Build an LLM-Driven Agent Assist Microservice in Go for NICE CXone

What You Will Build

This microservice captures live agent conversation context from the CXone Assist API, generates vector embeddings using a local transformer model, searches a knowledge base via approximate nearest neighbor retrieval, and pushes structured suggestions to the agent desktop over a persistent WebSocket connection. It uses the NICE CXone Assist REST API and the CXone Agent Desktop WebSocket endpoint. The implementation is written entirely in Go.

Prerequisites

• OAuth 2.0 Client Credentials grant configured in CXone with assist:read and agent-desktop:write scopes
• CXone Assist API v2 (https://{domain}.niceincontact.com/api/assist/)
• Go 1.21+ runtime
• Dependencies: github.com/qdrant/go-client/qdrant, github.com/gorilla/websocket, golang.org/x/oauth2/clientcredentials, github.com/google/uuid
• Local embedding inference server running on http://localhost:11434 (Ollama, vLLM, or compatible transformer endpoint)
• Qdrant vector database running with a preconfigured collection matching the embedding dimension

Authentication Setup

CXone uses standard OAuth 2.0 Client Credentials flow. Tokens expire after 3600 seconds. You must implement token caching and automatic refresh to avoid 401 errors during long-running WebSocket sessions. The following code establishes a thread-safe token manager that fetches credentials from environment variables and handles expiration.

package main

import (
	"context"
	"fmt"
	"net/http"
	"os"
	"sync"
	"time"

	"golang.org/x/oauth2/clientcredentials"
)

type TokenManager struct {
	token   *oauth2.Token
	client  *http.Client
	config  *clientcredentials.Config
	mu      sync.RWMutex
}

func NewTokenManager(domain, clientID, clientSecret string) *TokenManager {
	cfg := &clientcredentials.Config{
		ClientID:     clientID,
		ClientSecret: clientSecret,
		TokenURL:     fmt.Sprintf("https://%s/oauth/token", domain),
		Scopes:       []string{"assist:read", "agent-desktop:write"},
	}

	return &TokenManager{
		config: cfg,
		client: cfg.Client(context.Background()),
	}
}

func (tm *TokenManager) GetClient() *http.Client {
	tm.mu.RLock()
	if tm.token != nil && time.Until(tm.token.Expiry) > time.Minute {
		tm.mu.RUnlock()
		return tm.client
	}
	tm.mu.RUnlock()

	tm.mu.Lock()
	defer tm.mu.Unlock()

	if tm.token != nil && time.Until(tm.token.Expiry) > time.Minute {
		return tm.client
	}

	token, err := tm.config.Token(context.Background())
	if err != nil {
		panic(fmt.Sprintf("failed to fetch OAuth token: %v", err))
	}
	tm.token = token
	return tm.client
}

The GetClient method checks expiration under a read lock, upgrades to a write lock only when refresh is required, and returns the authenticated http.Client. This prevents concurrent token refresh attempts during high-throughput Assist polling.

Implementation

Step 1: Capture Agent Context via the Assist API

The CXone Assist API exposes session state at GET /api/assist/sessions/{sessionId}. This endpoint returns the active transcript, agent metadata, and customer details. You must handle 403 Forbidden (missing scope) and 429 Too Many Requests (rate limiting) explicitly. The Assist API enforces per-client rate limits, so you must implement exponential backoff.

