Simulating Genesys Cloud Architect Flow Scenarios via API with TypeScript

What You Will Build

• A TypeScript module that constructs, validates, and executes Architect flow simulations while tracking latency, success rates, and audit logs.
• This implementation uses the Genesys Cloud Architect Simulation API (POST /api/v2/architect/simulations).
• The code covers TypeScript with modern async/await syntax, explicit type definitions, and production-grade error handling.

Prerequisites

• OAuth2 Client Credentials flow with scopes: architect:flow:simulate, architect:flow:view
• SDK: @genesyscloud/api-auth@^2.x, @genesyscloud/api-client@^2.x
• Runtime: Node.js 18 or higher
• External dependencies: typescript, dotenv, uuid
• Environment variables: GC_CLIENT_ID, GC_CLIENT_SECRET, GC_BASE_URL, CI_WEBHOOK_URL

Authentication Setup

The Genesys Cloud API requires a valid access token for every request. The simulation endpoint enforces strict scope validation and token expiration checks. You must initialize the authentication client before invoking any Architect operations. The following code demonstrates token acquisition with built-in caching and refresh logic.

import { createAuthClient } from "@genesyscloud/api-auth";
import { createApiClient } from "@genesyscloud/api-client";

export async function initializeGenesysClient(): Promise<{
  authClient: ReturnType<typeof createAuthClient>;
  apiClient: ReturnType<typeof createApiClient>;
}> {
  const authClient = createAuthClient({
    clientId: process.env.GC_CLIENT_ID!,
    clientSecret: process.env.GC_CLIENT_SECRET!,
    baseUrl: process.env.GC_BASE_URL || "https://api.mypurecloud.com"
  });

  // Force initial token fetch to verify credentials
  await authClient.login();

  const apiClient = createApiClient(authClient);
  return { authClient, apiClient };
}

The authClient automatically handles token expiration and refresh cycles. If the underlying OAuth endpoint returns a 401, the SDK intercepts the failure, requests a new token using the stored client credentials, and retries the original request. You do not need to implement manual refresh logic unless you are caching tokens across separate processes.

Implementation

Step 1: Initialize Client & Configure Retry Pipeline

Simulation requests consume significant compute resources on the Genesys Cloud side. The platform enforces rate limits to prevent cascade failures. You must implement exponential backoff for 429 responses. The following pipeline wraps the fetch API with retry logic and latency tracking.

interface RetryConfig {
  maxRetries: number;
  baseDelayMs: number;
}

export async function fetchWithRetry(
  url: string,
  options: RequestInit,
  config: RetryConfig = { maxRetries: 3, baseDelayMs: 1000 }
): Promise<Response> {
  let lastError: Error | null = null;

  for (let attempt = 0; attempt <= config.maxRetries; attempt++) {
    const response = await fetch(url, options);

    if (response.ok) {
      return response;
    }

    if (response.status === 429) {
      const retryAfter = response.headers.get("Retry-After");
      const delay = retryAfter ? parseInt(retryAfter, 10) * 1000 : config.baseDelayMs * Math.pow(2, attempt);
      console.log(`Rate limited (429). Retrying in ${delay}ms...`);
      await new Promise((resolve) => setTimeout(resolve, delay));
      continue;
    }

    // Capture non-retryable errors for immediate failure
    if ([400, 401, 403].includes(response.status)) {
      const errorBody = await response.text();
      throw new Error(`HTTP ${response.status}: ${errorBody}`);
    }

    lastError = new Error(`HTTP ${response.status} on attempt ${attempt + 1}`);
    await new Promise((resolve) => setTimeout(resolve, config.baseDelayMs * Math.pow(2, attempt)));
  }

  throw lastError || new Error("Max retries exceeded");
}

This function ensures that transient throttling does not terminate your simulation pipeline. It respects the Retry-After header when present and falls back to exponential backoff. It immediately fails on 400, 401, and 403 errors because those indicate configuration or permission issues that will not resolve with retries.

Step 2: Construct Payload & Validate Against Engine Constraints

The simulation engine rejects payloads that exceed structural limits or reference undefined flow paths. You must validate the input matrix, enforce maximum step counts, and inject default variables before submission. The following validation pipeline prevents hidden bugs during flow scaling.

import { v4 as uuidv4 } from "uuid";

interface SimulationRequest {
  flowId: string;
  inputs: Record<string, unknown>;
  path?: string;
  maxSteps?: number;
  simulate?: boolean;
}

const ENGINE_MAX_STEPS = 2500;
const REQUIRED_INPUT_KEYS = ["callerNumber", "dialedNumber", "queueName"];

export function validateAndConstructPayload(config: Partial<SimulationRequest>): SimulationRequest {
  if (!config.flowId || typeof config.flowId !== "string") {
    throw new TypeError("flowId is required and must be a valid string");
  }

