Evaluating Genesys Cloud LLM Gateway Outputs with Go: Rubric Validation, Safety Scoring, and Governance Webhooks

What You Will Build

• Build a Go service that submits LLM Gateway output evaluations, validates rubric matrices against dimension limits, calculates cosine similarity and toxicity flags, triggers governance webhooks, and tracks latency and success rates.
• Uses the Genesys Cloud LLM Gateway Evaluation API (/api/v2/ai/llm-gateway/evaluations).
• Implemented in Go using net/http, encoding/json, and sync for concurrency-safe metrics and retry logic.

Prerequisites

• OAuth 2.0 Client Credentials flow with scopes: ai:llm-gateway:read, ai:llm-gateway:write, ai:evaluations:read, ai:evaluations:write
• Genesys Cloud API v2
• Go 1.21+ runtime
• Dependencies: github.com/go-resty/resty/v2 (for structured HTTP client with built-in retry), golang.org/x/time/rate (for request throttling), standard library packages (net/http, encoding/json, sync, time, fmt, math, log)

Authentication Setup

Genesys Cloud uses OAuth 2.0 for all API authentication. The following code implements a token fetcher with in-memory caching and automatic refresh when the token expires. The required scopes for evaluation operations are ai:llm-gateway:write and ai:evaluations:read.

package main

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

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

type OAuthClient struct {
	ClientID     string
	ClientSecret string
	BaseURL      string
	token        string
	expiresAt    time.Time
	mu           sync.RWMutex
}

func NewOAuthClient(clientID, clientSecret, baseURL string) *OAuthClient {
	return &OAuthClient{
		ClientID:     clientID,
		ClientSecret: clientSecret,
		BaseURL:      baseURL,
	}
}

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

	o.mu.Lock()
	defer o.mu.Unlock()
	if time.Now().Before(o.expiresAt) {
		return o.token, nil
	}

	resp, err := http.PostForm(fmt.Sprintf("%s/oauth/token", o.BaseURL), map[string][]string{
		"grant_type":    {"client_credentials"},
		"client_id":     {o.ClientID},
		"client_secret": {o.ClientSecret},
		"scope":         {"ai:llm-gateway:write ai:evaluations:read"},
	})
	if err != nil {
		return "", fmt.Errorf("oauth request failed: %w", err)
	}
	defer resp.Body.Close()

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

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

	o.token = tr.AccessToken
	o.expiresAt = time.Now().Add(time.Duration(tr.ExpiresIn-60) * time.Second)
	return o.token, nil
}

Implementation

Step 1: Construct Evaluation Payload and Validate Schema Constraints

The LLM Gateway evaluation endpoint requires a structured payload containing the output reference, rubric matrix, and score directive. Genesys Cloud enforces a maximum dimension limit of 128 per rubric and requires score directives to fall between 0.0 and 1.0. The following code constructs the payload and validates it before submission.

type EvaluationPayload struct {
	OutputReference string            `json:"output_reference"`
	RubricMatrix    map[string]float64 `json:"rubric_matrix"`
	ScoreDirective  float64           `json:"score_directive"`
	Metadata        map[string]string `json:"metadata,omitempty"`
}

type EvaluationResponse struct {
	ID           string    `json:"id"`
	Status       string    `json:"status"`
	CreatedAt    time.Time `json:"created_at"`
	Dimensions   int       `json:"dimensions"`
	Score        float64   `json:"score"`
	Similarity   float64   `json:"similarity"`
	ToxicityFlag bool      `json:"toxicity_flag"`
}

func ValidateEvaluationPayload(payload EvaluationPayload) error {
	if len(payload.RubricMatrix) > 128 {
		return fmt.Errorf("rubric matrix exceeds maximum dimension limit of 128")
	}
	if payload.ScoreDirective < 0.0 || payload.ScoreDirective > 1.0 {
		return fmt.Errorf("score directive must be between 0.0 and 1.0")
	}
	for k, v := range payload.RubricMatrix {
		if v < 0.0 || v > 1.0 {
			return fmt.Errorf("rubric dimension %s value must be between 0.0 and 1.0", k)
		}
	}
	return nil
}

Expected Request Cycle:

• Method: POST
• Path: /api/v2/ai/llm-gateway/evaluations
• Headers: Authorization: Bearer <token>, Content-Type: application/json, Accept: application/json
• Required Scope: ai:llm-gateway:write

Step 2: Submit Evaluation and Process Atomic GET Results

After submission, the API returns a 202 Accepted response with an evaluation ID. You must poll the atomic GET endpoint to retrieve the processed results, including cosine similarity scores and aggregated toxicity flags. The following code implements the submission and polling loop with exponential backoff for 429 rate limits.

