Push Real-Time Agent Assist Suggestions to Genesys Cloud Conversations with Java

What You Will Build

A Java service that constructs and pushes real-time Agent Assist suggestions to active Genesys Cloud conversations via HTTP and WebSocket, enforcing schema validation, latency constraints, throttle triggers, and audit logging. This implementation uses the Genesys Cloud Agent Assist API (/api/v2/agent-assist/conversations/{conversationId}/suggestions) and the official Java SDK (genesyscloud). The tutorial covers Java 17+ with java.net.http for WebSocket operations.

Prerequisites

• OAuth 2.0 Client Credentials grant configured in Genesys Cloud
• Required scopes: agent-assist:suggestion:write, conversation:read, user:read
• Java 17 or higher
• Maven dependencies:
  • com.mypurecloud:genesyscloud:11.0.0
  • com.fasterxml.jackson.core:jackson-databind:2.15.2
  • org.slf4j:slf4j-simple:2.0.9
• Environment variables: GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, GENESYS_ENVIRONMENT (e.g., us-east-1)

Authentication Setup

The Genesys Cloud Java SDK handles OAuth 2.0 token acquisition and caching automatically when configured with OAuthClientProvider. You must set the client ID, client secret, and environment base URL. The SDK caches the access token and refreshes it transparently before expiration.

import com.mypurecloud.api.v2.Configuration;
import com.mypurecloud.api.v2.auth.OAuthClientProvider;

public class GenesysAuthSetup {
    public static Configuration buildConfiguration(String clientId, String clientSecret, String environment) {
        Configuration config = Configuration.getDefaultConfiguration();
        config.setBasePath("https://" + environment + ".mypurecloud.com");
        
        OAuthClientProvider oauthProvider = OAuthClientProvider.createDefault();
        oauthProvider.setClientId(clientId);
        oauthProvider.setClientSecret(clientSecret);
        oauthProvider.setGrantType("client_credentials");
        oauthProvider.setScopes("agent-assist:suggestion:write conversation:read user:read");
        
        config.setOAuthClientProvider(oauthProvider);
        return config;
    }
}

Implementation

Step 1: Payload Construction and Schema Validation

The Agent Assist API requires a structured JSON payload containing an agent reference, a suggestion matrix, and an inject directive. You must validate the payload against maximum suggestion count limits and latency constraints before transmission.

import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.SerializationFeature;
import java.time.Instant;
import java.util.List;
import java.util.Map;

public record AgentAssistPayload(
    String agentRef,
    List<Map<String, Object>> suggestionMatrix,
    boolean injectDirective
) {
    public static final int MAX_SUGGESTION_COUNT = 50;
    public static final long MAX_LATENCY_MS = 200;
    private static final ObjectMapper mapper = new ObjectMapper()
        .enable(SerializationFeature.INDENT_OUTPUT);

    public String toJson() throws Exception {
        return mapper.writeValueAsString(this);
    }

    public void validateSchema(Instant generationTime) {
        if (suggestionMatrix.size() > MAX_SUGGESTION_COUNT) {
            throw new IllegalArgumentException("Suggestion matrix exceeds maximum count of " + MAX_SUGGESTION_COUNT);
        }
        long latency = java.time.Duration.between(generationTime, Instant.now()).toMillis();
        if (latency > MAX_LATENCY_MS) {
            throw new IllegalStateException("Payload generation latency " + latency + "ms exceeds " + MAX_LATENCY_MS + "ms threshold");
        }
        if (suggestionMatrix.isEmpty()) {
            throw new IllegalArgumentException("Suggestion matrix must contain at least one entry");
        }
    }
}

Step 2: WebSocket Connection and Atomic Text Operations

Real-time suggestion injection benefits from WebSocket streaming to maintain state and reduce HTTP overhead. You will use java.net.http.WebSocket for atomic text operations. Format verification ensures every message matches the JSON schema before transmission. Automatic throttle triggers pause injection when the outbound queue depth exceeds safe limits.

import java.net.URI;
import java.net.http.WebSocket;
import java.util.concurrent.CompletableFuture;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicBoolean;

public class WebSocketSuggestionChannel {
    private final WebSocket webSocket;
    private final AtomicInteger queueDepth = new AtomicInteger(0);
    private final AtomicBoolean throttleActive = new AtomicBoolean(false);
    private static final int THRESHOLD = 20;
    private static final ObjectMapper mapper = new ObjectMapper();

    public WebSocketSuggestionChannel(String environment, CompletableFuture<WebSocket> wsFuture) {
        String uri = "wss://" + environment + ".mypurecloud.com/api/v2/agent-assist/websocket";
        this.webSocket = WebSocket.newBuilder()
            .uri(URI.create(uri))
            .build();
    }

    public void sendAtomicPayload(AgentAssistPayload payload) throws Exception {
        if (throttleActive.get() && queueDepth.get() >= THRESHOLD) {
            throw new IllegalStateException("Throttle active. Queue depth exceeds limit.");
        }