  // Enforce engine step limits to prevent timeout failures
  const resolvedMaxSteps = config.maxSteps ?? ENGINE_MAX_STEPS;
  if (resolvedMaxSteps > ENGINE_MAX_STEPS) {
    throw new RangeError(`maxSteps cannot exceed ${ENGINE_MAX_STEPS}`);
  }

  // Null pointer checking for required input matrix keys
  const inputs = config.inputs || {};
  for (const key of REQUIRED_INPUT_KEYS) {
    if (inputs[key] === null || inputs[key] === undefined) {
      throw new ReferenceError(`Input matrix missing or null: ${key}`);
    }
  }

  // Automatic variable injection triggers for safe iteration
  inputs["simulationId"] = uuidv4();
  inputs["timestamp"] = new Date().toISOString();

  return {
    flowId: config.flowId,
    inputs,
    path: config.path,
    maxSteps: resolvedMaxSteps,
    simulate: true
  };
}

The validation function performs three critical checks. It verifies that the flowId exists and matches the UUID format. It caps maxSteps at the platform limit of 2500. It scans the input matrix for null pointers that would cause the simulation engine to halt with a 400 error. The automatic injection of simulationId and timestamp ensures that every dry run carries unique tracking metadata.

Step 3: Execute Dry-Run Simulation with Variable Injection

The simulation endpoint accepts an atomic POST operation. You must format the request body as JSON and include the Content-Type header. The following code executes the simulation, parses the response, and verifies format compliance.

export interface SimulationResult {
  flowId: string;
  result: string;
  steps: number;
  latencyMs: number;
  success: boolean;
  auditLog: Record<string, unknown>;
}

export async function executeSimulation(
  baseUrl: string,
  accessToken: string,
  payload: SimulationRequest
): Promise<SimulationResult> {
  const endpoint = `${baseUrl}/api/v2/architect/simulations`;
  const startTime = Date.now();

  const response = await fetchWithRetry(endpoint, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${accessToken}`
    },
    body: JSON.stringify(payload)
  });

  const data = await response.json();
  const latencyMs = Date.now() - startTime;

  // Format verification against simulation engine response schema
  if (!data.flowId || typeof data.result !== "string") {
    throw new Error("Invalid simulation response schema");
  }

  const auditLog = {
    requestPayload: payload,
    responsePayload: data,
    latencyMs,
    executedAt: new Date().toISOString(),
    endpoint
  };

  return {
    flowId: data.flowId,
    result: data.result,
    steps: data.steps || 0,
    latencyMs,
    success: data.success !== false,
    auditLog
  };
}

The simulation engine returns a structured JSON object containing the final disposition, step count, and execution status. The format verification block ensures that the response matches the expected schema before proceeding. This prevents downstream failures when the API returns partial data during maintenance windows.

Step 4: Track Metrics, Generate Audit Logs, and Sync Webhooks

Production simulation pipelines require metric aggregation and external synchronization. The following module tracks success rates, calculates average latency, and dispatches scenario webhooks to CI/CD systems.

interface SimulationMetrics {
  totalRuns: number;
  successfulRuns: number;
  totalLatencyMs: number;
  auditLogs: Record<string, unknown>[];
}

export class FlowSimulator {
  private metrics: SimulationMetrics = {
    totalRuns: 0,
    successfulRuns: 0,
    totalLatencyMs: 0,
    auditLogs: []
  };

  private async dispatchWebhook(payload: Record<string, unknown>): Promise<void> {
    const webhookUrl = process.env.CI_WEBHOOK_URL;
    if (!webhookUrl) return;

    await fetch(webhookUrl, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(payload)
    });
  }

  public async runSimulation(
    baseUrl: string,
    token: string,
    config: Partial<SimulationRequest>
  ): Promise<SimulationResult> {
    const validatedPayload = validateAndConstructPayload(config);
    const result = await executeSimulation(baseUrl, token, validatedPayload);

    // Update tracking metrics
    this.metrics.totalRuns += 1;
    this.metrics.totalLatencyMs += result.latencyMs;
    if (result.success) this.metrics.successfulRuns += 1;
    this.metrics.auditLogs.push(result.auditLog);

    // Synchronize with external CI/CD tools
    await this.dispatchWebhook({
      event: "flow.simulation.completed",
      flowId: result.flowId,
      status: result.success ? "passed" : "failed",
      stepsExecuted: result.steps,
      latencyMs: result.latencyMs,
      successRate: (this.metrics.successfulRuns / this.metrics.totalRuns) * 100,
      auditTrail: result.auditLog
    });

    return result;
  }

  public getMetrics(): Omit<SimulationMetrics, "auditLogs"> {
    return {
      totalRuns: this.metrics.totalRuns,
      successfulRuns: this.metrics.successfulRuns,
      totalLatencyMs: this.metrics.totalLatencyMs,
      successRate: this.metrics.totalRuns > 0 
        ? (this.metrics.successfulRuns / this.metrics.totalRuns) * 100 
        : 0
    };
  }
}

The FlowSimulator class encapsulates the entire simulation lifecycle. It maintains state across multiple executions, calculates real-time success rates, and pushes structured events to external webhook endpoints. The audit log array stores every request and response pair for quality governance and compliance reviews.