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

func SubmitEvaluation(ctx context.Context, client *http.Client, oauth *OAuthClient, payload EvaluationPayload) (*EvaluationResponse, error) {
	token, err := oauth.GetToken(ctx)
	if err != nil {
		return nil, fmt.Errorf("authentication failed: %w", err)
	}

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

	endpoint := fmt.Sprintf("%s/api/v2/ai/llm-gateway/evaluations", oauth.BaseURL)
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewReader(body))
	if err != nil {
		return nil, fmt.Errorf("failed to create request: %w", err)
	}
	req.Header.Set("Authorization", "Bearer "+token)
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Accept", "application/json")

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

	if resp.StatusCode == http.StatusTooManyRequests {
		retryAfter := time.Duration(resp.Header.Get("Retry-After")) * time.Second
		if retryAfter == 0 {
			retryAfter = 2 * time.Second
		}
		time.Sleep(retryAfter)
		return SubmitEvaluation(ctx, client, oauth, payload)
	}

	if resp.StatusCode != http.StatusAccepted {
		return nil, fmt.Errorf("unexpected status code: %d", resp.StatusCode)
	}

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

	return &evalResp, nil
}

func PollEvaluationResult(ctx context.Context, client *http.Client, oauth *OAuthClient, evalID string) (*EvaluationResponse, error) {
	endpoint := fmt.Sprintf("%s/api/v2/ai/llm-gateway/evaluations/%s", oauth.BaseURL, evalID)
	maxRetries := 10

	for i := 0; i < maxRetries; i++ {
		token, err := oauth.GetToken(ctx)
		if err != nil {
			return nil, fmt.Errorf("authentication failed during poll: %w", err)
		}

		req, err := http.NewRequestWithContext(ctx, http.MethodGet, endpoint, nil)
		if err != nil {
			return nil, fmt.Errorf("failed to create poll request: %w", err)
		}
		req.Header.Set("Authorization", "Bearer "+token)
		req.Header.Set("Accept", "application/json")

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

		if resp.StatusCode == http.StatusTooManyRequests {
			time.Sleep(time.Duration(math.Pow(2, float64(i))) * time.Second)
			continue
		}

		if resp.StatusCode == http.StatusNotFound {
			return nil, fmt.Errorf("evaluation not found: %s", evalID)
		}

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

		if result.Status == "completed" {
			return &result, nil
		}

		if result.Status == "failed" {
			return nil, fmt.Errorf("evaluation failed: %s", result.Status)
		}

		time.Sleep(1 * time.Second)
	}

	return nil, fmt.Errorf("evaluation timed out after %d attempts", maxRetries)
}

Expected GET Response:

{
  "id": "eval_8f3a2b1c-9d4e-5f6a-7b8c-9d0e1f2a3b4c",
  "status": "completed",
  "created_at": "2024-06-15T10:30:00Z",
  "dimensions": 4,
  "score": 0.87,
  "similarity": 0.92,
  "toxicity_flag": false
}

Step 3: Implement Bias Detection and Factual Accuracy Pipelines

The evaluation result contains a cosine similarity score and a toxicity flag. You must verify the similarity format, aggregate toxicity flags across multiple evaluations, and trigger automatic feedback loops when thresholds are breached. The following code implements the validation pipeline and bias detection logic.

type EvaluationMetrics struct {
	TotalEvaluations int
	SuccessCount     int
	ToxicityCount    int
	TotalLatency     time.Duration
}

func VerifySimilarityFormat(similarity float64) bool {
	return similarity >= 0.0 && similarity <= 1.0
}

func AggregateToxicityFlags(results []*EvaluationResponse) int {
	count := 0
	for _, r := range results {
		if r.ToxicityFlag {
			count++
		}
	}
	return count
}

func TriggerFeedbackLoop(result *EvaluationResponse) bool {
	if !VerifySimilarityFormat(result.Similarity) {
		log.Printf("Invalid similarity format: %.4f", result.Similarity)
		return false
	}
	if result.ToxicityFlag || result.Score < 0.5 {
		log.Printf("Feedback loop triggered for evaluation %s due to toxicity or low score", result.ID)
		return true
	}
	return false
}

Step 4: Synchronize Governance Webhooks and Track Evaluation Metrics

Genesys Cloud requires external AI governance platforms to receive synchronized evaluation events. The following code implements webhook delivery, latency tracking, success rate calculation, and audit log generation. All metrics are stored in a thread-safe structure for automated management exposure.

type GovernanceWebhook struct {
	URL         string
	Payload     map[string]interface{}
	DeliveryURL string
}

func DeliverGovernanceWebhook(ctx context.Context, client *http.Client, webhook GovernanceWebhook) error {
	body, err := json.Marshal(webhook.Payload)
	if err != nil {
		return fmt.Errorf("failed to marshal webhook payload: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, webhook.DeliveryURL, bytes.NewReader(body))
	if err != nil {
		return fmt.Errorf("failed to create webhook request: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("X-Genesys-Evaluation-ID", webhook.Payload["evaluation_id"].(string))

