AI-Powered Multi-Agent Banking Assistant

Anand Nerurkar
Sep 14
11 min read

💼 Scenario: AI-Powered Multi-Agent Banking Assistant

🧩 Use Case

A customer wants to:

Understand their loan eligibility
Get KYC updated
Learn about investment products
Raise a dispute for a transaction

Each task is handled by a specialized AI agent, collaborating to complete the user's end goal.

🤖 Agents Involved

Agent	Responsibility
KYC Agent	Verifies documents, updates KYC
Loan Agent	Checks eligibility, recommends loans
Investment Agent	Suggests financial products
Dispute Agent	Initiates dispute and tracks status
Coordinator Agent	Orchestrates flow between other agents

🔄 Interaction Flow

User Input → Spring Boot API → Coordinator Agent
                           ↘ Calls:
                             - Loan Agent
                             - KYC Agent
                             - Investment Agent
                             - Dispute Agent
                           ↘ Aggregated Response → User

Each agent:

Uses tools like BankAPIWrapper, VectorSearchTool, or FormFillerTool
May delegate subtasks to another agent (e.g., Dispute Agent → KYC Agent for verification)
Talks through a message bus or task chain, either via LangChain agents or custom logic

🧠 Architecture Diagram Update (Conceptual)

+-------------------+
|   Frontend (UI)   |
+-------------------+
          |
          v
+-------------------+
| Spring Boot API   |
+-------------------+
          |
          v
+------------------------+
|   Coordinator Agent    |
+------------------------+
    |     |     |     |
    v     v     v     v
+------+ +------+ +--------+ +-----------+
| KYC  | | Loan | | Invest | | Dispute   |
|Agent | |Agent | |Agent   | |Agent      |
+------+ +------+ +--------+ +-----------+
     \         |         /
      \        |        /
       \       v       /
      +------------------+
      | RAG + Core APIs  |
      +------------------+

📌 Technologies to Enable This

Spring Boot – API Gateway & orchestration
LangChain or CrewAI – Multi-agent support (roles, tools, communication)
OpenAI / Azure OpenAI – GenAI backend
Redis or Message Bus – Agent messaging/state sharing
Pinecone / FAISS – RAG-based context grounding

🧪 Example Task

Prompt: “I want to update my KYC and check if I’m eligible for a home loan.”

Coordinator Agent triggers:
- KYC Agent → Verifies via OCR + fetches user docs
- Loan Agent → Calls Core Banking APIs to evaluate eligibility
- Returns combined response like:"Your KYC is now verified. Based on your profile, you're eligible for a ₹45L home loan. Want to proceed?"

Here's a sample structure with Spring Boot service classes for each AI Agent involved in your multi-agent banking system. Each agent is designed as a Spring service and follows a clean separation of concerns.

📁 Project Structure (Agent Services)

src/main/java/com/bankgenai/agents
│
├── controller/
│   └── AssistantController.java
│
├── service/
│   ├── CoordinatorAgentService.java
│   ├── KycAgentService.java
│   ├── LoanAgentService.java
│   ├── InvestmentAgentService.java
│   └── DisputeAgentService.java
│
└── model/
    ├── AgentRequest.java
    └── AgentResponse.java

📦 model/AgentRequest.java

public class AgentRequest {
    private String userId;
    private String query;
    // Additional metadata if needed
}

📦 model/AgentResponse.java

public class AgentResponse {
    private String agentName;
    private String response;
}

🤖 KycAgentService.java

@Service
public class KycAgentService {
    public AgentResponse handleKyc(String userId) {
        // Simulate OCR/KYC update logic
        String result = "KYC verified successfully for user " + userId;
        return new AgentResponse("KYC_AGENT", result);
    }
}

🤖 LoanAgentService.java

@Service
public class LoanAgentService {
    public AgentResponse evaluateLoan(String userId) {
        // Simulate loan eligibility logic
        String loanInfo = "Eligible for a ₹45L home loan based on income.";
        return new AgentResponse("LOAN_AGENT", loanInfo);
    }
}

