Classifying Genesys Cloud Agent Assist Intents via Agent Assist API with Go

What You Will Build

• A Go module that classifies conversation text against a Genesys Cloud Agent Assist model, validates predictions against confidence thresholds, handles multi-label scoring with fallback logic, and emits audit logs and latency metrics for external ML monitoring.
• This tutorial uses the Genesys Cloud Agent Assist REST API (/api/v2/ai/agentassist/models/{modelId}/classifications).
• The implementation covers Go 1.21+ with standard library HTTP, JSON serialization, structured logging, and atomic request handling.

Prerequisites

• OAuth confidential client credentials registered in Genesys Cloud with the ai:agentassist:write scope.
• Go 1.21 or later.
• An active Agent Assist model ID in your Genesys Cloud org.
• No external dependencies. The code relies on net/http, encoding/json, log/slog, sync, time, and context.

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server API access. The token must be cached and refreshed before expiration to prevent unnecessary authentication round trips. The following implementation stores the token in a thread-safe map and validates expiration before reuse.

package main

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

const (
	authEndpoint = "https://login.mypurecloud.com/oauth/token"
	apiBaseURL   = "https://api.mypurecloud.com"
)

type TokenResponse struct {
	AccessToken string `json:"access_token"`
	ExpiresIn   int64  `json:"expires_in"`
	TokenType   string `json:"token_type"`
}

type OAuthClient struct {
	clientID     string
	clientSecret string
	token        *TokenResponse
	expiresAt    time.Time
	mu           sync.RWMutex
	httpClient   *http.Client
}

func NewOAuthClient(clientID, clientSecret string) *OAuthClient {
	return &OAuthClient{
		clientID:     clientID,
		clientSecret: clientSecret,
		httpClient:   &http.Client{Timeout: 10 * time.Second},
	}
}

func (o *OAuthClient) GetToken(ctx context.Context) (string, error) {
	o.mu.RLock()
	if o.token != nil && time.Now().Before(o.expiresAt.Add(-30*time.Second)) {
		token := o.token.AccessToken
		o.mu.RUnlock()
		return token, nil
	}
	o.mu.RUnlock()

	return o.fetchNewToken(ctx)
}

func (o *OAuthClient) fetchNewToken(ctx context.Context) (string, error) {
	o.mu.Lock()
	defer o.mu.Unlock()

	// Double-check after acquiring write lock
	if o.token != nil && time.Now().Before(o.expiresAt.Add(-30*time.Second)) {
		return o.token.AccessToken, nil
	}

	form := fmt.Sprintf("grant_type=client_credentials&client_id=%s&client_secret=%s", o.clientID, o.clientSecret)
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, authEndpoint, nil)
	if err != nil {
		return "", fmt.Errorf("failed to create auth request: %w", err)
	}
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")
	req.SetBasicAuth(o.clientID, o.clientSecret)

	resp, err := o.httpClient.Do(req)
	if err != nil {
		return "", fmt.Errorf("auth request failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("auth failed with status %d", resp.StatusCode)
	}

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

	o.token = &tokenResp
	o.expiresAt = time.Now().Add(time.Duration(tokenResp.ExpiresIn) * time.Second)
	return tokenResp.AccessToken, nil
}

The GetToken method implements a read-write lock pattern to prevent thundering herd token refreshes. The thirty-second buffer before expiration ensures downstream API calls never receive an expired bearer token.

Implementation

Step 1: Payload Construction and Schema Validation

The Agent Assist classification endpoint requires a structured JSON payload. Genesys Cloud expects text, intentIds, and confidenceThreshold. The prompt terminology maps as follows: intent references become intentIds, confidence matrix maps to the threshold and scoring configuration, and predict directive controls server-side feature extraction flags. This section defines the request schema and validates it against model constraints before transmission.

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

type ClassificationRequest struct {
	Text               string   `json:"text"`
	IntentIDs          []string `json:"intentIds,omitempty"`
	ConfidenceThreshold float64 `json:"confidenceThreshold"`
	EnableMultiLabel   bool    `json:"enableMultiLabel,omitempty"`
}

type ClassificationResponse struct {
	Classifications []ClassificationResult `json:"classifications"`
	RequestID       string                 `json:"requestId"`
}

type ClassificationResult struct {
	IntentID   string  `json:"intentId"`
	IntentName string  `json:"intentName"`
	Confidence float64 `json:"confidence"`
}

func (req *ClassificationRequest) Validate() error {
	if req.Text == "" {
		return fmt.Errorf("text field cannot be empty")
	}
	if req.ConfidenceThreshold < 0.0 || req.ConfidenceThreshold > 1.0 {
		return fmt.Errorf("confidenceThreshold must be between 0.0 and 1.0")
	}
	if len(req.IntentIDs) > 0 && len(req.IntentIDs) > 50 {
		return fmt.Errorf("intentIds array exceeds maximum limit of 50")
	}
	return nil
}

The Validate method enforces schema constraints before the HTTP call. Genesys Cloud rejects payloads with malformed thresholds or oversized intent arrays. Validating locally prevents unnecessary network overhead and provides immediate failure feedback.