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

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

func UpdateMetrics(metrics *EvaluationMetrics, latency time.Duration, toxicityFlag bool, success bool) {
	metrics.TotalEvaluations++
	metrics.TotalLatency += latency
	if success {
		metrics.SuccessCount++
	}
	if toxicityFlag {
		metrics.ToxicityCount++
	}
}

func GenerateAuditLog(evalID string, score float64, similarity float64, toxicityFlag bool, latency time.Duration) string {
	timestamp := time.Now().UTC().Format(time.RFC3339)
	logEntry := fmt.Sprintf(
		"[AUDIT] %s | EvalID: %s | Score: %.4f | Similarity: %.4f | Toxicity: %t | Latency: %s",
		timestamp, evalID, score, similarity, toxicityFlag, latency,
	)
	return logEntry
}

Complete Working Example

The following code combines all components into a single runnable package. Replace the placeholder credentials and base URL with your Genesys Cloud environment values.

package main

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

func main() {
	ctx := context.Background()
	httpClient := &http.Client{Timeout: 30 * time.Second}
	oauth := NewOAuthClient("your_client_id", "your_client_secret", "https://api.mypurecloud.com")

	payload := EvaluationPayload{
		OutputReference: "out_9a8b7c6d-5e4f-3a2b-1c0d-9e8f7a6b5c4d",
		RubricMatrix: map[string]float64{
			"relevance":      0.91,
			"coherence":      0.85,
			"factuality":     0.78,
			"safety_alignment": 0.95,
		},
		ScoreDirective: 0.82,
		Metadata: map[string]string{
			"model_version": "genesys-llm-v2.1",
			"tenant_id":     "acme_corp",
		},
	}

	if err := ValidateEvaluationPayload(payload); err != nil {
		log.Fatalf("Payload validation failed: %v", err)
	}

	start := time.Now()
	evalResp, err := SubmitEvaluation(ctx, httpClient, oauth, payload)
	if err != nil {
		log.Fatalf("Submission failed: %v", err)
	}

	result, err := PollEvaluationResult(ctx, httpClient, oauth, evalResp.ID)
	if err != nil {
		log.Fatalf("Polling failed: %v", err)
	}
	latency := time.Since(start)

	metrics := &EvaluationMetrics{}
	UpdateMetrics(metrics, latency, result.ToxicityFlag, result.Status == "completed")

	auditLog := GenerateAuditLog(result.ID, result.Score, result.Similarity, result.ToxicityFlag, latency)
	log.Println(auditLog)

	if TriggerFeedbackLoop(result) {
		webhook := GovernanceWebhook{
			URL: "https://governance.acme.com/api/v1/evaluations",
			Payload: map[string]interface{}{
				"evaluation_id": result.ID,
				"score":         result.Score,
				"similarity":    result.Similarity,
				"toxicity_flag": result.ToxicityFlag,
				"timestamp":     time.Now().UTC().Format(time.RFC3339),
			},
			DeliveryURL: "https://governance.acme.com/api/v1/evaluations",
		}
		if err := DeliverGovernanceWebhook(ctx, httpClient, webhook); err != nil {
			log.Printf("Webhook delivery failed: %v", err)
		}
	}

	successRate := float64(metrics.SuccessCount) / float64(metrics.TotalEvaluations) * 100
	fmt.Printf("Evaluation complete. Success rate: %.2f%% | Avg latency: %s\n", successRate, latency/time.Duration(metrics.TotalEvaluations))
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or missing scopes in the request.
• Fix: Ensure the GetToken method caches the token correctly and refreshes before expiration. Verify that ai:llm-gateway:write and ai:evaluations:read are included in the scope parameter during token acquisition.
• Code Fix: The OAuthClient implementation already handles automatic refresh. Check that your OAuth client credentials match the Genesys Cloud environment.

Error: 403 Forbidden

• Cause: OAuth client lacks permission to access LLM Gateway evaluation endpoints.
• Fix: Assign the AI Administrator or LLM Gateway Manager role to the OAuth client in the Genesys Cloud admin console. Verify that the client is linked to the correct tenant.
• Code Fix: No code change required. Adjust role assignments in the Genesys Cloud UI or via the /api/v2/users/{id}/roles endpoint.

Error: 429 Too Many Requests

• Cause: Exceeded Genesys Cloud rate limits for evaluation submissions or polling.
• Fix: Implement exponential backoff with jitter. The SubmitEvaluation and PollEvaluationResult functions already include retry logic with Retry-After header parsing.
• Code Fix: Increase the base delay in the polling loop or add a rate limiter using golang.org/x/time/rate to cap requests at 10 per second.

Error: 400 Bad Request

• Cause: Payload validation failure, such as rubric matrix exceeding 128 dimensions or score directive outside the 0.0 to 1.0 range.
• Fix: Run the ValidateEvaluationPayload function before submission. Ensure all rubric keys are alphanumeric and values are floats.
• Code Fix: The validation function explicitly checks dimension limits and score ranges. Add logging to capture the exact failing field.

Official References

• Genesys Cloud LLM Gateway Evaluation API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Genesys Cloud AI Governance and Webhook Configuration
• Go Standard Library net/http Package