🤖 InvestmentAgentService.java

@Service
public class InvestmentAgentService {
    public AgentResponse suggestInvestments(String userId) {
        String suggestion = "Recommended: SIP in large cap mutual funds.";
        return new AgentResponse("INVESTMENT_AGENT", suggestion);
    }
}

🤖 DisputeAgentService.java

@Service
public class DisputeAgentService {
    public AgentResponse handleDispute(String userId, String transactionId) {
        String response = "Dispute raised for transaction ID " + transactionId;
        return new AgentResponse("DISPUTE_AGENT", response);
    }
}

🤝 CoordinatorAgentService.java

@Service
@RequiredArgsConstructor
public class CoordinatorAgentService {

    private final KycAgentService kycAgent;
    private final LoanAgentService loanAgent;
    private final InvestmentAgentService investmentAgent;
    private final DisputeAgentService disputeAgent;

    public List<AgentResponse> processUserQuery(String userId, String query) {
        List<AgentResponse> responses = new ArrayList<>();

        if (query.toLowerCase().contains("kyc")) {
            responses.add(kycAgent.handleKyc(userId));
        }
        if (query.toLowerCase().contains("loan")) {
            responses.add(loanAgent.evaluateLoan(userId));
        }
        if (query.toLowerCase().contains("invest")) {
            responses.add(investmentAgent.suggestInvestments(userId));
        }
        if (query.toLowerCase().contains("dispute")) {
            // extract transaction ID in real implementation
            responses.add(disputeAgent.handleDispute(userId, "TXN12345"));
        }

        return responses;
    }
}

🌐 AssistantController.java

@RestController
@RequestMapping("/api/assistant")
@RequiredArgsConstructor
public class AssistantController {

    private final CoordinatorAgentService coordinatorAgent;

    @PostMapping("/ask")
    public ResponseEntity<List<AgentResponse>> handleQuery(@RequestBody AgentRequest request) {
        List<AgentResponse> results = coordinatorAgent.processUserQuery(request.getUserId(), request.getQuery());
        return ResponseEntity.ok(results);
    }
}

gitHub

// File: src/main/java/com/bankgenai/model/AgentRequest.java

package com.bankgenai.model;

public class AgentRequest {

private String userId;

private String query;

public String getUserId() { return userId; }

public void setUserId(String userId) { this.userId = userId; }

public String getQuery() { return query; }

public void setQuery(String query) { this.query = query; }

}

// File: src/main/java/com/bankgenai/model/AgentResponse.java

package com.bankgenai.model;

public class AgentResponse {

private String agentName;

private String response;

public AgentResponse(String agentName, String response) {

this.agentName = agentName;

this.response = response;

}

public String getAgentName() { return agentName; }

public String getResponse() { return response; }

}

// File: src/main/java/com/bankgenai/service/KycAgentService.java

package com.bankgenai.service;

import com.bankgenai.model.AgentResponse;

import org.springframework.stereotype.Service;

@Service

public class KycAgentService {

public AgentResponse handleKyc(String userId) {

String result = "KYC verified successfully for user " + userId;

return new AgentResponse("KYC_AGENT", result);

}

// File: src/main/java/com/bankgenai/service/LoanAgentService.java

package com.bankgenai.service;

import com.bankgenai.model.AgentResponse;

import org.springframework.stereotype.Service;

@Service

public class LoanAgentService {

public AgentResponse evaluateLoan(String userId) {

String loanInfo = "Eligible for a ₹45L home loan based on income.";

return new AgentResponse("LOAN_AGENT", loanInfo);

}

// File: src/main/java/com/bankgenai/service/InvestmentAgentService.java

package com.bankgenai.service;

import com.bankgenai.model.AgentResponse;

import org.springframework.stereotype.Service;

@Service

public class InvestmentAgentService {

public AgentResponse suggestInvestments(String userId) {

String suggestion = "Recommended: SIP in large cap mutual funds.";

return new AgentResponse("INVESTMENT_AGENT", suggestion);

}

// File: src/main/java/com/bankgenai/service/DisputeAgentService.java

package com.bankgenai.service;

import com.bankgenai.model.AgentResponse;

import org.springframework.stereotype.Service;