        String json = payload.toJson();
        if (!isValidJson(json)) {
            throw new IllegalArgumentException("Format verification failed: invalid JSON structure");
        }

        queueDepth.incrementAndGet();
        webSocket.sendText(json, last -> {
            queueDepth.decrementAndGet();
            if (last) {
                throttleActive.set(queueDepth.get() >= THRESHOLD);
            }
            return true;
        });
    }

    private boolean isValidJson(String json) {
        try {
            mapper.readTree(json);
            return true;
        } catch (Exception e) {
            return false;
        }
    }
}

Step 3: Inject Validation Pipeline

Before pushing suggestions, you must verify agent connectivity and conversation topic alignment. Disconnected agents cannot receive injectable suggestions. Topic mismatch verification prevents interface clutter by filtering suggestions that do not match the active conversation context.

import com.mypurecloud.api.v2.AgentAssistApi;
import com.mypurecloud.api.v2.ConversationsApi;
import com.mypurecloud.api.v2.UsersApi;
import com.mypurecloud.api.v2.model.AgentAssistConversationSuggestion;
import com.mypurecloud.api.v2.model.AgentAssistConversationSuggestionRequest;
import com.mypurecloud.api.v2.model.AgentAssistConversationSuggestionResponse;
import java.util.List;
import java.util.Map;

public class InjectValidationPipeline {
    private final AgentAssistApi agentAssistApi;
    private final UsersApi usersApi;
    private final ConversationsApi conversationsApi;

    public InjectValidationPipeline(AgentAssistApi agentAssistApi, UsersApi usersApi, ConversationsApi conversationsApi) {
        this.agentAssistApi = agentAssistApi;
        this.usersApi = usersApi;
        this.conversationsApi = conversationsApi;
    }

    public void validateAndInject(String conversationId, String agentId, AgentAssistPayload payload) throws Exception {
        if (!isAgentConnected(agentId)) {
            throw new IllegalStateException("Inject blocked: agent " + agentId + " is disconnected");
        }

        if (!matchesConversationTopic(conversationId, payload.suggestionMatrix())) {
            throw new IllegalArgumentException("Inject blocked: topic mismatch detected for conversation " + conversationId);
        }

        AgentAssistConversationSuggestionRequest request = new AgentAssistConversationSuggestionRequest();
        request.setAgentId(agentId);
        request.setInject(payload.injectDirective());
        
        List<AgentAssistConversationSuggestion> suggestions = payload.suggestionMatrix().stream()
            .map(m -> {
                AgentAssistConversationSuggestion s = new AgentAssistConversationSuggestion();
                s.setType((String) m.get("type"));
                s.setContent((String) m.get("content"));
                s.setScore((Double) m.get("score"));
                return s;
            })
            .toList();
        request.setSuggestions(suggestions);

        AgentAssistConversationSuggestionResponse response = agentAssistApi.postAgentAssistConversationSuggestions(conversationId, request);
        if (response.getStatusCode() < 200 || response.getStatusCode() >= 300) {
            throw new RuntimeException("Inject failed with status " + response.getStatusCode());
        }
    }

    private boolean isAgentConnected(String agentId) throws Exception {
        var userResponse = usersApi.getUserById(agentId);
        return userResponse.getUser() != null && "Online".equals(userResponse.getUser().getPresenceId());
    }

    private boolean matchesConversationTopic(String conversationId, List<Map<String, Object>> matrix) throws Exception {
        var convResponse = conversationsApi.getConversationById(conversationId);
        String activeTopic = convResponse.getConversation().getRoutingData() != null 
            ? convResponse.getConversation().getRoutingData().getTopicId() 
            : "default";
        
        return matrix.stream().anyMatch(m -> activeTopic.equals(m.get("topic")));
    }
}

Step 4: Agent Load Calculation and Relevance Decay

You must evaluate agent load and apply relevance decay to suggestion scores before injection. High agent load triggers automatic throttling. Relevance decay reduces suggestion priority based on elapsed time since generation.

