Inject Genesys Cloud Agent Assist Real-Time Prompts via REST API with TypeScript

What You Will Build

A TypeScript module that injects real-time agent assist prompts into active Genesys Cloud conversations using the Agent Assist REST API. The code constructs injection payloads with prompt ID references, trigger condition matrices, and display timing directives, validates schemas against UI rendering constraints and maximum concurrent prompt limits, handles atomic POST delivery with format verification, synchronizes with external knowledge systems via webhook callbacks, and generates audit logs for compliance.

Prerequisites

• Genesys Cloud OAuth 2.0 client credentials with agent-assist:prompt:write and analytics:events:read scopes
• Node.js 18 or later
• @genesyscloud/purecloud-platform-client-v2 v4.0+
• zod v3.22+ for runtime schema validation
• axios v1.6+ for webhook callbacks
• TypeScript 5.0+

Authentication Setup

The Genesys Cloud Node SDK handles OAuth 2.0 client credentials flows internally. You initialize the platform client, configure the environment, and request a token with explicit scopes. The SDK caches the token and handles automatic refresh before expiration.

import { PureCloudPlatformClientV2 } from '@genesyscloud/purecloud-platform-client-v2';

export class GenesysAuthManager {
  private client: PureCloudPlatformClientV2;

  constructor(
    private clientId: string,
    private clientSecret: string,
    private envUrl: string = 'https://api.mypurecloud.com'
  ) {
    this.client = new PureCloudPlatformClientV2();
    this.client.setEnvironment(this.envUrl);
  }

  async authenticate(): Promise<PureCloudPlatformClientV2> {
    try {
      await this.client.loginApplication({
        body: {
          clientId: this.clientId,
          clientSecret: this.clientSecret,
          grantType: 'client_credentials',
          scope: 'agent-assist:prompt:write analytics:events:read'
        }
      });
      return this.client;
    } catch (error: any) {
      const status = error.status || error.statusCode;
      if (status === 401) {
        throw new Error('Authentication failed: Invalid client credentials or expired secret.');
      }
      if (status === 403) {
        throw new Error('Authorization failed: Client lacks agent-assist:prompt:write scope.');
      }
      throw new Error(`Authentication error: ${error.message}`);
    }
  }
}

Implementation

Step 1: Payload Construction and Schema Validation

Agent assist prompt injections require strict adherence to UI rendering constraints. The Genesys Cloud interface limits concurrent prompts to prevent notification fatigue. You validate the payload against a Zod schema that enforces maximum concurrent limits, minimum display timing, and valid trigger condition matrices.

import { z } from 'zod';

export const PromptInjectionSchema = z.object({
  conversationId: z.string().uuid(),
  promptId: z.string().uuid(),
  triggerConditions: z.array(z.object({
    type: z.enum(['keyword', 'sentiment', 'duration', 'confidence']),
    value: z.union([z.string(), z.number()]),
    threshold: z.number().min(0).max(1).optional()
  })).max(5),
  displaySettings: z.object({
    delayMs: z.number().min(500).max(5000),
    maxConcurrentPrompts: z.number().int().min(1).max(3),
    autoDismissMs: z.number().min(10000).max(60000)
  }),
  webhookUrl: z.string().url().optional(),
  contextData: z.record(z.string(), z.unknown()).optional()
}).refine(
  (data) => data.displaySettings.maxConcurrentPrompts <= 3,
  { message: 'UI rendering constraint violated: maximum concurrent prompts cannot exceed 3.' }
).refine(
  (data) => data.displaySettings.delayMs >= 500,
  { message: 'Display timing directive violated: minimum delay must be 500ms to prevent UI flicker.' }
);

export type PromptInjectionPayload = z.infer<typeof PromptInjectionSchema>;

Step 2: Atomic POST Injection with Format Verification

The injection operation uses an atomic POST to /api/v2/agent-assist/prompt-injections. You pass the validated payload to the SDK method. The SDK serializes the object to JSON and attaches the bearer token. You handle 429 rate limits with exponential backoff and 409 conflicts when the agent session already holds the maximum concurrent prompts.

import { PureCloudPlatformClientV2 } from '@genesyscloud/purecloud-platform-client-v2';
import { PromptInjectionPayload } from './validation';

export class PromptDeliveryService {
  constructor(private client: PureCloudPlatformClientV2) {}

  async inject(payload: PromptInjectionPayload, maxRetries = 3): Promise<any> {
    let attempt = 0;
    
    while (attempt < maxRetries) {
      try {
        const response = await this.client.agentAssistApi.postAgentAssistPromptInjections({
          body: payload as any
        });
        return response.body;
      } catch (error: any) {
        const status = error.status || error.statusCode;
        
        if (status === 429) {
          const retryAfter = parseInt(error.headers?.['retry-after'] || '1', 10);
          const backoff = Math.min(retryAfter * 1000 * Math.pow(2, attempt), 10000);
          console.warn(`Rate limited (429). Backing off for ${backoff}ms.`);
          await new Promise(resolve => setTimeout(resolve, backoff));
          attempt++;
          continue;
        }
        
        if (status === 409) {
          throw new Error('409 Conflict: Maximum concurrent prompt limit reached for this agent session.');
        }
        
        if (status === 400) {
          throw new Error(`400 Bad Request: Format verification failed. ${error.message}`);
        }
        
        throw error;
      }
    }
    throw new Error('Max retries exceeded for prompt injection.');
  }
}

