Simulating Function Calling in NICE Cognigy.AI with a Java Spring Boot Webhook

What You Will Build

• This tutorial builds a Spring Boot REST webhook that receives LLM tool invocation JSON, executes mapped business logic, and pushes structured results back to Cognigy.AI to resume the dialog.
• The implementation uses the Cognigy.AI Dialog REST API (/api/v1/dialogs/{dialogId}/setVariables) for context updates and variable injection.
• The code is written in Java 17 with Spring Boot 3.2, Jackson for JSON parsing, and WebClient for outbound HTTP calls.

Prerequisites

• Cognigy.AI API key with dialog:write permission (equivalent to OAuth scope requirement for dialog mutations)
• Cognigy.AI Platform API v1
• Java 17 or later, Maven 3.8+, Spring Boot 3.2+
• Dependencies: spring-boot-starter-web, spring-boot-starter-webflux (for WebClient), jackson-databind
• Access to a target business microservice or mock endpoint for tool execution

Authentication Setup

Cognigy.AI secures the Dialog API using API keys passed as Bearer tokens. You must configure the key in your Spring Boot application and inject it into every outbound request. The token does not expire, but you must cache it in a secure configuration store and rotate it via the Cognigy.AI admin console.

Create application.yml:

cognigy:
  base-url: https://your-tenant.cognigy.ai
  api-key: ${COGNIGY_API_KEY:your-api-key-here}
  timeout-seconds: 10

Configure the WebClient bean with automatic Bearer injection and timeout settings:

import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.http.HttpHeaders;
import org.springframework.http.MediaType;
import org.springframework.web.reactive.function.client.WebClient;
import java.time.Duration;

@Configuration
public class CognigyConfig {

    @Value("${cognigy.base-url}")
    private String baseUrl;

    @Value("${cognigy.api-key}")
    private String apiKey;

    @Value("${cognigy.timeout-seconds:10}")
    private int timeoutSeconds;

    @Bean
    public WebClient cognigyWebClient(WebClient.Builder builder) {
        return builder
                .baseUrl(baseUrl)
                .defaultHeader(HttpHeaders.AUTHORIZATION, "Bearer " + apiKey)
                .defaultHeader(HttpHeaders.CONTENT_TYPE, MediaType.APPLICATION_JSON_VALUE)
                .defaultHeader(HttpHeaders.ACCEPT, MediaType.APPLICATION_JSON_VALUE)
                .codecs(configurer -> configurer.defaultCodecs().maxInMemorySize(10 * 1024 * 1024))
                .build();
    }
}

Implementation

Step 1: Parse LLM Tool Invocation JSON

The webhook receives a JSON payload containing an array of tool calls. Each call includes a tool name and argument map. You must define strict DTOs to prevent deserialization errors and validate the structure before execution.

import com.fasterxml.jackson.annotation.JsonProperty;
import java.util.List;
import java.util.Map;

public class LlmToolPayload {
    private List<ToolCall> toolCalls;
    private String dialogId;
    private String sessionId;

    // Getters and setters omitted for brevity but required for Jackson
    public List<ToolCall> getToolCalls() { return toolCalls; }
    public void setToolCalls(List<ToolCall> toolCalls) { this.toolCalls = toolCalls; }
    public String getDialogId() { return dialogId; }
    public void setDialogId(String dialogId) { this.dialogId = dialogId; }
    public String getSessionId() { return sessionId; }
    public void setSessionId(String sessionId) { this.sessionId = sessionId; }
}

public class ToolCall {
    private String name;
    private Map<String, Object> arguments;

    public String getName() { return name; }
    public void setName(String name) { this.name = name; }
    public Map<String, Object> getArguments() { return arguments; }
    public void setArguments(Map<String, Object> arguments) { this.arguments = arguments; }
}

Expected incoming request:

POST /webhook/cognigy-llm HTTP/1.1
Host: your-spring-app.example.com
Content-Type: application/json

{
  "dialogId": "dlg_8f3a2b1c9d",
  "sessionId": "sess_x7y8z9",
  "toolCalls": [
    {
      "name": "get_order_status",
      "arguments": {
        "orderId": "ORD-9921",
        "includeShipping": true
      }
    }
  ]
}

Step 2: Execute Business Logic Microservices

Map each tool name to a specific business logic handler. The handler executes the microservice call, transforms the response, and returns a structured result. You must handle network errors, timeouts, and malformed responses gracefully.

