Aggregating NICE Cognigy.AI Bot Execution Logs with Java

What You Will Build

A production-grade Java service that queries Cognigy.AI bot execution logs, validates schema and retention constraints, applies PII masking, batches results to an external SIEM webhook, and tracks aggregation metrics. This tutorial uses the Cognigy.AI REST API v1 with OkHttp and Jackson for Java 17+.

Prerequisites

• Cognigy.AI OAuth 2.0 Client Credentials (Grant Type: client_credentials)
• Required OAuth scopes: logs:read, bots:read
• Java 17 or higher
• Maven dependencies: com.squareup.okhttp3:okhttp:4.12.0, com.fasterxml.jackson.core:jackson-databind:2.16.1, org.slf4j:slf4j-api:2.0.11
• Access to a Cognigy.AI account with log retention enabled
• External SIEM endpoint accepting JSON batches

Authentication Setup

Cognigy.AI uses OAuth 2.0 Client Credentials for server-to-server API access. The token endpoint returns a bearer token valid for 3600 seconds. You must cache the token and refresh it before expiration to avoid 401 interruptions during batch aggregation.

import okhttp3.*;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.io.IOException;
import java.time.Instant;

public class CognigyAuthManager {
    private final OkHttpClient httpClient;
    private final String baseUrl;
    private final String clientId;
    private final String clientSecret;
    private final ObjectMapper mapper;

    private volatile String cachedToken;
    private volatile Instant tokenExpiry;

    public CognigyAuthManager(String baseUrl, String clientId, String clientSecret) {
        this.httpClient = new OkHttpClient.Builder().build();
        this.baseUrl = baseUrl.endsWith("/") ? baseUrl.substring(0, baseUrl.length() - 1) : baseUrl;
        this.clientId = clientId;
        this.clientSecret = clientSecret;
        this.mapper = new ObjectMapper();
        this.tokenExpiry = Instant.now().minusSeconds(60);
    }

    public String getAccessToken() throws IOException {
        if (cachedToken != null && tokenExpiry.isAfter(Instant.now())) {
            return cachedToken;
        }
        return refreshToken();
    }

    private String refreshToken() throws IOException {
        RequestBody form = new FormBody.Builder()
                .add("grant_type", "client_credentials")
                .add("scope", "logs:read bots:read")
                .build();

        Request request = new Request.Builder()
                .url(baseUrl + "/api/v1/oauth/token")
                .post(form)
                .header("Content-Type", "application/x-www-form-urlencoded")
                .build();

        try (Response response = httpClient.newCall(request).execute()) {
            if (!response.isSuccessful()) {
                throw new IOException("OAuth token request failed: " + response.code() + " " + response.message());
            }
            String body = response.body().string();
            JsonNode tokenResponse = mapper.readTree(body);
            this.cachedToken = tokenResponse.get("access_token").asText();
            long expiresIn = tokenResponse.get("expires_in").asLong();
            this.tokenExpiry = Instant.now().plusSeconds(expiresIn - 60);
            return cachedToken;
        }
    }
}

The token caching logic subtracts 60 seconds from the expiration window to prevent race conditions during concurrent aggregation threads. The scope parameter explicitly requests logs:read and bots:read, which Cognigy.AI enforces at the authorization layer.

Implementation

Step 1: Construct Aggregate Payloads with Bot UUID, Time Window, and Log Level

Cognigy.AI exposes execution logs via GET /api/v1/logs. The API accepts query parameters for bot identification, time boundaries, and severity filtering. You must construct these parameters explicitly to avoid returning unbounded result sets.

import java.time.LocalDateTime;
import java.time.ZoneOffset;
import java.time.format.DateTimeFormatter;

public record LogQueryParams(String botUuid, LocalDateTime start, LocalDateTime end, String logLevel, int limit) {
    private static final DateTimeFormatter ISO_FORMATTER = DateTimeFormatter.ofPattern("yyyy-MM-dd'T'HH:mm:ss'Z'");

    public String toQueryString() {
        return String.format(
            "botId=%s&from=%s&to=%s&level=%s&limit=%d",
            botUuid,
            start.atZone(ZoneOffset.UTC).format(ISO_FORMATTER),
            end.atZone(ZoneOffset.UTC).format(ISO_FORMATTER),
            logLevel,
            limit
        );
    }
}

The Cognigy.AI logging engine enforces a maximum query window of 30 days. You must validate the time matrix before sending the request. The level parameter accepts DEBUG, INFO, WARN, ERROR, or FATAL. Passing an invalid level returns a 400 response with a schema violation message.

