Implementing Real-Time Next-Best-Response Suggestions Using LLM Inference During Live Chats

How-To-Articles
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Implementing Real-Time Next-Best-Response Suggestions Using LLM Inference During Live Chats

What This Guide Covers

This guide details the architectural implementation of a real-time Next-Best-Response (NBR) engine that leverages Large Language Model (LLM) inference to suggest agent replies during active Genesys Cloud CX chat sessions. You will build a serverless integration using AWS Lambda and the Genesys Cloud Message API to intercept chat messages, enrich context, invoke an LLM, and inject suggestions into the agent desktop without adding latency to the customer experience. The end result is an agent workflow where relevant, context-aware response options appear dynamically as the customer types, reducing Average Handle Time (AHT) and improving first-contact resolution.

Prerequisites, Roles & Licensing

Licensing & Subscriptions

  • Genesys Cloud CX: CX 1, CX 2, or CX 3 license. The core Chat functionality is included in all tiers.
  • Genesys Cloud WEM (Workforce Engagement Management): Optional but recommended for analyzing the impact of NBR usage on agent performance metrics.
  • Genesys Cloud Developer Portal: Access to create API keys and manage integrations.
  • LLM Provider Account: An active subscription to an LLM provider (e.g., OpenAI, Anthropic, or a self-hosted model via AWS Bedrock/SageMaker). This guide uses OpenAI gpt-4o-mini for cost-effective, low-latency inference.

Permissions & Roles

  • Genesys Cloud Admin: Integration > Integration > Edit, Message > Message > Read, Message > Message > Edit.
  • Developer/Architect: Message > Message > Read, Message > Message > Edit, Integration > Integration > Edit.
  • Agent: No special permissions required beyond standard Chat access. The suggestions appear within the standard Chat UI.

Technical Dependencies

  • AWS Account: For hosting the inference logic (Lambda, API Gateway).
  • OpenAI API Key: Stored securely in AWS Secrets Manager.
  • Node.js 18+: For the Lambda function runtime.
  • Genesys Cloud Message API: Specifically the POST /v2/messages/events endpoint for real-time message ingestion.

The Implementation Deep-Dive

1. Architecting the Low-Latency Inference Pipeline

The primary constraint in real-time NBR is latency. Customers expect agents to respond within seconds. If the LLM inference takes 5-10 seconds, the agent will ignore the suggestion. The architecture must decouple the customer-to-agent message flow from the agent-to-LLM inference flow.

The Flow:

  1. Customer sends a message in Genesys Cloud Chat.
  2. Genesys Cloud triggers a webhook to an AWS API Gateway.
  3. API Gateway invokes an AWS Lambda function.
  4. Lambda retrieves the conversation history from Genesys Cloud.
  5. Lambda constructs a prompt with system instructions and recent context.
  6. Lambda invokes the LLM API.
  7. LLM returns 2-3 suggested responses.
  8. Lambda posts these suggestions back to Genesys Cloud as agent-suggestion message types or via the Agent Desktop API (depending on UI customization capabilities). *Note: Genesys Cloud does not have a native “Suggestion” UI element exposed via standard public API for custom injection without using the Genesys Cloud UI Customization framework or third-party add-ons. For this guide, we will use the POST /v2/messages/events to send a system message to the agent channel that a custom UI overlay or script parses, or more robustly, we will use the **Genesys Cloud Message API to send a “Note” or “Agent-Only” message that is styled via CSS/JS injection if you have access to the UI customization layer. However, the most standard, out-of-the-box compatible method without custom UI code is to use the Genesys Cloud Knowledge API to push the suggestions as “Related Articles” or use a Custom Attribute on the interaction that a browser extension reads.

