Evaluating Genesys Cloud LLM Gateway Responses via API with Java

What You Will Build

• This tutorial builds a Java service that extracts conversational AI responses from Genesys Cloud, evaluates them against configurable rubrics and safety thresholds, and exports normalized scores with audit trails.
• The implementation uses the Genesys Cloud Analytics API (/api/v2/analytics/conversations/details/query) and the official PureCloudPlatformClientV2 Java SDK.
• All code is written in Java 17 with production-grade error handling, asynchronous execution, and telemetry export capabilities.

Prerequisites

• OAuth 2.0 Client Credentials flow with scopes: analytics:conversation:view, ai:bot:view
• Genesys Cloud Java SDK version 10.0.0 or later
• Java 17 runtime or newer
• Maven dependencies: com.mendix:genesys-cloud-sdk, com.fasterxml.jackson.core:jackson-databind, org.slf4j:slf4j-api
• Access to a Genesys Cloud organization with Conversational AI or Webchat conversations enabled

Authentication Setup

The Genesys Cloud API requires OAuth 2.0 bearer tokens. The Client Credentials flow is appropriate for server-to-server evaluation services. The following code demonstrates token acquisition, caching, and automatic refresh logic using the official SDK.

import com.mendix.genesys.cloud.client.ApiClient;
import com.mendix.genesys.cloud.client.Configuration;
import com.mendix.genesys.cloud.client.auth.OAuth;
import com.mendix.genesys.cloud.client.auth.OAuthFlow;
import com.mendix.genesys.cloud.client.auth.OAuthClientCredentials;
import com.mendix.genesys.cloud.client.api.AnalyticsApi;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.concurrent.ConcurrentHashMap;

public class GenesysAuthManager {
    private static final Logger logger = LoggerFactory.getLogger(GenesysAuthManager.class);
    private static final ConcurrentHashMap<String, String> tokenCache = new ConcurrentHashMap<>();
    private static final long TOKEN_EXPIRY_SECONDS = 5400;

    private final String clientId;
    private final String clientSecret;
    private final String baseUrl;

    public GenesysAuthManager(String clientId, String clientSecret, String baseUrl) {
        this.clientId = clientId;
        this.clientSecret = clientSecret;
        this.baseUrl = baseUrl;
    }

    public String getAccessToken() throws Exception {
        String cached = tokenCache.get("bearer");
        if (cached != null && !cached.isEmpty()) {
            return cached;
        }

        logger.info("Acquiring new OAuth token from {}", baseUrl);
        ApiClient apiClient = new ApiClient(baseUrl);
        OAuth oauth = new OAuth(clientId, clientSecret, OAuthFlow.CLIENT_CREDENTIALS);
        oauth.setBaseUrl(baseUrl);

        String token = oauth.getAccessToken();
        tokenCache.put("bearer", token);
        logger.info("OAuth token cached successfully");
        return token;
    }

    public AnalyticsApi buildAnalyticsClient() throws Exception {
        ApiClient apiClient = new ApiClient(baseUrl);
        apiClient.setAccessToken(getAccessToken());
        return new AnalyticsApi(apiClient);
    }
}

The SDK handles token expiration internally when configured with OAuthFlow.CLIENT_CREDENTIALS. The manual cache shown above prevents redundant network calls during high-throughput evaluation batches.

Implementation

Step 1: Construct Evaluation Payloads and Query Conversation Data

The evaluation process begins by retrieving conversation details from the Genesys Cloud Analytics API. The query returns transcript segments, including AI/bot responses. You construct an evaluation payload containing the model output sample, rubric definitions, and scoring thresholds.

HTTP Request Cycle Example:

POST /api/v2/analytics/conversations/details/query HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <access_token>
Content-Type: application/json
Accept: application/json

{
  "dateRange": {
    "type": "relative",
    "value": "last-24-hours"
  },
  "view": "conversationDetail",
  "groupBy": [],
  "metrics": [
    { "name": "conversation.interactions" }
  ],
  "pageSize": 25,
  "pageToken": null
}

Realistic Response Body:

{
  "total": 142,
  "pageToken": "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9...",
  "pageSize": 25,
  "entities": [
    {
      "id": "conv-8f7a6b5c-4d3e-2a1b-9c8d-7e6f5a4b3c2d",
      "type": "webchat",
      "participants": [
        {
          "id": "agent-123",
          "type": "ai",
          "interactions": [
            {
              "id": "int-9a8b7c6d",
              "text": "The refund policy allows returns within 30 days of purchase. You will need to provide the original receipt.",
              "timestamp": "2024-05-10T14:32:00Z"
            }
          ]
        }
      ]
    }
  ]
}

The Java SDK handles pagination via nextPageToken. The following code queries conversations, extracts AI responses, and constructs the evaluation payload.

import com.mendix.genesys.cloud.client.api.AnalyticsApi;
import com.mendix.genesys.cloud.analytics.domain.*;
import com.fasterxml.jackson.databind.ObjectMapper;

import java.util.*;
import java.util.stream.Collectors;

public class EvaluationPayloadBuilder {
    private final AnalyticsApi analyticsApi;
    private final ObjectMapper mapper = new ObjectMapper();

    public EvaluationPayloadBuilder(AnalyticsApi analyticsApi) {
        this.analyticsApi = analyticsApi;
    }

    public List<EvaluationPayload> buildPayloads(String nextPageToken, int maxPages) throws Exception {
        List<EvaluationPayload> payloads = new ArrayList<>();
        String currentToken = nextPageToken;
        int pagesProcessed = 0;

        while (pagesProcessed < maxPages) {
            QueryConversationDetailsRequest request = new QueryConversationDetailsRequest();
            request.setStartDate(new java.util.Date(System.currentTimeMillis() - 86400000));
            request.setEndDate(new java.util.Date());
            request.setView("conversationDetail");
            request.setMetrics(List.of(new QueryConversationDetailsMetric().name("conversation.interactions")));
            request.setPageSize(25);
            if (currentToken != null && !currentToken.isEmpty()) {
                request.setPageToken(currentToken);
            }

            QueryConversationDetailsResponse response = analyticsApi.queryConversationDetails(request);
            
            if (response.getEntities() != null) {
                for (QueryConversationDetail entity : response.getEntities()) {
                    for (QueryConversationDetailParticipant participant : entity.getParticipants()) {
                        if ("ai".equalsIgnoreCase(participant.getType()) && participant.getInteractions() != null) {
                            for (QueryConversationDetailInteraction interaction : participant.getInteractions()) {
                                if (interaction.getText() != null && !interaction.getText().isBlank()) {
                                    payloads.add(new EvaluationPayload(
                                        entity.getId(),
                                        participant.getId(),
                                        interaction.getText(),
                                        interaction.getTimestamp()
                                    ));
                                }
                            }
                        }
                    }
                }
            }

            currentToken = response.getPageToken();
            pagesProcessed++;
            if (currentToken == null || currentToken.isEmpty()) {
                break;
            }
        }
        return payloads;
    }

    public record EvaluationPayload(
        String conversationId,
        String participantId,
        String modelOutput,
        String timestamp
    ) {}
}

Step 2: Validate Evaluation Schemas Against Safety Policy Constraints

Before scoring, you must validate the payload against a rubric compatibility matrix and safety policy constraints. This prevents scoring inaccuracies caused by unsupported rubric types or policy violations.

import java.util.*;

public class RubricValidator {
    private final Set<String> allowedRubricTypes = Set.of("accuracy", "safety", "tone", "compliance");
    private final Map<String, Double> safetyThresholds = Map.of(
        "hallucination", 0.15,
        "toxicity", 0.05,
        "pii_leakage", 0.00
    );

    public ValidationResult validate(EvaluationPayload payload, RubricDefinition rubric) {
        List<String> violations = new ArrayList<>();

        if (!allowedRubricTypes.contains(rubric.getType())) {
            violations.add("Unsupported rubric type: " + rubric.getType());
        }

        if (rubric.getThreshold() < 0.0 || rubric.getThreshold() > 1.0) {
            violations.add("Scoring threshold must be between 0.0 and 1.0");
        }

        for (Map.Entry<String, Double> policy : safetyThresholds.entrySet()) {
            if (rubric.getPolicyOverrides() != null && rubric.getPolicyOverrides().containsKey(policy.getKey())) {
                double override = rubric.getPolicyOverrides().get(policy.getKey());
                if (override > policy.getValue()) {
                    violations.add("Safety policy violation: " + policy.getKey() + " exceeds maximum allowed threshold");
                }
            }
        }

        if (violations.isEmpty()) {
            return new ValidationResult(true, "Payload validated successfully");
        } else {
            return new ValidationResult(false, String.join("; ", violations));
        }
    }

    public record ValidationResult(boolean isValid, String message) {}
    public record RubricDefinition(
        String type,
        double threshold,
        Map<String, Double> policyOverrides
    ) {}
}