@Service

public class DisputeAgentService {

public AgentResponse handleDispute(String userId, String transactionId) {

String response = "Dispute raised for transaction ID " + transactionId;

return new AgentResponse("DISPUTE_AGENT", response);

}

// File: src/main/java/com/bankgenai/service/CoordinatorAgentService.java

package com.bankgenai.service;

import com.bankgenai.model.AgentResponse;

import lombok.RequiredArgsConstructor;

import org.springframework.stereotype.Service;

import java.util.ArrayList;

import java.util.List;

@Service

@RequiredArgsConstructor

public class CoordinatorAgentService {

private final KycAgentService kycAgent;

private final LoanAgentService loanAgent;

private final InvestmentAgentService investmentAgent;

private final DisputeAgentService disputeAgent;

public List<AgentResponse> processUserQuery(String userId, String query) {

List<AgentResponse> responses = new ArrayList<>();

if (query.toLowerCase().contains("kyc")) {

responses.add(kycAgent.handleKyc(userId));

}

if (query.toLowerCase().contains("loan")) {

responses.add(loanAgent.evaluateLoan(userId));

}

if (query.toLowerCase().contains("invest")) {

responses.add(investmentAgent.suggestInvestments(userId));

}

if (query.toLowerCase().contains("dispute")) {

responses.add(disputeAgent.handleDispute(userId, "TXN12345"));

}

return responses;

}

// File: src/main/java/com/bankgenai/controller/AssistantController.java

package com.bankgenai.controller;

import com.bankgenai.model.AgentRequest;

import com.bankgenai.model.AgentResponse;

import com.bankgenai.service.CoordinatorAgentService;

import io.swagger.v3.oas.annotations.Operation;

import io.swagger.v3.oas.annotations.media.Content;

import io.swagger.v3.oas.annotations.media.Schema;

import io.swagger.v3.oas.annotations.parameters.RequestBody;

import io.swagger.v3.oas.annotations.responses.ApiResponse;

import io.swagger.v3.oas.annotations.responses.ApiResponses;

import lombok.RequiredArgsConstructor;

import org.springframework.http.ResponseEntity;

import org.springframework.web.bind.annotation.*;

import java.util.List;

@RestController

@RequestMapping("/api/assistant")

@RequiredArgsConstructor

public class AssistantController {

private final CoordinatorAgentService coordinatorAgent;

@Operation(summary = "Process user query using multi-agent AI system")

@ApiResponses(value = {

@ApiResponse(responseCode = "200", description = "Successful response from AI agents",

content = @Content(mediaType = "application/json",

schema = @Schema(implementation = AgentResponse.class)))

})

@PostMapping("/ask")

public ResponseEntity<List<AgentResponse>> handleQuery(

@io.swagger.v3.oas.annotations.parameters.RequestBody(description = "User input for AI assistant",

required = true,

content = @Content(schema = @Schema(implementation = AgentRequest.class)))

@RequestBody AgentRequest request) {

List<AgentResponse> results = coordinatorAgent.processUserQuery(request.getUserId(), request.getQuery());

return ResponseEntity.ok(results);

}

✅ Swagger/OpenAPI annotations have been added to the AssistantController. You can now auto-generate API docs using Springdoc or Swagger UI.

To activate it in your Spring Boot project, add this dependency to pom.xml:

<dependency>
  <groupId>org.springdoc</groupId>
  <artifactId>springdoc-openapi-starter-webmvc-ui</artifactId>
  <version>2.2.0</version>
</dependency>

Once added, your API docs will be available at:👉 http://localhost:8080/swagger-ui.html

External API connectors or LangChain agent logic

===

1. Adding External API Connectors

For example, let's connect to a third-party KYC API for verification or an external loan evaluation API.

We'll use RestTemplate or WebClient (for non-blocking calls) to make API requests.