Correction for Production Viability: Injecting UI elements directly into the Genesys Cloud Agent Desktop via public API is not supported. The standard enterprise pattern is to use Genesys Cloud Knowledge or Custom Attributes. However, for a true “Next-Best-Response” feel, we will implement a Webhook-to-Message pattern where the Lambda function sends a formatted message to the Agent only, using the chat channel’s agent side. This message will be parsed by a simple browser extension or, if you are using the Genesys Cloud UI Customization feature (available in newer versions), rendered as buttons. For this guide, we will assume you are deploying a Chrome Extension or Browser-based Overlay that listens to the Genesys Cloud Message WebSocket or polls the Message API for specific tagged messages, as this is the only way to render interactive “Suggestion Buttons” without full native app development.

Alternative Native Approach: If you cannot deploy a browser extension, you can use Genesys Cloud Knowledge to push the LLM output as a “Knowledge Article” attachment to the chat, which appears in the agent’s resource pane. This is less “real-time button” but fully native.

Decision: We will build the API-First Inference Engine. The UI consumption method will be abstracted, but the core value is the generation and delivery of the suggestion payload. We will deliver the suggestion via a Custom Message Event to the Agent, which can be consumed by any frontend listener.

Step 1.1: Configure the Genesys Cloud Webhook

You need to capture chat messages in real-time. Do not use polling. Use the Message API webhook.

  1. Navigate to Admin > Integrations > Webhooks.
  2. Click Add Webhook.
  3. Name: NBR_LLM_Ingestion.
  4. Endpoint: Your AWS API Gateway URL (e.g., https://api.example.com/nbr-ingest).
  5. Method: POST.
  6. Events: Select message:created and message:updated.
  7. Filters:
    • Channel Type: chat
    • Message Type: text
    • Sender Role: customer (We only infer on customer messages to suggest agent replies).

The Trap: Configuring the webhook to trigger on all message types, including agent messages. This creates a feedback loop where the agent’s message triggers an inference, which sends a suggestion, which might be interpreted as a new message, causing infinite recursion. Always filter for customer sender role.

Step 1.2: Build the AWS Lambda Function

The Lambda function performs three critical tasks: Context Retrieval, Prompt Engineering, and Response Delivery.

Code Structure (Node.js 18):

const axios = require('axios');
const { Client } = require('@genesyscloud/api-client'); // Genesys Cloud SDK

// Initialize Genesys Client
const genesysClient = new Client({
  clientId: process.env.GENESYS_CLIENT_ID,
  clientSecret: process.env.GENESYS_CLIENT_SECRET,
  basePath: process.env.GENESYS_BASE_PATH // e.g., 'https://api.us.genesys.cloud'
});

// OpenAI Client
const openai = require('openai');
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;

exports.handler = async (event) => {
  try {
    // 1. Parse Genesys Webhook Payload
    const body = typeof event.body === 'string' ? JSON.parse(event.body) : event.body;
    
    // Validate it is a customer message
    if (body.message.sender.role !== 'customer') {
      return { statusCode: 200, body: 'Ignored: Not a customer message' };
    }

    const conversationId = body.message.conversationId;
    const messageId = body.message.id;

    // 2. Retrieve Conversation History (Last 5-10 messages)
    // We limit history to reduce token cost and latency
    const historyResponse = await genesysClient.messageApi.getMessageEvents(conversationId, {
      pageSize: 10,
      sortOrder: 'desc'
    });

    const recentMessages = historyResponse.entities.reverse();
    const contextText = recentMessages.map(m => {
      const role = m.sender.role === 'customer' ? 'Customer' : 'Agent';
      return `${role}: ${m.text}`;
    }).join('\n');

    // 3. Construct Prompt
    const systemPrompt = `You are a helpful support agent for [Company Name]. 
    Based on the conversation history, suggest 2 concise, empathetic, and accurate responses for the agent to send. 
    Format the output as a JSON array of strings. 
    Do not include any other text.`;

    const userPrompt = `Conversation History:\n${contextText}\n\nCustomer's Last Message: ${body.message.text}`;

    // 4. Invoke LLM
    const response = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      messages: [
        { role: "system", content: systemPrompt },
        { role: "user", content: userPrompt }
      ],
      temperature: 0.7,
      max_tokens: 150
    });

    const suggestions = JSON.parse(response.choices[0].message.content);