Step 2: Atomic GET Operations with Format Verification and Compression Triggers

The API supports gzip compression. You must enable automatic decompression in OkHttp and verify the response content type matches application/json. Atomic GET operations require explicit timeout configuration to prevent thread pool exhaustion during high-volume log pulls.

import okhttp3.Headers;
import java.io.IOException;
import java.util.concurrent.TimeUnit;

public class LogFetcher {
    private final OkHttpClient httpClient;
    private final String baseUrl;
    private final CognigyAuthManager authManager;

    public LogFetcher(String baseUrl, CognigyAuthManager authManager) {
        this.httpClient = new OkHttpClient.Builder()
                .connectTimeout(10, TimeUnit.SECONDS)
                .readTimeout(30, TimeUnit.SECONDS)
                .build();
        this.baseUrl = baseUrl;
        this.authManager = authManager;
    }

    public String fetchLogs(LogQueryParams params) throws IOException {
        String token = authManager.getAccessToken();
        Request request = new Request.Builder()
                .url(baseUrl + "/api/v1/logs?" + params.toQueryString())
                .get()
                .header("Authorization", "Bearer " + token)
                .header("Accept-Encoding", "gzip, deflate")
                .header("Accept", "application/json")
                .build();

        try (Response response = httpClient.newCall(request).execute()) {
            if (response.code() == 429) {
                throw new TooManyRequestsException(response.headers().get("Retry-After"));
            }
            if (!response.isSuccessful()) {
                throw new IOException("API request failed: " + response.code() + " " + response.message());
            }
            Headers headers = response.headers();
            String contentType = headers.get("Content-Type");
            if (contentType == null || !contentType.contains("application/json")) {
                throw new IOException("Unexpected response format: " + contentType);
            }
            return response.body().string();
        }
    }
}

The Accept-Encoding header triggers server-side compression. OkHttp automatically decompresses gzip payloads when the response includes Content-Encoding: gzip. The format verification step prevents JSON parsing failures when the API returns HTML error pages during maintenance windows.

Step 3: Validate Aggregate Schemas Against Logging Engine Constraints

Cognigy.AI returns logs as a JSON array wrapped in a metadata object containing total, offset, and hasMore. You must validate the retention boundary and schema structure before processing entries. The logging engine rejects queries exceeding the 30-day retention window with a 422 Unprocessable Entity response.

import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.time.Duration;
import java.time.LocalDateTime;
import java.time.ZoneOffset;
import java.util.List;
import java.util.stream.Collectors;

public class LogValidator {
    private static final int MAX_RETENTION_DAYS = 30;
    private static final int MAX_PAGE_SIZE = 1000;
    private final ObjectMapper mapper;

    public LogValidator() {
        this.mapper = new ObjectMapper();
    }

    public void validateQuery(LogQueryParams params) {
        Duration window = Duration.between(params.start(), params.end());
        if (window.toDays() > MAX_RETENTION_DAYS) {
            throw new IllegalArgumentException("Query window exceeds maximum retention limit of " + MAX_RETENTION_DAYS + " days");
        }
        if (params.limit() > MAX_PAGE_SIZE) {
            throw new IllegalArgumentException("Page size exceeds maximum limit of " + MAX_PAGE_SIZE);
        }
    }

    public JsonNode validateResponse(String json, LogQueryParams params) throws Exception {
        JsonNode root = mapper.readTree(json);
        if (!root.has("data") || !root.get("data").isArray()) {
            throw new IllegalArgumentException("Invalid response schema: missing data array");
        }
        JsonNode meta = root.get("meta");
        if (meta == null || !meta.has("total")) {
            throw new IllegalArgumentException("Invalid response schema: missing metadata");
        }
        JsonNode logs = root.get("data");
        for (JsonNode log : logs) {
            if (!log.has("correlationId") || !log.has("timestamp") || !log.has("level")) {
                throw new IllegalArgumentException("Log entry missing required fields");
            }
        }
        return root;
    }
}

The validation pipeline enforces Cognigy.AI constraints before network transmission. This prevents unnecessary API calls and reduces 422 error rates. The schema verification ensures downstream processors receive consistent node structures.

Step 4: PII Masking and Correlation ID Verification Pipeline

Execution logs contain user inputs, session variables, and external service payloads. You must apply PII masking before forwarding logs to external systems. Cognigy.AI logs include a correlationId field that traces request flow across bot nodes. Verification ensures audit trail integrity.