Step 3: Webhook Synchronization and Latency Tracking

After successful injection, you synchronize the event with an external knowledge retrieval system via webhook callbacks. You track injection latency from request initiation to HTTP 200 response. The webhook payload includes the injection ID, conversation context, and latency metrics for alignment with external systems.

import axios from 'axios';

export class WebhookSyncService {
  async notifyExternalSystem(webhookUrl: string, injectionId: string, latencyMs: number, context: any) {
    const syncPayload = {
      eventType: 'AGENT_ASSIST_PROMPT_INJECTED',
      injectionId,
      latencyMs,
      timestamp: new Date().toISOString(),
      context,
      alignmentStatus: 'SYNCED'
    };

    try {
      await axios.post(webhookUrl, syncPayload, {
        headers: { 'Content-Type': 'application/json' },
        timeout: 3000
      });
    } catch (error: any) {
      if (error.code === 'ECONNABORTED') {
        console.warn('Webhook sync timed out. Injection succeeded but external system notification delayed.');
      } else {
        console.warn(`Webhook sync failed: ${error.message}`);
      }
    }
  }
}

Step 4: Adoption Rate Tracking and Audit Logging

You track agent adoption rates by querying the Analytics Events API for prompt interaction metrics. The query uses pagination to retrieve batches of events. You generate audit logs for governance compliance by recording injection attempts, validation results, and delivery status.

export class AnalyticsAndAuditService {
  constructor(private client: PureCloudPlatformClientV2) {}

  async trackAdoptionRates(startDate: string, endDate: string, pageSize = 100): Promise<any[]> {
    const allEvents: any[] = [];
    let pageToken: string | undefined;

    do {
      const queryBody = {
        interval: `${startDate}/${endDate}`,
        metricNames: ['agent-assist.prompt.viewed', 'agent-assist.prompt.clicked', 'agent-assist.prompt.dismissed'],
        pageSize,
        pageToken,
        entityIds: ['all']
      };

      const response = await this.client.analyticsApi.postAnalyticsEventsQuery({ body: queryBody });
      if (response.body.events) {
        allEvents.push(...response.body.events);
      }
      pageToken = response.body.pageToken;
    } while (pageToken);

    return allEvents;
  }

  generateAuditLog(conversationId: string, promptId: string, status: string, latencyMs: number, error?: string) {
    return {
      auditId: crypto.randomUUID(),
      timestamp: new Date().toISOString(),
      conversationId,
      promptId,
      status,
      latencyMs,
      error,
      complianceTag: 'AGENT_ASSIST_INJECTION',
      retentionPolicy: '90_DAYS'
    };
  }
}

Complete Working Example

The following module combines authentication, validation, injection, webhook synchronization, analytics tracking, and audit logging into a single production-ready class. You run this script by providing environment variables for credentials.

import crypto from 'crypto';
import { PureCloudPlatformClientV2 } from '@genesyscloud/purecloud-platform-client-v2';
import { PromptInjectionSchema, PromptInjectionPayload } from './validation';
import { PromptDeliveryService } from './delivery';
import { WebhookSyncService } from './webhook';
import { AnalyticsAndAuditService } from './analytics';

export class AgentAssistPromptInjector {
  private client: PureCloudPlatformClientV2;
  private deliveryService: PromptDeliveryService;
  private webhookService: WebhookSyncService;
  private analyticsService: AnalyticsAndAuditService;

  constructor(
    private clientId: string,
    private clientSecret: string,
    private envUrl: string = 'https://api.mypurecloud.com'
  ) {
    this.client = new PureCloudPlatformClientV2();
    this.client.setEnvironment(this.envUrl);
    this.deliveryService = new PromptDeliveryService(this.client);
    this.webhookService = new WebhookSyncService();
    this.analyticsService = new AnalyticsAndAuditService(this.client);
  }

  async initialize(): Promise<void> {
    await this.client.loginApplication({
      body: {
        clientId: this.clientId,
        clientSecret: this.clientSecret,
        grantType: 'client_credentials',
        scope: 'agent-assist:prompt:write analytics:events:read'
      }
    });
  }

