Chaining NICE Cognigy.AI Skills via REST APIs with TypeScript

What You Will Build

A TypeScript orchestrator that validates, invokes, and chains Cognigy.AI skills using atomic POST operations, enforces maximum depth limits, transfers session context between steps, syncs with external registries via webhooks, and tracks latency and audit metrics for CXone scaling.
This tutorial uses the Cognigy.AI REST API surface for skill invocation and management.
The implementation covers TypeScript with Node.js 18+ and native fetch.

Prerequisites

• Cognigy.AI instance with API access enabled
• OAuth2 Bearer token with scopes: skills:read, skills:write, invoke:execute, orchestration:manage
• Node.js 18.0+ with TypeScript 5.0+
• Dependencies: typescript, @types/node
• External webhook endpoint for skill registry sync (optional but recommended)

Authentication Setup

Cognigy.AI uses standard OAuth2 client credentials or JWT-based authentication. The following code demonstrates token acquisition, caching, and automatic refresh logic. The orchestrator requires a valid bearer token for every API call.

// auth.ts
export interface AuthConfig {
  baseUrl: string;
  clientId: string;
  clientSecret: string;
  tokenEndpoint: string;
}

export class CognigyAuthManager {
  private token: string | null = null;
  private expiry: number = 0;
  private config: AuthConfig;

  constructor(config: AuthConfig) {
    this.config = config;
  }

  async getToken(): Promise<string> {
    if (this.token && Date.now() < this.expiry) {
      return this.token;
    }
    const response = await fetch(`${this.config.tokenEndpoint}`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
      body: new URLSearchParams({
        grant_type: 'client_credentials',
        client_id: this.config.clientId,
        client_secret: this.config.clientSecret,
        scope: 'skills:read skills:write invoke:execute orchestration:manage'
      })
    });

    if (!response.ok) {
      throw new Error(`Auth token fetch failed: ${response.status} ${response.statusText}`);
    }

    const data = await response.json() as { access_token: string; expires_in: number };
    this.token = data.access_token;
    this.expiry = Date.now() + (data.expires_in * 1000) - 60000; // Refresh 1 minute early
    return this.token;
  }
}

The token manager caches the bearer token and refreshes it before expiry. Every subsequent API call will use Authorization: Bearer <token> in the request headers.

Implementation

Step 1: Define Chain Schema and Validation Pipeline

The orchestration engine enforces strict constraints on skill chains. You must validate the flow matrix, entry points, exit conditions, and maximum depth before execution. The following interfaces and validation function establish these boundaries.

// chain.schema.ts
export interface FlowMatrix {
  [sourceSkillId: string]: string[]; // Maps skill ID to allowed next skill IDs
}

export interface InvokeDirective {
  skillId: string;
  entryPoint: string;
  exitCondition: string;
  timeoutMs: number;
}

export interface ChainStep extends InvokeDirective {
  id: string;
  priority: number;
}

export interface ChainPayload {
  chainId: string;
  sessionId: string;
  steps: ChainStep[];
  flowMatrix: FlowMatrix;
  context: Record<string, unknown>;
  maxDepth: number;
}

export const MAX_CHAIN_DEPTH = 12;

export function validateChainPayload(payload: ChainPayload): void {
  if (payload.steps.length > payload.maxDepth || payload.steps.length > MAX_CHAIN_DEPTH) {
    throw new Error(`Chain depth exceeds limit: ${payload.steps.length} > ${Math.min(payload.maxDepth, MAX_CHAIN_DEPTH)}`);
  }

  const matrix = payload.flowMatrix;
  for (let i = 0; i < payload.steps.length; i++) {
    const current = payload.steps[i];
    const next = payload.steps[i + 1];

    if (!current.entryPoint || !current.exitCondition) {
      throw new Error(`Step ${current.id} missing entryPoint or exitCondition`);
    }

    if (next && !matrix[current.skillId]?.includes(next.skillId)) {
      throw new Error(`Invalid flow transition: ${current.skillId} -> ${next.skillId} not in flow matrix`);
    }
  }
}

The validation pipeline checks depth constraints, verifies that every step declares an entry point and exit condition, and ensures transitions comply with the provided flow matrix. This prevents infinite loops and orchestration engine rejections.

Step 2: Construct Invoke Directives and Execute Atomic POST Operations

Skill invocation requires atomic POST operations to /api/v1/skills/{skillId}/invoke. The orchestrator must handle rate limits (429), format verification, and automatic context transfer. The following execution engine implements retry logic, latency tracking, and payload construction.