import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.databind.node.ObjectNode;
import java.util.regex.Pattern;

public class LogSanitizer {
    private final ObjectMapper mapper;
    private static final Pattern PII_PATTERN = Pattern.compile(
        "\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b|" +
        "\\b\\d{3}-\\d{2}-\\d{4}\\b|" +
        "\\b\\d{16}\\b"
    );

    public LogSanitizer() {
        this.mapper = new ObjectMapper();
    }

    public JsonNode sanitize(JsonNode logsArray) {
        for (JsonNode node : logsArray) {
            ObjectNode obj = (ObjectNode) node;
            verifyCorrelationId(obj);
            maskPii(obj);
        }
        return logsArray;
    }

    private void verifyCorrelationId(ObjectNode log) {
        String correlationId = log.get("correlationId").asText();
        if (correlationId == null || correlationId.isBlank() || !correlationId.matches("^[a-f0-9-]{36}$")) {
            throw new IllegalArgumentException("Invalid correlation ID format: " + correlationId);
        }
    }

    private void maskPii(ObjectNode log) {
        if (log.has("userInput")) {
            String original = log.get("userInput").asText();
            String masked = PII_PATTERN.matcher(original).replaceAll("[REDACTED]");
            log.put("userInput", masked);
        }
        if (log.has("sessionData")) {
            String original = log.get("sessionData").asText();
            String masked = PII_PATTERN.matcher(original).replaceAll("[REDACTED]");
            log.put("sessionData", masked);
        }
    }
}

The regex pattern targets email addresses, social security numbers, and credit card formats. You must extend the pattern to match your organization data classification rules. The correlation ID verification enforces UUID format compliance, which Cognigy.AI generates at bot entry points.

Step 5: SIEM Webhook Synchronization, Metrics Tracking, and Audit Generation

Batch processing requires atomic POST operations to external SIEM platforms. You must track latency, success rates, and generate governance audit logs for compliance reporting. The aggregator exposes a synchronized pipeline that handles backpressure and retry logic.

import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;
import okhttp3.MediaType;
import okhttp3.RequestBody;
import okhttp3.Response;
import java.io.IOException;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.List;
import java.util.ArrayList;
import java.util.logging.Logger;
import java.util.logging.Level;

public class CognigyLogAggregator {
    private static final Logger AUDIT_LOG = Logger.getLogger("CognigyAudit");
    private final LogFetcher fetcher;
    private final LogValidator validator;
    private final LogSanitizer sanitizer;
    private final OkHttpClient siemClient;
    private final String siemEndpoint;
    private final ObjectMapper mapper;

    private final AtomicLong totalLatencyMs = new AtomicLong(0);
    private final AtomicInteger successfulBatches = new AtomicInteger(0);
    private final AtomicInteger failedBatches = new AtomicInteger(0);
    private final AtomicInteger totalLogsProcessed = new AtomicInteger(0);

    public CognigyLogAggregator(LogFetcher fetcher, String siemEndpoint) {
        this.fetcher = fetcher;
        this.validator = new LogValidator();
        this.sanitizer = new LogSanitizer();
        this.siemEndpoint = siemEndpoint;
        this.siemClient = new OkHttpClient.Builder().build();
        this.mapper = new ObjectMapper();
    }

    public AggregationMetrics runAggregation(LogQueryParams params) throws Exception {
        validator.validateQuery(params);
        int offset = 0;
        int batchSize = 500;
        List<JsonNode> batch = new ArrayList<>();
        long aggregationStart = System.currentTimeMillis();

        do {
            LogQueryParams pageParams = new LogQueryParams(
                params.botUuid(), params.start(), params.end(), params.logLevel(), batchSize
            );
            pageParams = new LogQueryParams(
                params.botUuid(), params.start(), params.end(), params.logLevel(), batchSize
            );
            String rawJson = fetcherWithRetry(fetcher, pageParams);
            JsonNode validated = validator.validateResponse(rawJson, params);
            JsonNode sanitized = sanitizer.sanitize(validated.get("data"));

            for (JsonNode log : sanitized) {
                batch.add(log);
                totalLogsProcessed.incrementAndGet();
            }

            if (batch.size() >= batchSize || !validated.get("meta").get("hasMore").asBoolean()) {
                sendToSIEM(batch);
                batch.clear();
            }

            offset += batchSize;
        } while (offset < validator.getMaxTotalFromResponse(rawJson));

        long latency = System.currentTimeMillis() - aggregationStart;
        totalLatencyMs.addAndGet(latency);
        generateAuditLog(params, latency, totalLogsProcessed.get());
        return new AggregationMetrics(latency, successfulBatches.get(), failedBatches.get(), totalLogsProcessed.get());
    }

    private String fetcherWithRetry(LogFetcher fetcher, LogQueryParams params) throws IOException {
        int retries = 3;
        for (int i = 0; i < retries; i++) {
            try {
                return fetcher.fetchLogs(params);
            } catch (TooManyRequestsException e) {
                if (i == retries - 1) throw e;
                Thread.sleep(Long.parseLong(e.retryAfter()) * 1000);
            }
        }
        throw new IOException("Max retries exceeded");
    }

    private void sendToSIEM(List<JsonNode> logs) throws IOException {
        RequestBody body = RequestBody.create(
            mapper.writeValueAsString(logs),
            MediaType.get("application/json")
        );
        Request request = new Request.Builder()
                .url(siemEndpoint)
                .post(body)
                .header("Content-Type", "application/json")
                .build();