Step 2: Atomic Classification with Multi-Label Scoring and Fallback

Classification requests must be atomic. The API returns a list of scored intents when multi-label scoring is enabled. This implementation handles the HTTP cycle, parses the response, applies the confidence threshold, and triggers a safe fallback when scores fall below the minimum accuracy requirement.

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"io"
	"log/slog"
	"net/http"
	"time"
)

type IntentClassifier struct {
	oauth      *OAuthClient
	modelID    string
	baseURL    string
	httpClient *http.Client
	logger     *slog.Logger
}

func NewIntentClassifier(oauth *OAuthClient, modelID string) *IntentClassifier {
	return &IntentClassifier{
		oauth: oauth,
		modelID: modelID,
		baseURL: fmt.Sprintf("%s/api/v2/ai/agentassist/models/%s/classifications", apiBaseURL, modelID),
		httpClient: &http.Client{
			Timeout: 15 * time.Second,
			Transport: &http.Transport{
				MaxIdleConns:        10,
				MaxIdleConnsPerHost: 10,
			},
		},
		logger: slog.Default(),
	}
}

func (c *IntentClassifier) Classify(ctx context.Context, req ClassificationRequest) (*ClassificationResponse, error) {
	if err := req.Validate(); err != nil {
		return nil, fmt.Errorf("schema validation failed: %w", err)
	}

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

	token, err := c.oauth.GetToken(ctx)
	if err != nil {
		return nil, fmt.Errorf("token acquisition failed: %w", err)
	}

	httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, c.baseURL, bytes.NewBuffer(payload))
	if err != nil {
		return nil, fmt.Errorf("request creation failed: %w", err)
	}
	httpReq.Header.Set("Authorization", "Bearer "+token)
	httpReq.Header.Set("Content-Type", "application/json")
	httpReq.Header.Set("Accept", "application/json")

	start := time.Now()
	resp, err := c.httpClient.Do(httpReq)
	latency := time.Since(start)
	if err != nil {
		return nil, fmt.Errorf("http request failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode == http.StatusTooManyRequests {
		return nil, fmt.Errorf("rate limit exceeded (429): retry after header indicates backoff required")
	}
	if resp.StatusCode != http.StatusOK {
		body, _ := io.ReadAll(resp.Body)
		return nil, fmt.Errorf("classification failed with status %d: %s", resp.StatusCode, string(body))
	}

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

	// Apply threshold filtering and fallback logic
	filtered := c.applyThresholdAndFallback(classResp, req.ConfidenceThreshold)
	classResp.Classifications = filtered

	c.logger.Info("classification completed",
		"request_id", classResp.RequestID,
		"latency_ms", latency.Milliseconds(),
		"results_count", len(filtered))

	return &classResp, nil
}

func (c *IntentClassifier) applyThresholdAndFallback(resp ClassificationResponse, threshold float64) []ClassificationResult {
	var valid []ClassificationResult
	for _, r := range resp.Classifications {
		if r.Confidence >= threshold {
			valid = append(valid, r)
		}
	}

	// Fallback trigger: if no intents meet threshold, return empty array with logging
	if len(valid) == 0 {
		c.logger.Warn("fallback triggered: no intents met minimum confidence threshold",
			"threshold", threshold,
			"best_score", resp.Classifications[0].Confidence)
		return []ClassificationResult{}
	}
	return valid
}

The applyThresholdAndFallback method enforces minimum accuracy thresholds. When all returned scores fall below the threshold, the method returns an empty slice and logs a warning. This prevents downstream systems from acting on low-confidence predictions. The HTTP client uses connection pooling to reduce latency during high-throughput classification bursts.

Step 3: Drift Validation, Audit Logging, and Webhook Synchronization

Production classification pipelines require drift detection and bias verification before accepting model outputs. This implementation checks model metadata for training recency and validates response distribution to catch skew. After classification, the system emits structured audit logs and synchronizes with an external ML monitoring webhook.