    // 5. Deliver Suggestions to Agent
    // We send a custom event to the agent's side of the chat
    // Note: This requires a frontend listener to display it nicely.
    // Alternatively, we could update a Custom Attribute on the Interaction.
    
    const suggestionPayload = {
      type: 'text',
      text: JSON.stringify(suggestions), // Store as JSON string in text field for simplicity
      sender: {
        role: 'system' // Or 'agent' if you want it to look like an agent note
      },
      metadata: {
        nbr_suggestions: true // Flag for frontend to identify
      }
    };

    // Post to the Agent's channel in the conversation
    // We need the agent's participant ID. Usually, we target the conversation generally, 
    // but Genesys Message API allows targeting specific participants.
    // For simplicity, we post to the conversation. The frontend will filter by sender.role === 'system'
    
    await genesysClient.messageApi.postMessageEvent(conversationId, suggestionPayload);

    return {
      statusCode: 200,
      body: JSON.stringify({ status: 'success', suggestions })
    };

  } catch (error) {
    console.error('Error processing NBR:', error);
    return {
      statusCode: 500,
      body: JSON.stringify({ error: error.message })
    };
  }
};

Architectural Reasoning:

  • Context Window Limiting: We only fetch the last 10 messages. Including the entire chat history increases token costs and inference time. For most support scenarios, the last 3-5 exchanges contain the relevant intent.
  • JSON Output Enforcement: The system prompt strictly enforces JSON output. Parsing free-form text is fragile. If the LLM fails to return JSON, the agent receives nothing rather than broken UI.
  • System Sender Role: By sending the message as system, we distinguish it from human text. A frontend overlay can hide these messages from the customer view entirely.

Step 1.3: Handling the UI Consumption (The Frontend Gap)

Genesys Cloud does not natively render “Suggestion Buttons” from API messages. You have two options:

Option A: Browser Extension (Recommended for Custom UI)
Develop a Chrome Extension that injects into the Genesys Cloud Agent Desktop. The extension listens to the Genesys Cloud WebSocket (if accessible via CORS) or polls the Message API for messages with metadata.nbr_suggestions: true. When detected, it parses the JSON and renders clickable buttons in the agent’s compose box.

Option B: Knowledge Article Injection (Native but Clunky)
Modify the Lambda to create a Knowledge Article with the suggestions as the body, then attach that article to the interaction. The agent sees it in the “Knowledge” pane. This is slower and less intuitive but requires no custom code.

The Trap: Sending the suggestions as a standard text message to the agent participant. If you do this, the agent sees the raw JSON string in the chat bubble. It looks like an error. You must use a custom UI layer or a structured data field (like Custom Attributes) that a frontend script reads.

2. Prompt Engineering for Support Consistency

The quality of the suggestion depends entirely on the prompt. A generic prompt yields generic, unhelpful answers.

Step 2.1: Injecting Company Knowledge

You should not rely solely on the LLM’s training data. Inject specific product details or policy constraints.

Enhanced System Prompt:

You are a Tier 1 Support Agent for [Company]. 
Guidelines:
1. Tone: Empathetic, professional, concise.
2. Policy: Never offer refunds over $50. Direct to Tier 2 for billing disputes.
3. Product Info: Our flagship product is "CloudSync", which supports Windows 10+ and macOS 12+.

Current Context:
{context}

Customer's Last Message:
{last_message}

Output only a JSON array of 2 suggested responses.

The Trap: Hallucination. If you do not constrain the LLM with specific product info, it may invent features. Use RAG (Retrieval-Augmented Generation) if you have a large knowledge base. For this guide, we keep it simple with static prompt injection. For RAG, you would query a vector database (e.g., Pinecone) in the Lambda before calling the LLM.

Step 2.2: Latency Optimization

LLMs are slow. gpt-4o-mini is fast (~1-2 seconds), but gpt-4 can take 5+ seconds.

  • Streaming: Do not stream the LLM response to the agent. Wait for the complete response. Partial suggestions are confusing.
  • Caching: If the customer sends the same message twice (e.g., “I am still waiting”), cache the previous suggestion. Use AWS ElastiCache (Redis) in the Lambda. Key: conversationId:customerId:hash(lastMessage).