package main

import (
	"encoding/json"
	"fmt"
	"net/http"
	"time"
)

type AssistSession struct {
	SessionID string          `json:"sessionId"`
	Context   AssistContext   `json:"context"`
	Transcript []TranscriptItem `json:"transcript"`
}

type AssistContext struct {
	AgentName   string `json:"agentName"`
	CustomerID  string `json:"customerId"`
	ChannelType string `json:"channelType"`
}

type TranscriptItem struct {
	Speaker string `json:"speaker"`
	Text    string `json:"text"`
	Timestamp string `json:"timestamp"`
}

func FetchAssistContext(client *http.Client, domain, sessionID string) (*AssistSession, error) {
	url := fmt.Sprintf("https://%s/api/assist/sessions/%s", domain, sessionID)
	
	var lastErr error
	for attempt := 0; attempt < 3; attempt++ {
		req, err := http.NewRequestWithContext(context.Background(), http.MethodGet, url, nil)
		if err != nil {
			return nil, fmt.Errorf("failed to create request: %w", err)
		}
		req.Header.Set("Accept", "application/json")
		req.Header.Set("Content-Type", "application/json")

		resp, err := client.Do(req)
		if err != nil {
			lastErr = err
			continue
		}
		defer resp.Body.Close()

		if resp.StatusCode == http.StatusTooManyRequests {
			backoff := time.Duration(1<<attempt) * time.Second
			time.Sleep(backoff)
			continue
		}
		if resp.StatusCode != http.StatusOK {
			return nil, fmt.Errorf("assist API returned %d: %w", resp.StatusCode, lastErr)
		}

		var session AssistSession
		if err := json.NewDecoder(resp.Body).Decode(&session); err != nil {
			return nil, fmt.Errorf("failed to decode assist response: %w", err)
		}
		return &session, nil
	}
	return nil, fmt.Errorf("max retries exceeded: %w", lastErr)
}

The response body follows this structure:

{
  "sessionId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "context": {
    "agentName": "Maria Gonzalez",
    "customerId": "cust_998877",
    "channelType": "voice"
  },
  "transcript": [
    {"speaker": "customer", "text": "I need to update my billing address.", "timestamp": "2024-05-12T14:30:00Z"},
    {"speaker": "agent", "text": "I can help with that. Please verify your account number.", "timestamp": "2024-05-12T14:30:05Z"}
  ]
}

Step 2: Generate Embeddings and Perform Approximate Nearest Neighbor Search

You must convert the conversation transcript into a dense vector. This code calls a local transformer model endpoint (compatible with Ollama or vLLM) to generate embeddings. You then query a Qdrant vector database for the most similar knowledge chunks. The dimensionality must match exactly between your embedding model and Qdrant collection schema.

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"

	"github.com/qdrant/go-client/qdrant"
)

type EmbeddingRequest struct {
	Model  string `json:"model"`
	Input  string `json:"input"`
}

type EmbeddingResponse struct {
	Embedding []float32 `json:"embedding"`
}

func GenerateEmbedding(client *http.Client, text string) ([]float32, error) {
	payload := EmbeddingRequest{Model: "nomic-embed-text", Input: text}
	jsonBody, err := json.Marshal(payload)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal embedding request: %w", err)
	}

	resp, err := client.Post("http://localhost:11434/api/embeddings", "application/json", bytes.NewBuffer(jsonBody))
	if err != nil {
		return nil, fmt.Errorf("embedding server unreachable: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return nil, fmt.Errorf("embedding server returned %d", resp.StatusCode)
	}

	var result EmbeddingResponse
	if err := json.NewDecoder(resp.Body).Decode(&result); err != nil {
		return nil, fmt.Errorf("failed to decode embedding response: %w", err)
	}
	return result.Embedding, nil
}

func SearchKnowledgeBase(qdrantClient *qdrant.Qdrant, collectionName string, queryVector []float32) ([]qdrant.ScoredPoint, error) {
	records, err := qdrantClient.Search(context.Background(), qdrant.SearchRequest{
		CollectionName: collectionName,
		Vector:         queryVector,
		Limit:          3,
		WithPayload:    qdrant.WithPayload{Enable: true},
	})
	if err != nil {
		return nil, fmt.Errorf("qdrant search failed: %w", err)
	}
	return records, nil
}

The Qdrant Search method performs cosine similarity ranking by default. You must ensure your knowledge base chunks are preprocessed and stored with metadata fields like article_id, title, and content. The WithPayload flag guarantees you receive the full document text for downstream formatting.