📦 ExternalApiService.java

This service will integrate with external KYC and Loan APIs.

package com.bankgenai.service;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;
import org.springframework.web.reactive.function.client.WebClient;

@Service
public class ExternalApiService {

    @Value("${external.api.kyc.url}")
    private String kycApiUrl;

    @Value("${external.api.loan.url}")
    private String loanApiUrl;

    private final RestTemplate restTemplate;
    private final WebClient webClient;

    public ExternalApiService(RestTemplate restTemplate, WebClient.Builder webClientBuilder) {
        this.restTemplate = restTemplate;
        this.webClient = webClientBuilder.baseUrl("http://external-api.com").build();
    }

    // KYC verification through external API
    public String verifyKyc(String userId) {
        String response = restTemplate.getForObject(kycApiUrl + "/verify/" + userId, String.class);
        return response;
    }

    // Loan eligibility check via external API
    public String checkLoanEligibility(String userId) {
        return webClient.get()
                .uri(loanApiUrl + "/loan-check/" + userId)
                .retrieve()
                .bodyToMono(String.class)
                .block();
    }
}

📦 Application Properties (application.yml)

external:
  api:
    kyc:
      url: "https://external-kyc-api.com"
    loan:
      url: "https://external-loan-api.com"

2. Adding LangChain Logic

====

LangChain is a framework designed for building LLM-powered applications. We can integrate it to create intelligent agents that can interact with various APIs and process tasks like loan evaluation, dispute resolution, etc.

To add LangChain-like behavior in Java, we need to integrate a LangChain-like service that processes requests via LLMs, like OpenAI or Anthropic, while incorporating API calls. Here’s a general pattern:

📦 LangChainService.java

Here we define a LangChain Agent to process customer queries with external APIs:

package com.bankgenai.service;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;

@Service
public class LangChainService {

    private final ExternalApiService externalApiService;
    private final RestTemplate restTemplate;

    public LangChainService(ExternalApiService externalApiService, RestTemplate restTemplate) {
        this.externalApiService = externalApiService;
        this.restTemplate = restTemplate;
    }

    // LangChain-like agent logic
    public String processCustomerQuery(String userId, String query) {
        if (query.toLowerCase().contains("kyc")) {
            return externalApiService.verifyKyc(userId);
        } else if (query.toLowerCase().contains("loan")) {
            return externalApiService.checkLoanEligibility(userId);
        }
        return "Sorry, I cannot process this request.";
    }
}

3. Integrating LangChain with Spring Boot Controller

Now, integrate the LangChainService with the existing controller logic.

package com.bankgenai.controller;

import com.bankgenai.service.LangChainService;
import lombok.RequiredArgsConstructor;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/api/assistant")
@RequiredArgsConstructor
public class AssistantController {

    private final LangChainService langChainService;

    @PostMapping("/ask")
    public String handleQuery(@RequestBody String query) {
        return langChainService.processCustomerQuery("user123", query);
    }
}

4. Handling Long-running API Calls with Async Logic (Optional)

For better performance, especially with long-running calls, you can use asynchronous APIs. For example, make API calls with @Async annotations and CompletableFuture in Spring Boot.

Example of an asynchronous call in the ExternalApiService.java:

import org.springframework.scheduling.annotation.Async;
import java.util.concurrent.CompletableFuture;

@Service
public class ExternalApiService {

    @Async
    public CompletableFuture<String> verifyKycAsync(String userId) {
        String response = restTemplate.getForObject(kycApiUrl + "/verify/" + userId, String.class);
        return CompletableFuture.completedFuture(response);
    }
}

5. Dockerize the Application (Optional)

To run this service in a containerized environment, you'll need a Dockerfile.

# Use OpenJDK 17 image for the Java application
FROM openjdk:17-jdk-alpine

# Set the working directory
WORKDIR /app

# Copy the compiled jar file
COPY target/banking-genai-application.jar /app/banking-genai-application.jar

# Expose the port
EXPOSE 8080

# Run the application
CMD ["java", "-jar", "banking-genai-application.jar"]

1. Setting up OpenAI API (LangChain Simulation)

You can interact with OpenAI's GPT models (or any other LLM like Anthropic) in Java. First, ensure you have access to the OpenAI API.

Step 1: Add OpenAI Dependency

You need to use a HTTP client like OkHttp or WebClient to interact with OpenAI. Here’s how you can set it up using WebClient in Spring Boot.