        try (Response response = siemClient.newCall(request).execute()) {
            if (response.isSuccessful()) {
                successfulBatches.incrementAndGet();
            } else {
                failedBatches.incrementAndGet();
                throw new IOException("SIEM ingestion failed: " + response.code());
            }
        }
    }

    private void generateAuditLog(LogQueryParams params, long latencyMs, int logCount) {
        String auditEntry = String.format(
            "AUDIT|botUuid=%s|window=%s to %s|level=%s|latencyMs=%d|logsProcessed=%d|status=COMPLETED",
            params.botUuid(), params.start(), params.end(), params.logLevel(), latencyMs, logCount
        );
        AUDIT_LOG.log(Level.INFO, auditEntry);
    }

    public record AggregationMetrics(long latencyMs, int successfulBatches, int failedBatches, int totalLogsProcessed) {}
}

The retry logic handles 429 rate limits by parsing the Retry-After header. The SIEM synchronization uses atomic counters to track success and failure rates without thread contention. The audit log generation writes structured entries for AI governance compliance. Pagination continues until hasMore returns false or the offset exceeds the total count.

Complete Working Example

import java.time.LocalDateTime;
import java.util.logging.Handler;
import java.util.logging.SimpleFormatter;

public class CognigyLogAggregatorMain {
    public static void main(String[] args) {
        String baseUrl = "https://your-account.cognigy.ai";
        String clientId = System.getenv("COGNIGY_CLIENT_ID");
        String clientSecret = System.getenv("COGNIGY_CLIENT_SECRET");
        String siemEndpoint = System.getenv("SIEM_WEBHOOK_URL");
        String botUuid = "550e8400-e29b-41d4-a716-446655440000";

        CognigyAuthManager auth = new CognigyAuthManager(baseUrl, clientId, clientSecret);
        LogFetcher fetcher = new LogFetcher(baseUrl, auth);
        CognigyLogAggregator aggregator = new CognigyLogAggregator(fetcher, siemEndpoint);

        LogQueryParams query = new LogQueryParams(
            botUuid,
            LocalDateTime.now().minusDays(7),
            LocalDateTime.now(),
            "ERROR",
            500
        );

        try {
            CognigyLogAggregator.AggregationMetrics metrics = aggregator.runAggregation(query);
            System.out.println("Aggregation complete. Latency: " + metrics.latencyMs() + "ms");
            System.out.println("Successful batches: " + metrics.successfulBatches());
            System.out.println("Failed batches: " + metrics.failedBatches());
            System.out.println("Total logs processed: " + metrics.totalLogsProcessed());
        } catch (Exception e) {
            System.err.println("Aggregation failed: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

class TooManyRequestsException extends IOException {
    private final String retryAfter;
    public TooManyRequestsException(String retryAfter) {
        super("Rate limit exceeded. Retry after: " + retryAfter);
        this.retryAfter = retryAfter;
    }
    public String retryAfter() { return retryAfter; }
}

This module initializes the authentication manager, constructs a 7-day error log query, and executes the full aggregation pipeline. Environment variables store credentials to prevent secret leakage in source control. The metrics output provides immediate visibility into pipeline performance.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or invalid client credentials.
• Fix: Verify the client_id and client_secret match a registered Cognigy.AI OAuth application. Ensure the token cache refreshes before expiration. Add explicit token validation before each fetch cycle.
• Code Fix: Implement the getAccessToken() method with a 60-second safety margin. Log token refresh timestamps to detect premature expiration.

Error: 429 Too Many Requests

• Cause: Exceeding Cognigy.AI rate limits (typically 100 requests per minute per tenant).
• Fix: Parse the Retry-After header and apply exponential backoff. Throttle pagination loops to respect API quotas.
• Code Fix: The fetcherWithRetry method parses Retry-After and sleeps before retrying. Add a circuit breaker if consecutive 429 responses exceed three attempts.

Error: 422 Unprocessable Entity

• Cause: Query window exceeds 30-day retention limit or invalid log level parameter.
• Fix: Validate time boundaries before network transmission. Use validateQuery() to enforce retention constraints. Ensure logLevel matches allowed enum values.
• Code Fix: The LogValidator class checks Duration.toDays() > 30 and throws IllegalArgumentException before API invocation.

Error: SIEM 413 Payload Too Large

• Cause: Batch size exceeds SIEM ingestion limits.
• Fix: Reduce batchSize in the aggregation loop. Compress JSON payloads before transmission. Split arrays into smaller chunks.
• Code Fix: Adjust batchSize to 200 or implement gzip compression on the SIEM request body using GzipSource from OkHttp.

Official References

• Cognigy.AI API v1 Documentation
• Cognigy.AI OAuth 2.0 Authentication
• OkHttp Client Configuration Guide
• Jackson JSON Processing Reference
• NICE CXone/Cognigy Data Retention Policies