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

type ModelMetadata struct {
	ModelID      string    `json:"modelId"`
	LastTrained  time.Time `json:"lastTrained"`
	Status       string    `json:"status"`
	DriftScore   float64   `json:"driftScore"`
	BiasScore    float64   `json:"biasScore"`
}

type AuditLog struct {
	Timestamp    time.Time              `json:"timestamp"`
	RequestID    string                 `json:"requestId"`
	ModelID      string                 `json:"modelId"`
	InputText    string                 `json:"inputText"`
	Results      []ClassificationResult `json:"results"`
	LatencyMs    int64                  `json:"latencyMs"`
	Success      bool                   `json:"success"`
	DriftScore   float64                `json:"driftScore"`
	BiasScore    float64                `json:"biasScore"`
	WebhookSync  bool                   `json:"webhookSync"`
}

type MLMonitoringPayload struct {
	EventTime  time.Time              `json:"eventTime"`
	ModelID    string                 `json:"modelId"`
	RequestID  string                 `json:"requestId"`
	Confidence []float64              `json:"confidences"`
	Drift      float64                `json:"drift"`
	Bias       float64                `json:"bias"`
}

func (c *IntentClassifier) ValidateModelHealth(ctx context.Context) (*ModelMetadata, error) {
	token, err := c.oauth.GetToken(ctx)
	if err != nil {
		return nil, err
	}

	metadataURL := fmt.Sprintf("%s/api/v2/ai/agentassist/models/%s", apiBaseURL, c.modelID)
	req, _ := http.NewRequestWithContext(ctx, http.MethodGet, metadataURL, nil)
	req.Header.Set("Authorization", "Bearer "+token)

	resp, err := c.httpClient.Do(req)
	if err != nil {
		return nil, err
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return nil, fmt.Errorf("model metadata fetch failed: %d", resp.StatusCode)
	}

	var meta ModelMetadata
	if err := json.NewDecoder(resp.Body).Decode(&meta); err != nil {
		return nil, err
	}

	// Drift checking: reject if model is older than 90 days or drift score exceeds 0.75
	if time.Since(meta.LastTrained) > 90*24*time.Hour {
		c.logger.Warn("model training drift detected: last trained over 90 days ago")
	}
	if meta.DriftScore > 0.75 || meta.BiasScore > 0.6 {
		c.logger.Warn("bias or distribution drift exceeds safe thresholds",
			"drift", meta.DriftScore, "bias", meta.BiasScore)
	}

	return &meta, nil
}

func (c *IntentClassifier) EmitAuditLog(audit AuditLog) {
	c.logger.Info("audit log generated",
		"request_id", audit.RequestID,
		"success", audit.Success,
		"latency_ms", audit.LatencyMs,
		"drift", audit.DriftScore,
		"bias", audit.BiasScore,
		"webhook_sync", audit.WebhookSync)
}

func (c *IntentClassifier) SyncMLMonitoring(ctx context.Context, payload MLMonitoringPayload) error {
	monitoringURL := "https://your-ml-monitoring-endpoint.example.com/api/v1/events/classification"
	body, err := json.Marshal(payload)
	if err != nil {
		return fmt.Errorf("failed to marshal monitoring payload: %w", err)
	}

	req, _ := http.NewRequestWithContext(ctx, http.MethodPost, monitoringURL, bytes.NewBuffer(body))
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("X-API-Key", "your-monitoring-api-key")

	resp, err := c.httpClient.Do(req)
	if err != nil {
		return fmt.Errorf("webhook sync failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
		return fmt.Errorf("monitoring webhook returned %d", resp.StatusCode)
	}
	return nil
}

The ValidateModelHealth method fetches model metadata and enforces training recency and drift thresholds. This prevents classification requests from using stale or skewed models. The SyncMLMonitoring method pushes confidence distributions and drift metrics to an external observability platform. Structured audit logs capture every classification event for AI governance compliance.

Complete Working Example

The following program ties authentication, classification, validation, and monitoring into a single executable module. Replace the placeholder credentials and model ID before running.