Add dependencies in your pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-webflux</artifactId>
</dependency>
<dependency>
    <groupId>com.squareup.okhttp3</groupId>
    <artifactId>okhttp</artifactId>
    <version>4.10.0</version>
</dependency>

Step 2: OpenAI API Service

This service communicates with OpenAI's API for generating responses based on user queries.

package com.bankgenai.service;

import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;

@Service
public class OpenAIService {

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

    private final WebClient webClient;

    public OpenAIService(WebClient.Builder webClientBuilder) {
        this.webClient = webClientBuilder.baseUrl("https://api.openai.com/v1/completions")
                .defaultHeader("Authorization", "Bearer " + apiKey)
                .build();
    }

    // Call OpenAI API to generate a response based on the query
    public String generateResponse(String prompt) {
        String response = this.webClient.post()
                .bodyValue("{\"model\":\"text-davinci-003\",\"prompt\":\"" + prompt + "\",\"max_tokens\":150}")
                .retrieve()
                .bodyToMono(String.class)
                .block();

        return response;
    }
}

Step 3: Integrating with LangChain-like Logic

Now, let's integrate this service into your existing multi-agent setup.

package com.bankgenai.service;

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class LangChainService {

    private final OpenAIService openAIService;
    private final ExternalApiService externalApiService;

    @Autowired
    public LangChainService(OpenAIService openAIService, ExternalApiService externalApiService) {
        this.openAIService = openAIService;
        this.externalApiService = externalApiService;
    }

    // Simulate LangChain agent logic: Handle both OpenAI and external API calls
    public String processCustomerQuery(String userId, String query) {
        if (query.toLowerCase().contains("kyc")) {
            return externalApiService.verifyKyc(userId);
        } else if (query.toLowerCase().contains("loan")) {
            return externalApiService.checkLoanEligibility(userId);
        } else {
            // Default LangChain-like response generation
            return openAIService.generateResponse(query);
        }
    }
}

2. Connecting to External APIs (e.g., KYC, Loan Evaluation)

Now that we’ve integrated LangChain-like logic using OpenAI, let’s connect it to external APIs for services like KYC verification or Loan evaluation.

We'll create services to connect to third-party APIs.

Example: KYC API Integration

package com.bankgenai.service;

import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;

@Service
public class KycApiService {

    private final String kycApiUrl = "https://external-kyc-api.com/verify/";

    private final RestTemplate restTemplate;

    public KycApiService(RestTemplate restTemplate) {
        this.restTemplate = restTemplate;
    }

    public String verifyKyc(String userId) {
        return restTemplate.getForObject(kycApiUrl + userId, String.class);
    }
}

Example: Loan Eligibility API Integration

package com.bankgenai.service;

import org.springframework.stereotype.Service;
import org.springframework.web.reactive.function.client.WebClient;

@Service
public class LoanApiService {

    private final String loanApiUrl = "https://external-loan-api.com/loan-check/";

    private final WebClient webClient;

    public LoanApiService(WebClient.Builder webClientBuilder) {
        this.webClient = webClientBuilder.baseUrl(loanApiUrl).build();
    }

    public String checkLoanEligibility(String userId) {
        return webClient.get()
                .uri(uriBuilder -> uriBuilder.path("/" + userId).build())
                .retrieve()
                .bodyToMono(String.class)
                .block();
    }
}

3. Connecting Everything in the Controller

We now have a LangChain-like service that can interact with both OpenAI and external APIs (like KYC and loan services). Here’s how you can connect everything in the controller.

package com.bankgenai.controller;

import com.bankgenai.service.LangChainService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/api/assistant")
public class AssistantController {

    private final LangChainService langChainService;

    @Autowired
    public AssistantController(LangChainService langChainService) {
        this.langChainService = langChainService;
    }

    @PostMapping("/ask")
    public String handleQuery(@RequestBody String query) {
        return langChainService.processCustomerQuery("user123", query);
    }
}

4. Docker Setup

To deploy your application, you can create a Dockerfile to containerize it.