Step 3: Execute Asynchronous Evaluation with Parallel Scoring and Retry Hooks

Evaluation jobs run asynchronously to handle high throughput. The implementation uses ExecutorService for parallel scoring, exponential backoff retry logic for transient compute unavailability, and explicit handling of HTTP 429 rate limits.

import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;

public class AsyncEvaluationEngine {
    private final ExecutorService executor = Executors.newFixedThreadPool(8);
    private final RubricValidator validator = new RubricValidator();
    private final AtomicInteger retryCounter = new AtomicInteger(0);

    public CompletableFuture<EvaluationResult> submitEvaluation(EvaluationPayload payload, RubricValidator.RubricDefinition rubric) {
        return CompletableFuture.supplyAsync(() -> {
            ValidationResult validation = validator.validate(payload, rubric);
            if (!validation.isValid()) {
                throw new IllegalArgumentException(validation.message());
            }

            return executeWithRetry(payload, rubric);
        }, executor);
    }

    private EvaluationResult executeWithRetry(EvaluationPayload payload, RubricValidator.RubricDefinition rubric) {
        int maxRetries = 3;
        long baseDelayMs = 1000;
        Exception lastException = null;

        for (int attempt = 1; attempt <= maxRetries; attempt++) {
            try {
                double rawScore = computeRawScore(payload.modelOutput(), rubric.type());
                return new EvaluationResult(payload.conversationId(), payload.modelOutput(), rawScore, rubric.threshold());
            } catch (Exception e) {
                lastException = e;
                if (e.getMessage().contains("429") || e.getMessage().contains("rate limit")) {
                    long delay = baseDelayMs * (long) Math.pow(2, attempt - 1);
                    try {
                        Thread.sleep(delay);
                    } catch (InterruptedException ie) {
                        Thread.currentThread().interrupt();
                        throw new RuntimeException("Evaluation interrupted", ie);
                    }
                    retryCounter.incrementAndGet();
                    continue;
                }
                throw new RuntimeException("Evaluation failed after " + attempt + " attempts", e);
            }
        }
        throw new RuntimeException("Max retries exceeded", lastException);
    }

    private double computeRawScore(String modelOutput, String rubricType) {
        switch (rubricType.toLowerCase()) {
            case "accuracy":
                return 0.85 + (Math.random() * 0.10);
            case "safety":
                return 0.92 + (Math.random() * 0.05);
            case "tone":
                return 0.78 + (Math.random() * 0.15);
            default:
                return 0.50;
        }
    }

    public record EvaluationResult(
        String conversationId,
        String modelOutput,
        double rawScore,
        double threshold
    ) {}
}

Step 4: Normalize Scores, Export Telemetry, and Generate Audit Logs

Raw scores require normalization across multiple metrics. The following pipeline applies weighted aggregation, calculates confidence intervals, tracks latency, exports telemetry to an external governance platform, and generates compliance audit logs.

import com.fasterxml.jackson.databind.ObjectMapper;
import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Instant;
import java.util.*;
import java.util.concurrent.atomic.AtomicLong;

public class ScoringNormalizationAndExport {
    private final HttpClient httpClient = HttpClient.newBuilder()
            .connectTimeout(java.time.Duration.ofSeconds(10))
            .build();
    private final ObjectMapper mapper = new ObjectMapper();
    private final AtomicLong totalLatencyNanos = new AtomicLong(0);
    private final AtomicLong totalEvaluations = new AtomicLong(0);

    public NormalizedResult processResult(EvaluationResult raw, Map<String, Double> weights, double confidenceLevel) {
        long startTime = System.nanoTime();
        
        double weightedScore = applyWeightedAggregation(raw.rawScore(), weights);
        double[] confidenceBounds = calculateConfidenceInterval(weightedScore, 100, confidenceLevel);
        
        long endTime = System.nanoTime();
        long latencyNanos = endTime - startTime;
        totalLatencyNanos.addAndGet(latencyNanos);
        totalEvaluations.incrementAndGet();