Step 3: Format the Payload and Inject Suggestions via WebSocket

CXone Agent Desktop accepts real-time suggestions through a secure WebSocket endpoint at wss://{domain}.niceincontact.com/socket. You must authenticate the connection using the bearer token in the Authorization header. The payload must conform to the Assist injection schema, which expects an action field, a sessionId, and a content object containing the suggestion markup.

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"net/http"

	"github.com/gorilla/websocket"
)

type SuggestionPayload struct {
	Action    string      `json:"action"`
	SessionID string      `json:"sessionId"`
	Content   SuggestionContent `json:"content"`
}

type SuggestionContent struct {
	Title string `json:"title"`
	Body  string `json:"body"`
	Type  string `json:"type"`
}

func InjectSuggestion(domain string, token string, payload SuggestionPayload) error {
	headers := http.Header{}
	headers.Set("Authorization", fmt.Sprintf("Bearer %s", token))
	headers.Set("Content-Type", "application/json")

	dialer := websocket.Dialer{}
	conn, _, err := dialer.Dial(fmt.Sprintf("wss://%s/socket", domain), headers)
	if err != nil {
		return fmt.Errorf("websocket connection failed: %w", err)
	}
	defer conn.Close()

	jsonData, err := json.Marshal(payload)
	if err != nil {
		return fmt.Errorf("failed to marshal suggestion: %w", err)
	}

	if err := conn.WriteMessage(websocket.TextMessage, jsonData); err != nil {
		return fmt.Errorf("failed to send suggestion: %w", err)
	}

	// Read acknowledgment or timeout
	_, _, err = conn.ReadMessage()
	return err
}

The WebSocket server expects a single JSON object per message. You must serialize the payload exactly as shown. The type field controls how CXone renders the suggestion card (assist.suggestion, assist.knowledge, or assist.form). You should close the connection immediately after sending to conserve server resources, as CXone does not maintain long-lived suggestion channels per microservice.

Complete Working Example

The following program ties all components together. It polls a known session, generates embeddings, searches Qdrant, formats the top result, and injects it into the agent desktop. Replace the configuration values with your environment credentials.

package main

import (
	"context"
	"fmt"
	"os"
	"strings"

	"github.com/qdrant/go-client/qdrant"
	"golang.org/x/oauth2/clientcredentials"
)

type Config struct {
	CXoneDomain     string
	ClientID        string
	ClientSecret    string
	SessionID       string
	QdrantURL       string
	QdrantAPIKey    string
	CollectionName  string
}

func main() {
	cfg := Config{
		CXoneDomain:    os.Getenv("CXONE_DOMAIN"),
		ClientID:       os.Getenv("CXONE_CLIENT_ID"),
		ClientSecret:   os.Getenv("CXONE_CLIENT_SECRET"),
		SessionID:      os.Getenv("TARGET_SESSION_ID"),
		QdrantURL:      os.Getenv("QDRANT_URL"),
		QdrantAPIKey:   os.Getenv("QDRANT_API_KEY"),
		CollectionName: "cxone_knowledge_base",
	}

	if cfg.CXoneDomain == "" || cfg.ClientID == "" || cfg.ClientSecret == "" {
		fmt.Println("Missing required environment variables")
		os.Exit(1)
	}

	tokenMgr := NewTokenManager(cfg.CXoneDomain, cfg.ClientID, cfg.ClientSecret)
	httpClient := tokenMgr.GetClient()

	// Step 1: Fetch context
	session, err := FetchAssistContext(httpClient, cfg.CXoneDomain, cfg.SessionID)
	if err != nil {
		fmt.Printf("Failed to fetch assist context: %v\n", err)
		os.Exit(1)
	}

	// Build conversation string for embedding
	var transcriptBuilder strings.Builder
	for _, item := range session.Transcript {
		transcriptBuilder.WriteString(fmt.Sprintf("%s: %s\n", item.Speaker, item.Text))
	}
	conversationText := transcriptBuilder.String()