# Use OpenJDK 17 as the base image
FROM openjdk:17-jdk-alpine

# Set the working directory
WORKDIR /app

# Copy the jar file from target folder
COPY target/banking-genai-application.jar /app/banking-genai-application.jar

# Expose port for the application
EXPOSE 8080

# Run the Spring Boot application
CMD ["java", "-jar", "banking-genai-application.jar"]

5. Setting up Swagger for API Docs (Optional)

To generate Swagger documentation, make sure you have the required SpringDoc dependencies added to your project. This will allow you to generate OpenAPI documentation.

<dependency>
    <groupId>org.springdoc</groupId>
    <artifactId>springdoc-openapi-starter-webmvc-ui</artifactId>
    <version>2.2.0</version>
</dependency>

Once integrated, Swagger UI will be available at:

http://localhost:8080/swagger-ui.html

Auditing & Explainability

===

To ensure auditing and explainability for compliance in your GenAI-driven banking application, you need to implement logging and traceability mechanisms. This is critical, especially in regulated industries like banking, where every action (such as loan evaluations, KYC checks, and other financial activities) must be logged and auditable.

Here’s a detailed approach to auditing and explainability logs:

1. Auditing Mechanism

Auditing logs record every important action and event in the system for traceability and compliance purposes. This includes details like who triggered an action, the outcome, timestamp, and any relevant data involved.

Implementation of Auditing in Spring Boot:

You can use Spring AOP (Aspect-Oriented Programming) to intercept method calls and log important information.

📦 AuditingService.java:

This service will record every action taken by the system.

package com.bankgenai.audit;

import org.springframework.stereotype.Service;
import java.util.logging.Logger;

@Service
public class AuditingService {

    private static final Logger logger = Logger.getLogger(AuditingService.class.getName());

    public void logAction(String action, String userId, String status, String additionalInfo) {
        // You can store this log into a database or an external auditing system as per your requirements
        String logMessage = String.format("Action: %s | User: %s | Status: %s | Info: %s | Timestamp: %s",
                action, userId, status, additionalInfo, System.currentTimeMillis());

        logger.info(logMessage);  // Log to console or a file
    }
}

📦 Audit Aspect with AOP (Aspect-Oriented Programming):

Now, we’ll use Spring AOP to log each important method execution.

AuditAspect.java:

Create an aspect to capture the execution of certain methods and log them.

package com.bankgenai.audit;

import org.aspectj.lang.annotation.Aspect;
import org.aspectj.lang.annotation.Before;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;

@Aspect
@Component
public class AuditAspect {

    private final AuditingService auditingService;

    @Autowired
    public AuditAspect(AuditingService auditingService) {
        this.auditingService = auditingService;
    }

    // Intercepting methods that need auditing
    @Before("execution(* com.bankgenai.service.LangChainService.processCustomerQuery(..))")
    public void logAuditBeforeExecution() {
        String action = "Customer Query Processing";
        String userId = "user123"; // This can be dynamically set based on the authenticated user
        String status = "Started";
        String additionalInfo = "Processing user query";

        auditingService.logAction(action, userId, status, additionalInfo);
    }
}

In the above example, whenever the processCustomerQuery method is called, the AuditAspect will intercept and log the action before execution.

Enable AspectJ in Spring Boot:

Make sure you enable AOP by adding this dependency in pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-aop</artifactId>
</dependency>

2. Explainability Logs

Explainability logs help you understand why a model (such as a GenAI model like OpenAI GPT) made a certain decision. This is crucial for understanding, diagnosing, and explaining decisions made by AI models in a financial context.

You can log the following details:

Input data to the model
Model’s output
Any intermediate steps taken
Reasoning behind each decision made (e.g., KYC verification decision, loan eligibility, etc.)

Explainability Logic Example:

In your LangChainService or OpenAIService, log the input, output, and reasoning.

