const MAX_QUOTA_LIMIT = parseInt(process.env.MAX_TOKEN_QUOTA || '100000', 10);

// Schemas
const EnforcementDirectiveSchema = z.enum(['STRICT', 'WARN', 'BYPASS']);
const BudgetMatrixSchema = z.object({
  dailyLimit: z.number().int().positive(),
  monthlyLimit: z.number().int().positive(),
  perRequestLimit: z.number().int().positive(),
  currency: z.enum(['USD', 'EUR', 'GBP']).default('USD')
});

const QuotaPayloadSchema = z.object({
  modelId: z.string().uuid(),
  tenantId: z.string().uuid(),
  budgetMatrix: BudgetMatrixSchema,
  enforcementDirective: EnforcementDirectiveSchema,
  metadata: z.object({
    costCenter: z.string(),
    environment: z.enum(['production', 'staging', 'development'])
  })
});

class CognigyTokenManager {
  constructor() {
    this.tokenCache = null;
    this.auditLog = [];
    this.axiosInstance = axios.create({
      baseURL: BASE_URL,
      timeout: 10000
    });
  }

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

    const payload = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: process.env.COGNIGY_CLIENT_ID,
      client_secret: process.env.COGNIGY_CLIENT_SECRET,
      scope: 'llm:read llm:write usage:read quota:manage webhook:write'
    });

    const res = await axios.post(AUTH_URL, payload, {
      headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
    });

    this.tokenCache = {
      token: res.data.access_token,
      expiresAt: Date.now() + (res.data.expires_in * 1000) - 60000
    };

    this.axiosInstance.defaults.headers.common['Authorization'] = `Bearer ${this.tokenCache.token}`;
    return this.tokenCache.token;
  }

  validatePayload(raw) {
    const result = QuotaPayloadSchema.safeParse(raw);
    if (!result.success) {
      const errors = result.error.errors.map(e => `${e.path.join('.')}: ${e.message}`);
      throw new Error(`Schema validation failed: ${errors.join(', ')}`);
    }
    return result.data;
  }

  async verifyComplexity(modelId) {
    const res = await this.axiosInstance.get(`/llm/models/${modelId}`);
    const model = res.data;
    const complexityMatrix = {
      'gpt-4': { tier: 'HIGH', maxTokens: 100000 },
      'gpt-3.5-turbo': { tier: 'MEDIUM', maxTokens: 50000 },
      'claude-3-opus': { tier: 'HIGH', maxTokens: 120000 }
    };
    const config = complexityMatrix[model.providerModelId] || { tier: 'LOW', maxTokens: 30000 };
    
    if (model.providerModelId.includes('custom') && config.tier !== 'LOW') {
      throw new Error('Custom models require LOW tier validation.');
    }
    if (MAX_QUOTA_LIMIT > config.maxTokens) {
      throw new Error(`Quota exceeds gateway maximum ${config.maxTokens} for ${model.providerModelId}`);
    }
    return { verified: true, tier: config.tier };
  }

  detectAnomaly(current, historical) {
    const mean = historical.reduce((a, b) => a + b, 0) / historical.length;
    const variance = historical.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / historical.length;
    const stdDev = Math.sqrt(variance);
    if (stdDev === 0) return false;
    return Math.abs((current - mean) / stdDev) > 2.5;
  }

  async applyQuota(quotaId, payload, historicalUsage = []) {
    const validated = this.validatePayload(payload);
    await this.verifyComplexity(validated.modelId);
    
    const currentUsage = validated.budgetMatrix.dailyLimit;
    if (this.detectAnomaly(currentUsage, historicalUsage)) {
      throw new Error('Usage anomaly detected. Budget adjustment requires manual approval.');
    }

    const startTime = performance.now();
    let response;
    let attempt = 1;
    
    while (attempt <= 3) {
      try {
        response = await this.axiosInstance.put(`/llm/usage-quotas/${quotaId}`, validated, {
          headers: { 'Idempotency-Key': crypto.randomUUID() }
        });
        break;
      } catch (err) {
        if (err.response?.status === 429 && attempt < 3) {
          await new Promise(r => setTimeout(r, (err.response.headers['retry-after'] || 2) * 1000));
          attempt++;
          continue;
        }
        throw err;
      }
    }