	// Step 2: Generate embedding and search
	embedding, err := GenerateEmbedding(httpClient, conversationText)
	if err != nil {
		fmt.Printf("Failed to generate embedding: %v\n", err)
		os.Exit(1)
	}

	qdrantClient, err := qdrant.New(qdrant.Config{
		URL:  cfg.QdrantURL,
		Key:  cfg.QdrantAPIKey,
	})
	if err != nil {
		fmt.Printf("Failed to initialize Qdrant client: %v\n", err)
		os.Exit(1)
	}

	results, err := SearchKnowledgeBase(qdrantClient, cfg.CollectionName, embedding)
	if err != nil || len(results) == 0 {
		fmt.Println("No relevant knowledge chunks found")
		os.Exit(0)
	}

	// Step 3: Format and inject
	topResult := results[0]
	payload := SuggestionPayload{
		Action:    "assist.suggestion",
		SessionID: session.SessionID,
		Content: SuggestionContent{
			Title: fmt.Sprintf("Knowledge: %s", topResult.Payload["title"]),
			Body:  fmt.Sprintf("Relevance: %.2f\n%s", topResult.Score, topResult.Payload["content"]),
			Type:  "assist.knowledge",
		},
	}

	token, err := tokenMgr.GetClient().Transport.(*oauth2.Transport).Source.Token()
	if err != nil {
		fmt.Printf("Failed to retrieve token for WebSocket: %v\n", err)
		os.Exit(1)
	}

	if err := InjectSuggestion(cfg.CXoneDomain, token.AccessToken, payload); err != nil {
		fmt.Printf("Failed to inject suggestion: %v\n", err)
		os.Exit(1)
	}

	fmt.Println("Suggestion injected successfully")
}

Run the program with go run main.go. The microservice will authenticate, retrieve the live session, compute the vector representation, locate the top knowledge match, and push the card into the active agent desktop.

Common Errors & Debugging

Error: 401 Unauthorized on WebSocket Dial

• Cause: The bearer token expired between the REST call and the WebSocket connection, or the OAuth client lacks agent-desktop:write scope.
• Fix: Always fetch a fresh token immediately before dialing the WebSocket. Verify scope assignment in the CXone Admin Console under Identity Management.
• Code: Replace cached token retrieval with a direct call to tokenMgr.GetClient().Transport.(*oauth2.Transport).Source.Token() right before dialer.Dial.

Error: 429 Too Many Requests on Assist API

• Cause: CXone enforces strict rate limits per OAuth client on session polling endpoints.
• Fix: Implement exponential backoff with jitter. The provided FetchAssistContext includes a retry loop, but you must adjust the base delay and maximum attempts based on your tenant tier.
• Code: Increase backoff multiplier or add time.Sleep(time.Millisecond * time.Duration(rand.Intn(1000))) for jitter.

Error: Qdrant Dimension Mismatch

• Cause: The embedding model outputs a vector of length N, but the Qdrant collection was created with dimension M.
• Fix: Verify your local transformer configuration. Ollama models like nomic-embed-text output 768 dimensions. Recreate the Qdrant collection with vector_size: 768 if misaligned.
• Code: Query Qdrant collection info via qdrantClient.CollectionInfo(ctx, collectionName) to confirm VectorsConfig.Dimensions.

Error: WebSocket 1006 Abnormal Closure

• Cause: The CXone socket server terminates connections that remain idle or exceed payload size limits.
• Fix: Serialize payloads under 16KB. Close the connection immediately after the ReadMessage acknowledgment. Do not attempt to reuse the websocket.Conn for multiple suggestions.
• Code: Ensure defer conn.Close() executes after the write/read cycle completes.

Official References

• NICE CXone Assist API Documentation
• NICE CXone WebSocket Integration Guide
• Qdrant Go Client Reference
• Ollama Embeddings API Specification