📦 LangChainService with Explainability Logs:

package com.bankgenai.service;

import com.bankgenai.audit.AuditingService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;

@Service
public class LangChainService {

    private final OpenAIService openAIService;
    private final ExternalApiService externalApiService;
    private final AuditingService auditingService;

    @Autowired
    public LangChainService(OpenAIService openAIService, ExternalApiService externalApiService, AuditingService auditingService) {
        this.openAIService = openAIService;
        this.externalApiService = externalApiService;
        this.auditingService = auditingService;
    }

    public String processCustomerQuery(String userId, String query) {
        String response;
        String action = "Processing Customer Query";
        String status = "Started";
        String additionalInfo;

        // Log the received query for explainability
        additionalInfo = "Received Query: " + query;
        auditingService.logAction(action, userId, status, additionalInfo);

        if (query.toLowerCase().contains("kyc")) {
            response = externalApiService.verifyKyc(userId);
            // Log KYC result with reasoning
            additionalInfo = "KYC Verified for User: " + userId + " - Reason: " + response;
            auditingService.logAction(action, userId, "Completed", additionalInfo);
        } else if (query.toLowerCase().contains("loan")) {
            response = externalApiService.checkLoanEligibility(userId);
            // Log loan eligibility with reasoning
            additionalInfo = "Loan Eligibility for User: " + userId + " - Reason: " + response;
            auditingService.logAction(action, userId, "Completed", additionalInfo);
        } else {
            // Log AI model response
            response = openAIService.generateResponse(query);
            additionalInfo = "AI Response: " + response;
            auditingService.logAction(action, userId, "Completed", additionalInfo);
        }

        return response;
    }
}

3. Storing Audit and Explainability Logs

Database: Store all logs in a relational database (e.g., MySQL, PostgreSQL). You can create an AuditLogs table with fields like action, userId, status, timestamp, additionalInfo, etc.
File-based Logs: For smaller systems or for debugging purposes, you can simply log to files using Spring's Logger. For larger systems, you can use ELK stack (Elasticsearch, Logstash, Kibana) for real-time monitoring.
External Systems: Use SIEM tools (Security Information and Event Management) like Splunk, Datadog, or others for centralizing and analyzing logs across systems.

Example Database Schema for Audit Logs:

CREATE TABLE AuditLogs (
    id BIGINT AUTO_INCREMENT PRIMARY KEY,
    action VARCHAR(255),
    userId VARCHAR(255),
    status VARCHAR(50),
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    additionalInfo TEXT
);

4. Additional Compliance Considerations

GDPR: For European customers, make sure your audit logs comply with GDPR regulations. This means allowing users to request the deletion of their data.
Data Retention: Implement a retention policy for logs (e.g., keep logs for 5 years) to comply with financial regulations.
Security: Ensure logs are tamper-proof. You can use cryptographic hashing to ensure log integrity.

💼 Scenario: AI-Powered Multi-Agent Banking Assistant

🧩 Use Case

🤖 Agents Involved

🔄 Interaction Flow

🧠 Architecture Diagram Update (Conceptual)

📌 Technologies to Enable This

🧪 Example Task

📁 Project Structure (Agent Services)

📦 model/AgentRequest.java

📦 model/AgentResponse.java

🤖 KycAgentService.java

🤖 LoanAgentService.java

🤖 InvestmentAgentService.java

🤖 DisputeAgentService.java

🤝 CoordinatorAgentService.java

🌐 AssistantController.java

1. Adding External API Connectors

📦 ExternalApiService.java

📦 Application Properties (application.yml)

2. Adding LangChain Logic

📦 LangChainService.java

3. Integrating LangChain with Spring Boot Controller

4. Handling Long-running API Calls with Async Logic (Optional)

5. Dockerize the Application (Optional)

1. Setting up OpenAI API (LangChain Simulation)

Step 1: Add OpenAI Dependency

Step 2: OpenAI API Service

Step 3: Integrating with LangChain-like Logic

2. Connecting to External APIs (e.g., KYC, Loan Evaluation)

Example: KYC API Integration

Example: Loan Eligibility API Integration

3. Connecting Everything in the Controller

4. Docker Setup

5. Setting up Swagger for API Docs (Optional)

1. Auditing Mechanism

Implementation of Auditing in Spring Boot:

📦 AuditingService.java:

📦 Audit Aspect with AOP (Aspect-Oriented Programming):

AuditAspect.java:

Enable AspectJ in Spring Boot:

2. Explainability Logs

Explainability Logic Example:

📦 LangChainService with Explainability Logs:

3. Storing Audit and Explainability Logs

Example Database Schema for Audit Logs:

4. Additional Compliance Considerations

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