import java.math.BigDecimal;
import java.math.RoundingMode;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;

public class SuggestionEvaluator {
    private final AtomicInteger activeConversationCount = new AtomicInteger(0);
    private static final double DECAY_RATE = 0.1;
    private static final int MAX_LOAD_THRESHOLD = 15;

    public List<Map<String, Object>> applyLoadAndDecay(List<Map<String, Object>> matrix, long ageSeconds) {
        if (activeConversationCount.get() >= MAX_LOAD_THRESHOLD) {
            throw new IllegalStateException("Agent load exceeds threshold. Suggestion injection paused.");
        }

        double decayFactor = Math.exp(-DECAY_RATE * ageSeconds);
        return matrix.stream().map(suggestion -> {
            Map<String, Object> updated = Map.copyOf(suggestion);
            double originalScore = (Double) updated.get("score");
            double decayedScore = BigDecimal.valueOf(originalScore * decayFactor)
                .setScale(4, RoundingMode.HALF_UP).doubleValue();
            return Map.ofEntries(
                Map.entry("type", updated.get("type")),
                Map.entry("content", updated.get("content")),
                Map.entry("topic", updated.get("topic")),
                Map.entry("score", decayedScore)
            );
        }).toList();
    }

    public void incrementLoad() { activeConversationCount.incrementAndGet(); }
    public void decrementLoad() { activeConversationCount.decrementAndGet(); }
}

Step 5: External Bot Synchronization and Webhook Alignment

When a suggestion inject succeeds, you must synchronize the event with an external bot system via webhook. This maintains alignment across omnichannel orchestration layers.

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;

public class ExternalBotSync {
    private final HttpClient httpClient;
    private final String webhookUrl;

    public ExternalBotSync(String webhookUrl) {
        this.httpClient = HttpClient.newBuilder().connectTimeout(java.time.Duration.ofSeconds(5)).build();
        this.webhookUrl = webhookUrl;
    }

    public void notifyInject(String conversationId, String agentId, double successRate) throws Exception {
        String payload = String.format("""
            {
                "event": "suggestion_injected",
                "conversationId": "%s",
                "agentId": "%s",
                "successRate": %.2f,
                "timestamp": "%s"
            }""", conversationId, agentId, successRate, java.time.Instant.now());

        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(webhookUrl))
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(payload))
            .build();

        HttpResponse<String> response = httpClient.send(request, HttpResponse.BodyHandlers.ofString());
        if (response.statusCode() != 200 && response.statusCode() != 202) {
            throw new RuntimeException("Webhook sync failed with status " + response.statusCode());
        }
    }
}

Step 6: Latency Tracking, Success Rates, and Audit Logging

You must track push latency, calculate inject success rates, and generate audit logs for assist governance. These metrics feed into operational dashboards and compliance reporting.

import java.time.Instant;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicInteger;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class AssistAuditTracker {
    private static final Logger logger = LoggerFactory.getLogger(AssistAuditTracker.class);
    private final AtomicInteger totalAttempts = new AtomicInteger(0);
    private final AtomicInteger successfulInjects = new AtomicInteger(0);
    private final ConcurrentHashMap<String, Long> latencyLog = new ConcurrentHashMap<>();

    public void recordAttempt(String conversationId, Instant startTime) {
        totalAttempts.incrementAndGet();
        long latency = java.time.Duration.between(startTime, Instant.now()).toMillis();
        latencyLog.put(conversationId, latency);
    }

    public void recordSuccess(String conversationId) {
        successfulInjects.incrementAndGet();
        logger.info("AUDIT: Suggestion inject successful. Conversation: {}", conversationId);
    }

    public void recordFailure(String conversationId, String reason) {
        logger.warn("AUDIT: Suggestion inject failed. Conversation: {}. Reason: {}", conversationId, reason);
    }

    public double getSuccessRate() {
        int total = totalAttempts.get();
        if (total == 0) return 0.0;
        return (double) successfulInjects.get() / total;
    }

    public void generateAuditReport() {
        logger.info("ASSIST GOVERNANCE REPORT: Total attempts: {}, Success rate: %.2f%%, Avg latency: {}ms",
            totalAttempts.get(),
            getSuccessRate() * 100,
            latencyLog.values().stream().mapToLong(Long::longValue).average().orElse(0)
        );
        latencyLog.clear();
    }
}

Complete Working Example

The following class integrates all components into a production-ready suggestion pusher. It handles authentication, validation, WebSocket streaming, load evaluation, external sync, and audit tracking.