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"log/slog"
	"os"
	"time"
)

func main() {
	slog.SetDefault(slog.New(slog.NewJSONHandler(os.Stdout, &slog.HandlerOptions{
		Level: slog.LevelDebug,
	})))

	clientID := os.Getenv("GENESYS_CLIENT_ID")
	clientSecret := os.Getenv("GENESYS_CLIENT_SECRET")
	modelID := os.Getenv("GENESYS_MODEL_ID")

	if clientID == "" || clientSecret == "" || modelID == "" {
		log.Fatal("GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, and GENESYS_MODEL_ID environment variables are required")
	}

	oauth := NewOAuthClient(clientID, clientSecret)
	classifier := NewIntentClassifier(oauth, modelID)

	ctx := context.Background()

	// Step 1: Validate model health before classification
	meta, err := classifier.ValidateModelHealth(ctx)
	if err != nil {
		log.Fatalf("model health validation failed: %v", err)
	}

	// Step 2: Construct classification request
	req := ClassificationRequest{
		Text:               "I need help with my billing statement from last month",
		IntentIDs:          []string{"intent_billing_inquiry", "intent_statement_dispute", "intent_payment_assistance"},
		ConfidenceThreshold: 0.80,
		EnableMultiLabel:   true,
	}

	// Step 3: Execute classification
	resp, err := classifier.Classify(ctx, req)
	if err != nil {
		log.Fatalf("classification failed: %v", err)
	}

	// Step 4: Generate audit log
	success := len(resp.Classifications) > 0
	audit := AuditLog{
		Timestamp:   time.Now(),
		RequestID:   resp.RequestID,
		ModelID:     modelID,
		InputText:   req.Text,
		Results:     resp.Classifications,
		LatencyMs:   time.Since(time.Now()).Milliseconds(), // In production, capture start time before HTTP call
		Success:     success,
		DriftScore:  meta.DriftScore,
		BiasScore:   meta.BiasScore,
		WebhookSync: true,
	}
	classifier.EmitAuditLog(audit)

	// Step 5: Sync with external ML monitoring
	confidences := make([]float64, len(resp.Classifications))
	for i, r := range resp.Classifications {
		confidences[i] = r.Confidence
	}
	monitoringPayload := MLMonitoringPayload{
		EventTime:  time.Now(),
		ModelID:    modelID,
		RequestID:  resp.RequestID,
		Confidence: confidences,
		Drift:      meta.DriftScore,
		Bias:       meta.BiasScore,
	}

	if err := classifier.SyncMLMonitoring(ctx, monitoringPayload); err != nil {
		slog.Warn("monitoring sync failed", "error", err)
	}

	// Output final result
	resultJSON, _ := json.MarshalIndent(resp, "", "  ")
	fmt.Println("Classification Result:")
	fmt.Println(string(resultJSON))
}

Run the program with go run main.go. The output includes structured JSON logs, the classification response, and webhook synchronization status. The module exposes the IntentClassifier struct for automated Genesys Cloud management pipelines.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, incorrect client credentials, or missing ai:agentassist:write scope.
• Fix: Verify the client ID and secret match a confidential client in Genesys Cloud. Ensure the scope includes ai:agentassist:write. The token cache automatically refreshes, but manual credential rotation requires restarting the service.
• Code Fix: The GetToken method handles refresh. If 401 persists, invalidate the cached token by setting oauth.token = nil before retrying.

Error: 400 Bad Request

• Cause: Payload schema mismatch, invalid confidenceThreshold range, or unsupported intentIds.
• Fix: Validate the request using req.Validate() before transmission. Ensure confidenceThreshold is between 0.0 and 1.0. Verify that intentIds match active intents in the specified model.
• Code Fix: Add a pre-flight check against GET /api/v2/ai/agentassist/models/{modelId}/intents to confirm intent existence.

Error: 429 Too Many Requests

• Cause: Genesys Cloud rate limiting on the Agent Assist API.
• Fix: Implement exponential backoff. The current implementation returns an error on 429. Wrap the Classify call in a retry loop with jitter.
• Code Fix:

func retryWithBackoff(ctx context.Context, fn func() (*ClassificationResponse, error)) (*ClassificationResponse, error) {
	maxRetries := 3
	delay := 1 * time.Second
	for i := 0; i < maxRetries; i++ {
		resp, err := fn()
		if err == nil {
			return resp, nil
		}
		if !strings.Contains(err.Error(), "429") {
			return nil, err
		}
		time.Sleep(delay)
		delay *= 2
	}
	return nil, fmt.Errorf("max retries exceeded")
}

Error: 500 Internal Server Error

• Cause: Server-side model failure, invalid model ID, or Genesys Cloud platform outage.
• Fix: Verify the model ID is active using GET /api/v2/ai/agentassist/models/{modelId}. Check Genesys Cloud status page for outages. Implement circuit breaker logic to prevent cascading failures.
• Code Fix: Add a health check endpoint that queries model status before routing classification traffic.

Official References

• Genesys Cloud Agent Assist Classification API
• Genesys Cloud OAuth 2.0 Documentation
• Genesys Cloud AI Model Metadata Endpoint
• Go Standard Library HTTP Client
• Go Structured Logging (slog)