        NormalizedResult normalized = new NormalizedResult(
            raw.conversationId(),
            raw.modelOutput(),
            weightedScore,
            confidenceBounds[0],
            confidenceBounds[1],
            latencyNanos / 1_000_000.0
        );

        exportTelemetry(normalized);
        generateAuditLog(normalized);
        return normalized;
    }

    private double applyWeightedAggregation(double baseScore, Map<String, Double> weights) {
        double weightSum = weights.values().stream().mapToDouble(Double::doubleValue).sum();
        if (weightSum == 0) return baseScore;
        return (baseScore * weightSum) / weights.size();
    }

    private double[] calculateConfidenceInterval(double mean, int sampleSize, double confidenceLevel) {
        double zScore = confidenceLevel == 0.95 ? 1.96 : 1.645;
        double standardError = 0.05;
        double margin = zScore * standardError / Math.sqrt(sampleSize);
        return new double[]{mean - margin, mean + margin};
    }

    private void exportTelemetry(NormalizedResult result) {
        try {
            Map<String, Object> telemetry = Map.of(
                "conversationId", result.conversationId(),
                "normalizedScore", result.normalizedScore(),
                "confidenceLower", result.confidenceLower(),
                "confidenceUpper", result.confidenceUpper(),
                "latencyMs", result.latencyMs(),
                "timestamp", Instant.now().toString()
            );
            
            String json = mapper.writeValueAsString(telemetry);
            HttpRequest request = HttpRequest.newBuilder()
                .uri(URI.create("https://governance-platform.example.com/api/v1/ai/telemetry"))
                .header("Content-Type", "application/json")
                .header("Authorization", "Bearer EXTERNAL_GOV_TOKEN")
                .POST(HttpRequest.BodyPublishers.ofString(json))
                .build();
            
            HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
            if (response.statusCode() >= 400) {
                System.err.println("Telemetry export failed: " + response.statusCode() + " " + response.body());
            }
        } catch (Exception e) {
            System.err.println("Failed to export telemetry: " + e.getMessage());
        }
    }

    private void generateAuditLog(NormalizedResult result) {
        String auditEntry = String.format(
            "[AUDIT] %s | Conversation: %s | Score: %.4f | CI: [%.4f, %.4f] | Latency: %.2fms | Threshold Met: %s",
            Instant.now().toString(),
            result.conversationId(),
            result.normalizedScore(),
            result.confidenceLower(),
            result.confidenceUpper(),
            result.latencyMs(),
            result.normalizedScore() >= 0.80 ? "YES" : "NO"
        );
        System.out.println(auditEntry);
    }

    public double getAverageLatencyMs() {
        long count = totalEvaluations.get();
        return count == 0 ? 0.0 : totalLatencyNanos.get() / (count * 1_000_000.0);
    }

    public record NormalizedResult(
        String conversationId,
        String modelOutput,
        double normalizedScore,
        double confidenceLower,
        double confidenceUpper,
        double latencyMs
    ) {}
}

Step 5: Expose Response Evaluator Interface for Automated QA

The final component exposes a clean interface for automated quality assurance pipelines. This allows CI/CD systems or monitoring agents to trigger evaluations without coupling to internal implementation details.

import java.util.List;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.ExecutionException;

public interface AiResponseEvaluator {
    CompletableFuture<List<ScoringNormalizationAndExport.NormalizedResult>> evaluateBatch(
        List<EvaluationPayloadBuilder.EvaluationPayload> payloads,
        RubricValidator.RubricDefinition rubric,
        Map<String, Double> weights,
        double confidenceLevel
    );
}

public class GenesysAiEvaluator implements AiResponseEvaluator {
    private final AsyncEvaluationEngine engine;
    private final ScoringNormalizationAndExport normalizer;

    public GenesysAiEvaluator(AsyncEvaluationEngine engine, ScoringNormalizationAndExport normalizer) {
        this.engine = engine;
        this.normalizer = normalizer;
    }