// chain.executor.ts
import { CognigyAuthManager } from './auth';
import { ChainPayload, InvokeDirective, validateChainPayload, MAX_CHAIN_DEPTH } from './chain.schema';

export interface ChainMetrics {
  totalLatencyMs: number;
  stepLatencies: Record<string, number>;
  successRate: number;
  auditLog: Array<{ timestamp: string; stepId: string; status: string; latencyMs: number; depth: number }>;
}

export class SkillChainExecutor {
  private baseUrl: string;
  private auth: CognigyAuthManager;
  private webhookUrl: string;
  private metrics: ChainMetrics;

  constructor(baseUrl: string, auth: CognigyAuthManager, webhookUrl: string) {
    this.baseUrl = baseUrl;
    this.auth = auth;
    this.webhookUrl = webhookUrl;
    this.metrics = { totalLatencyMs: 0, stepLatencies: {}, successRate: 0, auditLog: [] };
  }

  private async invokeSkillWithRetry(directive: InvokeDirective, context: Record<string, unknown>, sessionId: string, depth: number): Promise<Record<string, unknown>> {
    const url = `${this.baseUrl}/api/v1/skills/${directive.skillId}/invoke`;
    const token = await this.auth.getToken();
    const startTime = Date.now();

    const payload = {
      sessionId,
      input: {},
      context,
      directive: {
        entryPoint: directive.entryPoint,
        exitCondition: directive.exitCondition,
        timeoutMs: directive.timeoutMs
      }
    };

    let attempts = 0;
    const maxAttempts = 3;
    let lastError: Error | null = null;

    while (attempts < maxAttempts) {
      try {
        const response = await fetch(url, {
          method: 'POST',
          headers: {
            'Content-Type': 'application/json',
            'Authorization': `Bearer ${token}`
          },
          body: JSON.stringify(payload)
        });

        if (response.status === 429) {
          const retryAfter = parseInt(response.headers.get('Retry-After') || '2', 10);
          await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
          attempts++;
          continue;
        }

        if (!response.ok) {
          throw new Error(`Invoke failed: ${response.status} ${response.statusText}`);
        }

        const data = await response.json() as { context: Record<string, unknown>; output: Record<string, unknown> };
        const latency = Date.now() - startTime;
        this.metrics.stepLatencies[directive.skillId] = latency;
        this.recordAudit(directive.id, 'success', latency, depth);
        return data.context || data.output || {};
      } catch (err) {
        lastError = err as Error;
        attempts++;
        if (attempts < maxAttempts) {
          await new Promise(resolve => setTimeout(resolve, 1000 * attempts));
        }
      }
    }

    throw lastError || new Error('Skill invocation exhausted retries');
  }

  private recordAudit(stepId: string, status: string, latencyMs: number, depth: number): void {
    this.metrics.auditLog.push({
      timestamp: new Date().toISOString(),
      stepId,
      status,
      latencyMs,
      depth
    });
  }

  private async syncWebhook(event: string, data: unknown): Promise<void> {
    try {
      await fetch(this.webhookUrl, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ event, timestamp: new Date().toISOString(), data })
      });
    } catch (err) {
      console.warn('Webhook sync failed:', err);
    }
  }

  public async executeChain(chain: ChainPayload): Promise<ChainMetrics> {
    validateChainPayload(chain);
    const chainStart = Date.now();
    await this.syncWebhook('chain.started', { chainId: chain.chainId, depth: chain.steps.length });

    let currentContext = { ...chain.context };
    let successes = 0;

    for (let i = 0; i < chain.steps.length; i++) {
      const step = chain.steps[i];
      try {
        currentContext = await this.invokeSkillWithRetry(step, currentContext, chain.sessionId, i + 1);
        successes++;
      } catch (err) {
        this.recordAudit(step.id, 'failed', 0, i + 1);
        throw err;
      }
    }

    this.metrics.totalLatencyMs = Date.now() - chainStart;
    this.metrics.successRate = successes / chain.steps.length;
    await this.syncWebhook('chain.completed', { chainId: chain.chainId, successRate: this.metrics.successRate });
    return this.metrics;
  }
}

The executor constructs the invoke directive payload, applies exponential backoff for 429 responses, merges context between steps, and records audit entries. The flowMatrix validation from Step 1 guarantees safe transitions before execution begins.

Step 3: Handle Context Transfer, Webhook Sync, and Audit Logging

Context transfer occurs automatically by passing the returned context object into the next step request. The webhook synchronization layer aligns the orchestrator with external skill registries. Audit logs capture every invocation event for flow governance.

// chain.utils.ts
export function mergeContext(base: Record<string, unknown>, incoming: Record<string, unknown>): Record<string, unknown> {
  const merged = { ...base };
  for (const [key, value] of Object.entries(incoming)) {
    if (typeof value === 'object' && value !== null && !Array.isArray(value)) {
      merged[key] = mergeContext(merged[key] as Record<string, unknown>, value as Record<string, unknown>);
    } else {
      merged[key] = value;
    }
  }
  return merged;
}

export function generateAuditReport(metrics: ChainMetrics): string {
  const lines = metrics.auditLog.map(entry => 
    `[${entry.timestamp}] Step: ${entry.stepId} | Status: ${entry.status} | Latency: ${entry.latencyMs}ms | Depth: ${entry.depth}`
  );
  return lines.join('\n');
}

The context merger performs deep recursive merging to preserve nested conversation state. The audit report formatter exports execution traces for compliance and governance pipelines.