  async injectPrompt(payload: PromptInjectionPayload): Promise<{ success: boolean; auditLog: any; latencyMs: number }> {
    const validatedPayload = PromptInjectionSchema.parse(payload);
    const startTime = Date.now();
    const auditEntry = this.analyticsService.generateAuditLog(
      validatedPayload.conversationId,
      validatedPayload.promptId,
      'PENDING',
      0
    );

    try {
      const result = await this.deliveryService.inject(validatedPayload);
      const latencyMs = Date.now() - startTime;

      auditEntry.status = 'SUCCESS';
      auditEntry.latencyMs = latencyMs;
      auditEntry.responseId = result.id;

      if (validatedPayload.webhookUrl) {
        await this.webhookService.notifyExternalSystem(
          validatedPayload.webhookUrl,
          result.id,
          latencyMs,
          validatedPayload.contextData
        );
      }

      return { success: true, auditLog: auditEntry, latencyMs };
    } catch (error: any) {
      const latencyMs = Date.now() - startTime;
      auditEntry.status = 'FAILED';
      auditEntry.latencyMs = latencyMs;
      auditEntry.error = error.message || 'Unknown injection failure';
      return { success: false, auditLog: auditEntry, latencyMs };
    }
  }

  async getAdoptionMetrics(startDate: string, endDate: string) {
    return await this.analyticsService.trackAdoptionRates(startDate, endDate);
  }
}

// Execution wrapper
(async () => {
  const injector = new AgentAssistPromptInjector(
    process.env.GC_CLIENT_ID || '',
    process.env.GC_CLIENT_SECRET || '',
    process.env.GC_ENV_URL || 'https://api.mypurecloud.com'
  );

  await injector.initialize();

  const injectionResult = await injector.injectPrompt({
    conversationId: 'a1b2c3d4-e5f6-7890-abcd-ef1234567890',
    promptId: 'd4e5f6a7-b8c9-0123-4567-890abcdef123',
    triggerConditions: [
      { type: 'keyword', value: 'refund' },
      { type: 'sentiment', value: 'negative', threshold: 0.7 }
    ],
    displaySettings: {
      delayMs: 1000,
      maxConcurrentPrompts: 2,
      autoDismissMs: 30000
    },
    webhookUrl: 'https://internal-knowledge.example.com/api/v1/sync',
    contextData: { agentId: 'agent-01', queue: 'billing-support' }
  });

  console.log('Injection Result:', injectionResult);
})();

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: Invalid client credentials, expired client secret, or missing OAuth token in the request header.
• How to fix it: Verify the GC_CLIENT_ID and GC_CLIENT_SECRET environment variables. Ensure the client is registered in the Genesys Cloud Developer Portal. Re-run the loginApplication call to refresh the token cache.
• Code showing the fix: The GenesysAuthManager class catches 401 status codes and throws a descriptive error. Implement a token refresh retry loop if the secret rotates frequently.

Error: 403 Forbidden

• What causes it: The OAuth client lacks the agent-assist:prompt:write scope.
• How to fix it: Navigate to the Genesys Cloud Developer Portal, edit the OAuth client, and add agent-assist:prompt:write to the scope list. Regenerate the client secret if required.
• Code showing the fix: The authentication setup explicitly requests the required scope. If the server returns 403, the SDK throws a Forbidden error. Update the scope string and re-authenticate.

Error: 409 Conflict

• What causes it: The agent session already displays the maximum number of concurrent prompts defined in the displaySettings.maxConcurrentPrompts field or the platform default.
• How to fix it: Reduce the maxConcurrentPrompts value in the payload or wait for existing prompts to auto-dismiss. Implement queueing logic in your application to defer injection until the agent session has capacity.
• Code showing the fix: The PromptDeliveryService catches 409 status codes and throws a specific conflict error. Your calling code should catch this exception and schedule a retry after the autoDismissMs duration.

Error: 429 Too Many Requests

• What causes it: The injection endpoint exceeds the platform rate limit for the tenant or application.
• How to fix it: Implement exponential backoff with jitter. Read the Retry-After header from the response.
• Code showing the fix: The inject method in PromptDeliveryService includes a retry loop that parses Retry-After, applies exponential backoff, and continues until success or maxRetries is reached.

Error: Zod Validation Failure

• What causes it: The payload violates UI rendering constraints, such as delayMs below 500, maxConcurrentPrompts above 3, or invalid trigger condition types.
• How to fix it: Adjust the payload values to match the schema constraints. Review the PromptInjectionSchema refinements to understand platform limits.
• Code showing the fix: The injectPrompt method calls PromptInjectionSchema.parse(payload). If validation fails, Zod throws a structured error. Log the error paths to identify which field requires correction.

Official References

• Agent Assist Prompt Injections API
• Genesys Cloud Node SDK Documentation
• OAuth 2.0 Authentication Guide
• Analytics Events Query API