import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;
import org.springframework.web.reactive.function.client.WebClientResponseException;
import java.util.Map;
import java.util.concurrent.CompletableFuture;

@Service
public class ToolExecutorService {

    private final WebClient externalClient;

    public ToolExecutorService(WebClient.Builder builder) {
        this.externalClient = builder
                .baseUrl("https://internal-microservices.example.com")
                .build();
    }

    public Map<String, Object> executeTool(String toolName, Map<String, Object> arguments) {
        return switch (toolName) {
            case "get_order_status" -> fetchOrderStatus(arguments);
            case "check_inventory" -> checkInventory(arguments);
            default -> throw new IllegalArgumentException("Unknown tool: " + toolName);
        };
    }

    private Map<String, Object> fetchOrderStatus(Map<String, Object> args) {
        String orderId = (String) args.get("orderId");
        try {
            return externalClient.get()
                    .uri("/api/orders/{id}", orderId)
                    .retrieve()
                    .bodyToMono(Map.class)
                    .block();
        } catch (WebClientResponseException e) {
            throw new RuntimeException("Order service failed with status " + e.getStatusCode(), e);
        }
    }

    private Map<String, Object> checkInventory(Map<String, Object> args) {
        String sku = (String) args.get("sku");
        try {
            return externalClient.get()
                    .uri("/api/inventory/{sku}", sku)
                    .retrieve()
                    .bodyToMono(Map.class)
                    .block();
        } catch (WebClientResponseException e) {
            throw new RuntimeException("Inventory service failed with status " + e.getStatusCode(), e);
        }
    }
}

Step 3: Format Results and Update Cognigy Dialog Context

Cognigy.AI expects variable updates in a flat key-value structure. You must flatten nested tool results into Cognigy-compatible paths (e.g., sys.tools.result.get_order_status). After formatting, send the payload to the Dialog API. Implement retry logic for 429 Too Many Requests responses.

import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;
import org.springframework.web.reactive.function.client.WebClientResponseException;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

@Service
public class CognigyDialogService {

    private final WebClient cognigyClient;

    public CognigyDialogService(WebClient cognigyWebClient) {
        this.cognigyClient = cognigyWebClient;
    }

    public void updateDialogContext(String dialogId, List<Map<String, Object>> toolResults) {
        Map<String, Object> variables = new HashMap<>();
        
        // Flatten results into Cognigy variable schema
        for (Map<String, Object> result : toolResults) {
            String toolName = (String) result.get("toolName");
            Map<String, Object> payload = (Map<String, Object>) result.get("payload");
            String varPath = "sys.tools.result." + toolName;
            variables.put(varPath, payload);
        }

        Map<String, Object> requestBody = Map.of("variables", variables);
        
        // Retry logic for 429 rate limiting
        executeWithRetry(requestBody, dialogId);
    }

    private void executeWithRetry(Map<String, Object> requestBody, String dialogId) {
        int maxRetries = 3;
        long delayMs = 1000;
        
        for (int attempt = 1; attempt <= maxRetries; attempt++) {
            try {
                cognigyClient.post()
                        .uri("/api/v1/dialogs/{dialogId}/setVariables", dialogId)
                        .bodyValue(requestBody)
                        .retrieve()
                        .bodyToMono(String.class)
                        .block();
                return; // Success
            } catch (WebClientResponseException e) {
                if (e.getStatusCode().value() == 429 && attempt < maxRetries) {
                    try {
                        Thread.sleep(delayMs * attempt);
                    } catch (InterruptedException ex) {
                        Thread.currentThread().interrupt();
                        throw new RuntimeException("Retry interrupted", ex);
                    }
                } else {
                    throw new RuntimeException("Cognigy API failed after " + attempt + " attempts: " + e.getMessage(), e);
                }
            }
        }
    }
}

Expected HTTP exchange for Step 3:

POST /api/v1/dialogs/dlg_8f3a2b1c9d/setVariables HTTP/1.1
Host: your-tenant.cognigy.ai
Authorization: Bearer your-api-key-here
Content-Type: application/json
Accept: application/json

{
  "variables": {
    "sys.tools.result.get_order_status": {
      "status": "SHIPPED",
      "trackingNumber": "TRK123456",
      "estimatedDelivery": "2024-06-15"
    }
  }
}

Response:

HTTP/1.1 200 OK
Content-Type: application/json

{
  "success": true,
  "dialogId": "dlg_8f3a2b1c9d",
  "updatedVariables": ["sys.tools.result.get_order_status"]
}

Complete Working Example

The following module combines all components into a single runnable Spring Boot application. Replace placeholder values with your actual credentials and microservice URLs.