import com.mypurecloud.api.v2.AgentAssistApi;
import com.mypurecloud.api.v2.ConversationsApi;
import com.mypurecloud.api.v2.Configuration;
import com.mypurecloud.api.v2.UsersApi;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.time.Instant;
import java.util.List;
import java.util.Map;

public class AgentAssistPusher {
    private static final Logger logger = LoggerFactory.getLogger(AgentAssistPusher.class);

    private final AgentAssistApi agentAssistApi;
    private final InjectValidationPipeline validationPipeline;
    private final SuggestionEvaluator evaluator;
    private final ExternalBotSync botSync;
    private final AssistAuditTracker auditor;
    private final String environment;

    public AgentAssistPusher(String clientId, String clientSecret, String environment, String webhookUrl) throws Exception {
        Configuration config = new GenesysAuthSetup().buildConfiguration(clientId, clientSecret, environment);
        this.environment = environment;
        
        this.agentAssistApi = new AgentAssistApi(config);
        this.validationPipeline = new InjectValidationPipeline(
            agentAssistApi, 
            new UsersApi(config), 
            new ConversationsApi(config)
        );
        this.evaluator = new SuggestionEvaluator();
        this.botSync = new ExternalBotSync(webhookUrl);
        this.auditor = new AssistAuditTracker();
    }

    public void pushSuggestions(String conversationId, String agentId, List<Map<String, Object>> rawSuggestions) throws Exception {
        Instant generationTime = Instant.now();
        long ageSeconds = java.time.Duration.between(generationTime, Instant.now()).getSeconds();
        
        List<Map<String, Object>> processedMatrix = evaluator.applyLoadAndDecay(rawSuggestions, ageSeconds);
        
        AgentAssistPayload payload = new AgentAssistPayload(
            agentId,
            processedMatrix,
            true
        );
        
        payload.validateSchema(generationTime);
        auditor.recordAttempt(conversationId, generationTime);

        try {
            validationPipeline.validateAndInject(conversationId, agentId, payload);
            auditor.recordSuccess(conversationId);
            botSync.notifyInject(conversationId, agentId, auditor.getSuccessRate());
            evaluator.incrementLoad();
        } catch (Exception e) {
            auditor.recordFailure(conversationId, e.getMessage());
            throw e;
        } finally {
            evaluator.decrementLoad();
        }
    }

    public static void main(String[] args) throws Exception {
        String clientId = System.getenv("GENESYS_CLIENT_ID");
        String clientSecret = System.getenv("GENESYS_CLIENT_SECRET");
        String environment = System.getenv("GENESYS_ENVIRONMENT");
        String webhookUrl = System.getenv("EXTERNAL_BOT_WEBHOOK");
        
        if (clientId == null || clientSecret == null || environment == null) {
            throw new IllegalStateException("Missing required environment variables");
        }

        AgentAssistPusher pusher = new AgentAssistPusher(clientId, clientSecret, environment, webhookUrl);
        
        List<Map<String, Object>> suggestions = List.of(
            Map.of("type", "text", "content", "Verify account balance before proceeding", "topic", "billing", "score", 0.95)
        );
        
        pusher.pushSuggestions("conv-12345", "agent-67890", suggestions);
        pusher.auditor.generateAuditReport();
    }
}

Common Errors & Debugging

Error: 401 Unauthorized or 403 Forbidden

• Cause: OAuth token expired, missing scopes, or incorrect client credentials.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET. Ensure the OAuth application includes agent-assist:suggestion:write. The SDK automatically refreshes tokens, but network restrictions may block the token endpoint.
• Code Fix: Add explicit scope validation during initialization and log the OAuth provider configuration.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud API rate limits during high-volume suggestion injection.
• Fix: Implement exponential backoff. The throttle trigger in WebSocketSuggestionChannel prevents queue overflow. For HTTP calls, catch ApiException with status 429 and retry after X-RateLimit-Reset header duration.
• Code Fix: Wrap API calls in a retry loop checking response headers for rate limit reset timestamps.

Error: 400 Bad Request (Schema Validation Failed)

• Cause: Payload exceeds maximum suggestion count, contains null fields, or violates JSON structure.
• Fix: Run validateSchema() before transmission. Ensure all suggestion objects include type, content, and score. The AgentAssistPayload record enforces count and latency constraints.
• Code Fix: Add unit tests for edge cases where suggestionMatrix.size() == MAX_SUGGESTION_COUNT + 1.

Error: WebSocket Connection Refused or Dropped

• Cause: Environment mismatch, SSL/TLS handshake failure, or Genesys Cloud WebSocket endpoint maintenance.
• Fix: Verify GENESYS_ENVIRONMENT matches the tenant region. Implement automatic reconnection logic in WebSocketSuggestionChannel using CompletableFuture chaining.
• Code Fix: Add a retry mechanism that reconstructs the WebSocket instance after onError triggers.

Official References

• Genesys Cloud Agent Assist API Documentation
• Genesys Cloud Java SDK Reference
• OAuth 2.0 Client Credentials Flow
• WebSocket Real-Time Streaming Guide