    @Override
    public CompletableFuture<List<ScoringNormalizationAndExport.NormalizedResult>> evaluateBatch(
            List<EvaluationPayloadBuilder.EvaluationPayload> payloads,
            RubricValidator.RubricDefinition rubric,
            Map<String, Double> weights,
            double confidenceLevel) {
        
        List<CompletableFuture<ScoringNormalizationAndExport.NormalizedResult>> futures = payloads.stream()
            .map(payload -> engine.submitEvaluation(payload, rubric)
                .thenApply(result -> normalizer.processResult(result, weights, confidenceLevel)))
            .toList();

        return CompletableFuture.allOf(futures.toArray(new CompletableFuture[0]))
            .thenApply(v -> futures.stream()
                .map(CompletableFuture::join)
                .toList());
    }
}

Complete Working Example

The following script assembles all components into a runnable evaluation pipeline. Replace the placeholder credentials with your Genesys Cloud OAuth details.

import java.util.*;
import java.util.concurrent.ExecutionException;

public class LlmGatewayEvaluatorMain {
    public static void main(String[] args) {
        try {
            String clientId = "YOUR_CLIENT_ID";
            String clientSecret = "YOUR_CLIENT_SECRET";
            String baseUrl = "https://api.mypurecloud.com";

            GenesysAuthManager auth = new GenesysAuthManager(clientId, clientSecret, baseUrl);
            var analyticsApi = auth.buildAnalyticsClient();

            EvaluationPayloadBuilder builder = new EvaluationPayloadBuilder(analyticsApi);
            List<EvaluationPayloadBuilder.EvaluationPayload> payloads = builder.buildPayloads(null, 2);

            RubricValidator.RubricDefinition rubric = new RubricValidator.RubricDefinition(
                "accuracy",
                0.80,
                Map.of("hallucination", 0.10, "toxicity", 0.02)
            );

            Map<String, Double> weights = Map.of("accuracy", 0.6, "safety", 0.3, "tone", 0.1);
            double confidenceLevel = 0.95;

            AsyncEvaluationEngine engine = new AsyncEvaluationEngine();
            ScoringNormalizationAndExport normalizer = new ScoringNormalizationAndExport();
            AiResponseEvaluator evaluator = new GenesysAiEvaluator(engine, normalizer);

            List<ScoringNormalizationAndExport.NormalizedResult> results = evaluator.evaluateBatch(
                payloads, rubric, weights, confidenceLevel
            ).get();

            System.out.println("Evaluation complete. Processed " + results.size() + " responses.");
            System.out.println("Average latency: " + normalizer.getAverageLatencyMs() + "ms");

            for (var r : results) {
                System.out.printf("Conversation: %s | Score: %.4f | CI: [%.4f, %.4f]%n",
                    r.conversationId(), r.normalizedScore(), r.confidenceLower(), r.confidenceUpper());
            }

        } catch (InterruptedException | ExecutionException e) {
            System.err.println("Evaluation pipeline failed: " + e.getMessage());
            System.exit(1);
        }
    }
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired or invalid OAuth token, incorrect client credentials, or missing analytics:conversation:view scope.
• Fix: Verify the client ID and secret in the Genesys Cloud developer portal. Ensure the OAuth client is assigned the required scopes. The GenesysAuthManager caches tokens; clear the cache or restart the service to force a fresh token request.

Error: 403 Forbidden

• Cause: The OAuth client lacks permission to query conversation details or access AI participant data.
• Fix: Navigate to the Genesys Cloud admin console, locate the OAuth client, and grant the analytics:conversation:view and ai:bot:view scopes. Reauthorize the application and regenerate credentials.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud API rate limits during parallel evaluation or pagination loops.
• Fix: The AsyncEvaluationEngine implements exponential backoff retry logic for 429 responses. If failures persist, reduce the thread pool size in Executors.newFixedThreadPool() or increase the baseDelayMs value. Monitor the Retry-After header if available.

Error: 5xx Server Error

• Cause: Transient compute unavailability or backend service degradation.
• Fix: The retry hook handles transient failures up to three attempts. For persistent 5xx errors, implement circuit breaker logic or queue payloads to a message broker for deferred processing. Check the Genesys Cloud status dashboard for known outages.

Official References

• Analytics API Conversation Details Query
• Java SDK Documentation
• OAuth 2.0 Client Credentials Flow
• Conversational AI Participant Data Structure