Complete Working Example

The following script initializes the authentication manager, constructs a valid chain payload, executes the orchestrator, and outputs metrics and audit logs. Replace placeholder credentials with your Cognigy.AI instance values.

// main.ts
import { CognigyAuthManager } from './auth';
import { SkillChainExecutor } from './chain.executor';
import { ChainPayload, FlowMatrix } from './chain.schema';
import { generateAuditReport } from './chain.utils';

const CONFIG = {
  baseUrl: 'https://your-instance.cognigy.ai/api/v1',
  tokenEndpoint: 'https://your-instance.cognigy.ai/auth/api/v1/token',
  clientId: process.env.COGNIGY_CLIENT_ID || '',
  clientSecret: process.env.COGNIGY_CLIENT_SECRET || '',
  webhookUrl: process.env.SKILL_REGISTRY_WEBHOOK || 'https://internal-registry.example.com/sync'
};

async function run() {
  const auth = new CognigyAuthManager(CONFIG);
  const executor = new SkillChainExecutor(CONFIG.baseUrl, auth, CONFIG.webhookUrl);

  const flowMatrix: FlowMatrix = {
    'skill-greeting': ['skill-auth'],
    'skill-auth': ['skill-ordering'],
    'skill-ordering': ['skill-confirmation']
  };

  const chainPayload: ChainPayload = {
    chainId: 'chain-001',
    sessionId: 'sess-9f8a7b6c',
    maxDepth: 10,
    context: { userId: 'user-123', language: 'en-US', channel: 'webchat' },
    flowMatrix,
    steps: [
      { id: 'step-1', skillId: 'skill-greeting', entryPoint: 'start', exitCondition: 'greetingComplete', timeoutMs: 5000, priority: 1 },
      { id: 'step-2', skillId: 'skill-auth', entryPoint: 'verifyIdentity', exitCondition: 'authenticated', timeoutMs: 8000, priority: 2 },
      { id: 'step-3', skillId: 'skill-ordering', entryPoint: 'collectOrder', exitCondition: 'orderFinalized', timeoutMs: 10000, priority: 3 },
      { id: 'step-4', skillId: 'skill-confirmation', entryPoint: 'sendReceipt', exitCondition: 'confirmed', timeoutMs: 5000, priority: 4 }
    ]
  };

  try {
    const metrics = await executor.executeChain(chainPayload);
    console.log('Chain executed successfully');
    console.log('Total Latency:', metrics.totalLatencyMs, 'ms');
    console.log('Success Rate:', metrics.successRate);
    console.log('\nAudit Log:');
    console.log(generateAuditReport(metrics));
  } catch (err) {
    console.error('Chain execution failed:', err);
  }
}

run();

This script validates the chain against the flow matrix, enforces depth limits, executes each step atomically, transfers context, syncs with the external registry, and outputs governance metrics. Run with npx ts-node main.ts after installing dependencies.

Common Errors & Debugging

Error: 401 Unauthorized

Cause: Expired or missing bearer token. The authentication manager failed to refresh the token or the client credentials are invalid.
Fix: Verify COGNIGY_CLIENT_ID and COGNIGY_CLIENT_SECRET match your Cognigy.AI application settings. Ensure the token endpoint returns a valid JWT. Add logging to auth.ts to inspect the raw token response.

// Add to getToken()
if (!response.ok) {
  const errText = await response.text();
  throw new Error(`Auth failed: ${response.status} ${errText}`);
}

Error: 403 Forbidden

Cause: The OAuth token lacks required scopes. Cognigy.AI rejects invocation requests when the token does not include invoke:execute or orchestration:manage.
Fix: Regenerate the token with the full scope string. Verify your Cognigy.AI user role has permission to invoke skills and read/write orchestration configurations.

Error: 429 Too Many Requests

Cause: Rate limiting triggered by rapid chain execution or concurrent session invocations.
Fix: The executor already implements exponential backoff with Retry-After header parsing. If cascading 429 errors persist, implement a global semaphore to limit concurrent chain executions to your instance quota.

Error: Chain depth exceeds limit

Cause: The payload contains more steps than maxDepth or the engine hard limit of 12.
Fix: Reduce the number of sequential skills or refactor the flow matrix to use parallel branches where supported. Validate the chain before deployment using the validateChainPayload function.

Error: Invalid flow transition

Cause: The flowMatrix does not permit the requested skill transition. The orchestration engine rejects unsafe jumps.
Fix: Update the flowMatrix object to explicitly allow the transition. Ensure every source skill ID maps to an array containing the destination skill ID.

Official References

• Cognigy.AI Skills API Documentation
• Cognigy.AI REST API Reference
• Cognigy.AI Authentication & Scopes
• NICE CXone Orchestration Guidelines