    const latencyMs = performance.now() - startTime;
    const auditEntry = {
      timestamp: new Date().toISOString(),
      action: 'QUOTA_PUT',
      quotaId,
      status: response.status === 200 ? 'COMMITTED' : 'FAILED',
      latencyMs: latencyMs.toFixed(2),
      checksum: crypto.createHash('sha256').update(JSON.stringify(validated)).digest('hex').slice(0, 16)
    };
    this.auditLog.push(auditEntry);

    if (FINANCE_WEBHOOK) {
      try {
        await axios.post(FINANCE_WEBHOOK, {
          event: 'QUOTA_UPDATED',
          timestamp: auditEntry.timestamp,
          data: { quotaId, dailyBudget: validated.budgetMatrix.dailyLimit, enforcement: validated.enforcementDirective }
        }, { timeout: 5000 });
      } catch (webhookErr) {
        console.warn('Webhook sync failed:', webhookErr.message);
      }
    }

    return { success: true, data: response.data, audit: auditEntry };
  }

  async getAdherence(modelId) {
    const res = await this.axiosInstance.get('/usage/llm/consumption', { params: { modelId, timeframe: '30d' } });
    const used = res.data.totalTokensUsed;
    const quota = res.data.currentQuota;
    const rate = quota > 0 ? (used / quota) * 100 : 0;
    return {
      modelId,
      used,
      quota,
      adherenceRate: parseFloat(rate.toFixed(2)),
      status: rate > 95 ? 'CRITICAL' : rate > 80 ? 'WARNING' : 'HEALTHY'
    };
  }

  getAuditLog() {
    return [...this.auditLog];
  }
}

// Execution block
(async () => {
  const manager = new CognigyTokenManager();
  await manager.getAuthToken();

  try {
    const result = await manager.applyQuota(
      '550e8400-e29b-41d4-a716-446655440000',
      {
        modelId: 'f47ac10b-58cc-4372-a567-0e02b2c3d479',
        tenantId: 'a1b2c3d4-e5f6-7890-abcd-ef1234567890',
        budgetMatrix: { dailyLimit: 25000, monthlyLimit: 600000, perRequestLimit: 8000, currency: 'USD' },
        enforcementDirective: 'STRICT',
        metadata: { costCenter: 'AI-OPS-01', environment: 'production' }
      },
      [22000, 23500, 21000, 24000, 22800]
    );
    console.log('Quota applied:', result);
    console.log('Audit:', manager.getAuditLog());
  } catch (err) {
    console.error('Operation failed:', err.message);
  }
})();

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired access token, invalid client credentials, or missing llm:read/quota:manage scopes.
• Fix: Verify COGNIGY_CLIENT_ID and COGNIGY_CLIENT_SECRET. Ensure the token cache expiration logic subtracts a buffer period. Check the scope string matches exactly what the Cognigy.AI admin console configured.
• Code Fix: The getAuthToken method automatically refreshes the token when Date.now() >= expiresAt. Add explicit scope logging during development.

Error: 403 Forbidden

• Cause: The authenticated service account lacks tenant-level permissions for quota management, or the tenantId in the payload does not match the client scope.
• Fix: Assign the LLM Quota Administrator role to the OAuth client in the Cognigy.AI tenant settings. Verify the tenantId UUID matches the active workspace.
• Code Fix: Validate tenantId against the Authorization header claims before transmission.

Error: 422 Unprocessable Entity

• Cause: Payload schema mismatch, invalid enforcement directive, or budget matrix values below gateway minimums.
• Fix: Run the payload through zod validation before the HTTP call. Ensure dailyLimit and perRequestLimit align with model tier constraints.
• Code Fix: The validatePayload method throws descriptive errors on schema failure. Check the error message for exact field names.

Error: 429 Too Many Requests

• Cause: Exceeding the Cognigy.AI API rate limit (typically 100 requests per minute per tenant for quota endpoints).
• Fix: Implement exponential backoff. The applyQuota method includes a retry loop that reads the Retry-After header.
• Code Fix: Ensure the Idempotency-Key header is present on all PUT requests to prevent duplicate quota applications during retries.

Error: 5xx Internal Server Error

• Cause: AI gateway constraint violation, temporary backend instability, or quota engine synchronization failure.
• Fix: Wait 30 seconds and retry. If persistent, verify that the modelId exists and is not in a DEPROVISIONED state.
• Code Fix: Wrap the entire operation in a try-catch that logs the full response body for gateway diagnostics.

Official References

• Cognigy.AI REST API v1 Documentation
• Cognigy.AI OAuth 2.0 Client Credentials Flow
• LLM Usage & Quota Management Endpoints
• Zod Schema Validation Library
• Axios HTTP Client Documentation