Introduction

Every enterprise has one major problem:

• Knowledge is scattered
• Documents are hard to search
• Employees waste time finding answers
• Internal systems are disconnected

So we build:

Knowledge Assistant AI Agent

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What We Are Building

A Knowledge Assistant AI Agent that can:

• Answer enterprise questions
• Search internal documents
• Use RAG for accurate responses
• Summarize enterprise knowledge
• Integrate with APIs and databases
• Provide contextual answers

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Architecture Overview

flowchart TD

User

SpringBoot_API

KnowledgeAgent

PlannerAgent

ExecutorAgent

RAGEngine

VectorDB

DocumentStore

LLM

MCP_Server

User --> SpringBoot_API
SpringBoot_API --> KnowledgeAgent

KnowledgeAgent --> PlannerAgent
KnowledgeAgent --> ExecutorAgent

PlannerAgent --> MCP_Server
ExecutorAgent --> MCP_Server

MCP_Server --> RAGEngine
MCP_Server --> VectorDB
MCP_Server --> DocumentStore
MCP_Server --> LLM

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Step 1: Create Spring Boot Project

Dependencies:

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

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

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Step 2: Knowledge Request Model

public class KnowledgeRequest {
    private String question;
    private String userId;
}

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Step 3: Knowledge Response Model

public class KnowledgeResponse {
    private String answer;
}

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Step 4: Knowledge Controller

@RestController
@RequestMapping("/api/knowledge")
public class KnowledgeController {

    private final KnowledgeAgentService knowledgeAgentService;

    public KnowledgeController(KnowledgeAgentService knowledgeAgentService) {
        this.knowledgeAgentService = knowledgeAgentService;
    }

    @PostMapping("/ask")
    public KnowledgeResponse ask(@RequestBody KnowledgeRequest request) {
        return knowledgeAgentService.process(request);
    }
}

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Step 5: Knowledge Agent Service

@Service
public class KnowledgeAgentService {

    private final PlannerAgent plannerAgent;
    private final ExecutorAgent executorAgent;

    public KnowledgeAgentService(PlannerAgent plannerAgent,
                                 ExecutorAgent executorAgent) {
        this.plannerAgent = plannerAgent;
        this.executorAgent = executorAgent;
    }

    public KnowledgeResponse process(KnowledgeRequest request) {

        // 1. Create query plan
        String plan = plannerAgent.createPlan(request.getQuestion());

        // 2. Execute knowledge retrieval
        String answer = executorAgent.execute(plan,
                request.getUserId(),
                request.getQuestion());

        // 3. Return response
        KnowledgeResponse response = new KnowledgeResponse();
        response.setAnswer(answer);

        return response;
    }
}

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Step 6: Planner Agent

@Service
public class PlannerAgent {

    public String createPlan(String question) {

        if (question.toLowerCase().contains("policy")) {
            return "POLICY_KNOWLEDGE_PLAN";
        }

        if (question.toLowerCase().contains("system")) {
            return "SYSTEM_KNOWLEDGE_PLAN";
        }

        if (question.toLowerCase().contains("process")) {
            return "PROCESS_KNOWLEDGE_PLAN";
        }

        return "GENERAL_KNOWLEDGE_PLAN";
    }
}

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Step 7: Executor Agent

@Service
public class ExecutorAgent {

    public String execute(String plan,
                          String userId,
                          String question) {

        switch (plan) {

            case "POLICY_KNOWLEDGE_PLAN":
                return "Policy details retrieved for question: " + question;

            case "SYSTEM_KNOWLEDGE_PLAN":
                return "System documentation fetched for user: " + userId;

            case "PROCESS_KNOWLEDGE_PLAN":
                return "Process explanation generated successfully";

            default:
                return "General knowledge answer generated for: " + question;
        }
    }
}

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Step 8: MCP Integration (Advanced Layer)

Now we upgrade Knowledge Assistant using MCP:

ExecutorAgent → MCP Server → RAG Engine + Vector DB + Document Store + LLM

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MCP Enhanced Architecture

flowchart TD

KnowledgeAgent

PlannerAgent

ExecutorAgent

MCP_Client

MCP_Server

RAGEngine

VectorDB

DocumentStore

LLM

KnowledgeAgent --> PlannerAgent
KnowledgeAgent --> ExecutorAgent

ExecutorAgent --> MCP_Client
MCP_Client --> MCP_Server

MCP_Server --> RAGEngine
MCP_Server --> VectorDB
MCP_Server --> DocumentStore
MCP_Server --> LLM

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Knowledge Assistant Workflow

flowchart TD

UserQuestion

PlanGeneration

KnowledgeRetrieval

RAGProcessing

LLMReasoning

AnswerGeneration

Response

UserQuestion --> PlanGeneration
PlanGeneration --> KnowledgeRetrieval
KnowledgeRetrieval --> RAGProcessing
RAGProcessing --> LLMReasoning
LLMReasoning --> AnswerGeneration
AnswerGeneration --> Response

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Example 1: Policy Question

Input:

What is leave policy?

Flow:

1. Planner selects POLICY_KNOWLEDGE_PLAN
2. MCP retrieves policy documents
3. RAG engine processes context
4. LLM generates answer

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Example 2: System Question

Input:

How does billing system work?

Flow:

1. Planner selects SYSTEM_KNOWLEDGE_PLAN
2. MCP fetches system docs
3. Vector DB retrieves relevant context
4. Answer generated

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Example 3: Process Question

Input:

What is onboarding process?

Flow:

1. Planner selects PROCESS_KNOWLEDGE_PLAN
2. MCP retrieves process documents
3. LLM summarizes steps
4. Response returned

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Enterprise Architecture

flowchart LR

Client

API_Gateway

KnowledgeAgent

PlannerAgent

ExecutorAgent

MCP_Layer

RAGEngine

VectorDB

DocumentStore

LLMServices

Client --> API_Gateway
API_Gateway --> KnowledgeAgent

KnowledgeAgent --> PlannerAgent
PlannerAgent --> ExecutorAgent

ExecutorAgent --> MCP_Layer

MCP_Layer --> RAGEngine
MCP_Layer --> VectorDB
MCP_Layer --> DocumentStore
MCP_Layer --> LLMServices

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Benefits of Knowledge Assistant

1. Enterprise Knowledge Access

• Centralized information system

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2. Faster Decision Making

• Instant answers

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3. Reduced Dependency

• No need to search documents manually

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4. Scalable Knowledge Base

• Works across departments

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5. Context-Aware Responses

• Uses RAG + MCP integration

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Challenges

❌ Knowledge freshness issues
❌ Incorrect document retrieval
❌ Large-scale vector search latency
❌ Context mismatch
❌ Data security concerns

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Best Practices

✅ Use RAG + MCP together
✅ Keep vector DB optimized
✅ Regularly update knowledge base
✅ Secure sensitive documents
✅ Cache frequent queries
✅ Monitor retrieval quality

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Common Mistakes

❌ No proper indexing strategy
❌ Using LLM without retrieval
❌ No document versioning
❌ Ignoring security layer
❌ Overloading context window

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When to Use Knowledge Assistant

Use when:

• Enterprise knowledge is large
• Employees need instant answers
• Documentation is scattered
• RAG systems are required

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When NOT to Use

Avoid when:

• Small static systems
• No document-based knowledge
• Simple FAQ systems only

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Summary

In this article, you learned:

• How to build a Knowledge Assistant AI Agent
• Planner + Executor architecture
• MCP + RAG integration
• Vector DB and document retrieval
• Enterprise knowledge workflows
• Best practices and challenges

You now have a complete Knowledge Assistant system, which can evolve into a full enterprise AI knowledge platform using Java, Spring Boot, MCP, and RAG.

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