3. Security & Compliance

Step 3.1: PII Redaction

Sending PII (Personally Identifiable Information) to third-party LLMs is a violation of GDPR, HIPAA, and PCI-DSS in many contexts. OpenAI’s data usage policy may allow it, but your internal compliance likely does not.

Implementation:
In the Lambda, before sending the context to OpenAI, run a PII redaction step. Use AWS Comprehend Medical or a simple regex-based library to mask names, emails, and phone numbers.

// Example PII Masking
function maskPII(text) {
  return text
    .replace(/([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})/g, '[EMAIL_REDACTED]')
    .replace(/(\b\d{3}[-.]?\d{3}[-.]?\d{4}\b)/g, '[PHONE_REDACTED]')
    .replace(/(\b[A-Z][a-z]+ [A-Z][a-z]+\b)/g, '[NAME_REDACTED]'); // Basic Name Masking
}

The Trap: Assuming the LLM provider is HIPAA-compliant automatically. Even if they are, you must sign a BAA (Business Associate Agreement) and ensure data is not used for model training. Always redact PII on the client side (Lambda) before the API call.

Step 3.2: Secret Management

Never hardcode API keys. Use AWS Secrets Manager. The Lambda function should have an IAM role that allows secretsmanager:GetSecretValue.

Validation, Edge Cases & Troubleshooting

Edge Case 1: High Latency During Peak Hours

The Failure Condition: The Lambda function times out (30 seconds default), or the LLM response takes >5 seconds. The agent receives no suggestion, or the suggestion appears after the agent has already typed a reply.

The Root Cause:

  1. LLM provider API throttling.
  2. Genesys Cloud Message API rate limiting during high-volume chats.
  3. Inefficient context retrieval (fetching too many messages).

The Solution:

  • Set a Timeout: In the Lambda, set a Promise.race with a 3-second timeout for the LLM call. If it exceeds 3 seconds, abort and return nothing. It is better to have no suggestion than a late one.
  • Reduce Context: Lower the pageSize from 10 to 5.
  • Use a Faster Model: Switch from gpt-4o to gpt-4o-mini or claude-3-haiku.

Edge Case 2: Malformed JSON from LLM

The Failure Condition: The LLM returns text instead of JSON, or the JSON is invalid. The JSON.parse() in the Lambda throws an error, and the suggestion is not delivered.

The Root Cause: LLMs are probabilistic. Even with strict prompts, they occasionally fail.

The Solution:

  • Retry Logic: If JSON.parse fails, retry the LLM call once with a lower temperature (e.g., 0.1).
  • Fallback: If the second attempt fails, send a generic suggestion: ["How can I help you further?", "Is there anything else you need?"].
  • Robust Parsing: Use a library like jsonrepair to attempt to fix malformed JSON before parsing.

Edge Case 3: Feedback Loop on Agent Messages

The Failure Condition: The webhook triggers on agent messages, causing the LLM to generate suggestions for the agent’s own message, which are then sent back as system messages, potentially triggering another event.

The Root Cause: The webhook filter is not strictly enforcing sender.role === 'customer'.

The Solution:

  • Double-Check Filter: In the Lambda, add a strict guard clause at the very top:
    if (body.message.sender.role !== 'customer') {
  return { statusCode: 200, body: 'Ignored' };
}
  • Webhook Config: Ensure the Genesys Cloud Webhook filter is set to Customer only.

Edge Case 4: Cross-Channel Context Loss

The Failure Condition: The customer switches from Chat to Voice or vice versa. The NBR engine does not have context from the previous channel.

The Root Cause: Genesys Cloud treats Chat and Voice as separate conversation IDs.

The Solution:

  • Unified Conversation ID: Use the Genesys Cloud Interaction API to find the parent interaction ID. Fetch messages from all channels associated with that interaction. This is complex and expensive. For most implementations, accept that NBR is channel-specific.

Official References