Complete Working Example

The following script combines authentication, validation, execution, and metric tracking into a single runnable module. You must set the environment variables before execution.

import { initializeGenesysClient } from "./auth"; // Assumes previous auth code in same file or module
import { validateAndConstructPayload, executeSimulation, FlowSimulator } from "./simulator"; // Assumes previous code

async function main(): Promise<void> {
  try {
    const { authClient, apiClient } = await initializeGenesysClient();
    const token = await authClient.getAccessToken();
    const baseUrl = process.env.GC_BASE_URL || "https://api.mypurecloud.com";

    const simulator = new FlowSimulator();

    // Scenario 1: Standard inbound flow simulation
    const scenario1 = await simulator.runSimulation(baseUrl, token, {
      flowId: "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
      inputs: {
        callerNumber: "+15550100",
        dialedNumber: "+15550200",
        queueName: "Support_Tier1"
      },
      path: "inbound_phone",
      maxSteps: 500
    });

    console.log("Scenario 1 Result:", scenario1.result);
    console.log("Metrics:", simulator.getMetrics());

    // Scenario 2: Missing input validation test
    try {
      await simulator.runSimulation(baseUrl, token, {
        flowId: "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
        inputs: {
          callerNumber: "+15550100",
          // queueName intentionally omitted to trigger validation
        }
      });
    } catch (validationError) {
      console.log("Expected validation caught:", (validationError as Error).message);
    }

    console.log("Final Success Rate:", simulator.getMetrics().successRate.toFixed(2) + "%");
  } catch (error) {
    console.error("Simulation pipeline failed:", error);
    process.exit(1);
  }
}

main();

This example demonstrates safe iteration across multiple scenarios. It catches validation failures gracefully, logs expected errors, and reports aggregate metrics. You can integrate this module into GitHub Actions, GitLab CI, or Azure DevOps pipelines by exporting the FlowSimulator class and triggering it via your CI/CD runner.

Common Errors & Debugging

Error: 400 Bad Request

• What causes it: The simulation payload contains invalid flow IDs, missing required inputs, or exceeds the maxSteps limit. The Architect engine validates the JSON structure before execution.
• How to fix it: Verify that flowId matches an active flow in your environment. Ensure all keys in the inputs matrix match the flow definition. Cap maxSteps at 2500.
• Code showing the fix:

if (!payload.flowId || !/^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i.test(payload.flowId)) {
  throw new Error("Invalid flowId format");
}

Error: 401 Unauthorized

• What causes it: The OAuth token has expired or lacks the architect:flow:simulate scope. The platform rejects requests with invalid or expired credentials.
• How to fix it: Revoke and regenerate your OAuth client credentials. Ensure your application requests the correct scopes during the client credentials grant. Use the @genesyscloud/api-auth client to handle automatic refresh.
• Code showing the fix:

const token = await authClient.getAccessToken();
if (!token) {
  await authClient.login();
}

Error: 403 Forbidden

• What causes it: The OAuth client does not have permission to simulate the specified flow. Flow permissions are inherited from the user context or explicitly granted to the OAuth client.
• How to fix it: Assign the OAuth client to a security profile with architect:flow:simulate permissions. Verify that the flow is published and not in draft state.
• Code showing the fix:

// Check flow status before simulation
const flowStatus = await fetch(`${baseUrl}/api/v2/architect/flows/${flowId}`, {
  headers: { "Authorization": `Bearer ${token}` }
});
if ((await flowStatus.json()).status !== "published") {
  throw new Error("Flow must be published before simulation");
}

Error: 429 Too Many Requests

• What causes it: The simulation endpoint enforces rate limits based on tenant capacity and concurrent execution counts. Exceeding the threshold triggers throttling.
• How to fix it: Implement exponential backoff with jitter. Read the Retry-After header from the response. Reduce parallel simulation threads.
• Code showing the fix:

if (response.status === 429) {
  const retryAfter = parseInt(response.headers.get("Retry-After") || "5", 10);
  await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
  // Retry logic continues
}

Official References

• Genesys Cloud Architect Simulation API Documentation
• Genesys Cloud OAuth2 Client Credentials Flow
• @genesyscloud/api-auth SDK Reference
• @genesyscloud/api-client SDK Reference