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class CognigyToolWebhookApplication {
    public static void main(String[] args) {
        SpringApplication.run(CognigyToolWebhookApplication.class, args);
    }
}

import com.fasterxml.jackson.databind.ObjectMapper;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

@RestController
@RequestMapping("/webhook")
public class LlmToolController {

    private final ToolExecutorService toolExecutor;
    private final CognigyDialogService cognigyDialogService;
    private final ObjectMapper objectMapper;

    public LlmToolController(ToolExecutorService toolExecutor, 
                             CognigyDialogService cognigyDialogService,
                             ObjectMapper objectMapper) {
        this.toolExecutor = toolExecutor;
        this.cognigyDialogService = cognigyDialogService;
        this.objectMapper = objectMapper;
    }

    @PostMapping("/cognigy-llm")
    public ResponseEntity<Map<String, Object>> handleToolInvocation(@RequestBody LlmToolPayload payload) {
        if (payload.getToolCalls() == null || payload.getToolCalls().isEmpty()) {
            return ResponseEntity.badRequest().body(Map.of("error", "Empty toolCalls array"));
        }

        String dialogId = payload.getDialogId();
        if (dialogId == null || dialogId.isBlank()) {
            return ResponseEntity.badRequest().body(Map.of("error", "Missing dialogId"));
        }

        List<Map<String, Object>> results = new ArrayList<>();

        for (var toolCall : payload.getToolCalls()) {
            try {
                Map<String, Object> executionResult = toolExecutor.executeTool(toolCall.getName(), toolCall.getArguments());
                Map<String, Object> wrappedResult = Map.of(
                        "toolName", toolCall.getName(),
                        "success", true,
                        "payload", executionResult
                );
                results.add(wrappedResult);
            } catch (Exception e) {
                // Capture failures without breaking the entire batch
                Map<String, Object> errorResult = Map.of(
                        "toolName", toolCall.getName(),
                        "success", false,
                        "error", e.getMessage()
                );
                results.add(errorResult);
            }
        }

        try {
            cognigyDialogService.updateDialogContext(dialogId, results);
        } catch (Exception e) {
            // Log and return partial success to avoid blocking the LLM flow
            System.err.println("Failed to update Cognigy context: " + e.getMessage());
            return ResponseEntity.status(502).body(Map.of("error", "Context update failed", "details", e.getMessage()));
        }

        return ResponseEntity.ok(Map.of(
                "status", "processed",
                "toolsExecuted", results.size(),
                "dialogId", dialogId
        ));
    }
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The Cognigy.AI API key is missing, malformed, or lacks the dialog:write permission.
• Fix: Verify the COGNIGY_API_KEY environment variable matches the value generated in the Cognigy.AI admin console. Ensure the key is assigned to a user or service account with dialog mutation permissions.
• Code Check: Confirm the Authorization: Bearer {key} header is attached before the request leaves the client.

Error: 403 Forbidden

• Cause: The API key has insufficient permissions or the dialog ID belongs to a different tenant/project.
• Fix: Assign the dialog:write scope/permission to the API key. Verify that dialogId matches the active session returned by your initial POST /api/v1/dialogs call.
• Debug Step: Call GET /api/v1/dialogs/{dialogId} first to validate access before attempting variable updates.

Error: 429 Too Many Requests

• Cause: Cognigy.AI enforces rate limits on dialog mutation endpoints. Rapid LLM turn completions can trigger cascading 429 responses.
• Fix: Implement exponential backoff. The provided executeWithRetry method handles this automatically. Adjust maxRetries and delayMs based on your tenant’s quota.
• Code Check: Ensure the retry loop does not swallow non-429 errors. The switch statement in the catch block isolates rate-limit responses.

Error: 502 Bad Gateway (Context Update Failed)

• Cause: The Cognigy.AI platform is temporarily unavailable or the request payload exceeds size limits.
• Fix: Reduce the size of tool results by truncating large arrays or omitting debug metadata. Add circuit breaker logic in production to fail fast when the platform is down.
• Debug Step: Inspect the requestBody map before sending. Cognigy.AI rejects payloads larger than 1MB. Serialize and measure JSON size before transmission.

Official References

• Cognigy.AI Dialog API Documentation
• Cognigy.AI Authentication & API Keys
• Spring Boot WebClient Reference
• NICE CXone/Cognigy Integration Architecture Guidelines