Step-by-step Enterprise AI Project Guide

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Introduction

Code review is one of the most critical parts of software development.

But traditional code review is:

• Time-consuming
• Manual
• Inconsistent
• Subjective

So we build:

Code Review AI Agent

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What We Are Building

A Code Review AI Agent that can:

• Review pull requests automatically
• Detect bugs and code smells
• Suggest performance improvements
• Enforce coding standards
• Summarize changes in PRs
• Score code quality

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Architecture Overview

flowchart TD

User

SpringBoot_API

CodeReviewAgent

PlannerAgent

ExecutorAgent

GitDiffTool

StaticAnalysisTool

LLM

MCP_Server

User --> SpringBoot_API
SpringBoot_API --> CodeReviewAgent

CodeReviewAgent --> PlannerAgent
CodeReviewAgent --> ExecutorAgent

PlannerAgent --> MCP_Server
ExecutorAgent --> MCP_Server

MCP_Server --> GitDiffTool
MCP_Server --> StaticAnalysisTool
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: Code Review Request Model

public class CodeReviewRequest {
    private String repoName;
    private String pullRequestId;
    private String diff;
}

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Step 3: Code Review Response Model

public class CodeReviewResponse {
    private String summary;
    private String qualityScore;
}

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Step 4: Code Review Controller

@RestController
@RequestMapping("/api/code-review")
public class CodeReviewController {

    private final CodeReviewAgentService codeReviewAgentService;

    public CodeReviewController(CodeReviewAgentService codeReviewAgentService) {
        this.codeReviewAgentService = codeReviewAgentService;
    }

    @PostMapping("/run")
    public CodeReviewResponse run(@RequestBody CodeReviewRequest request) {
        return codeReviewAgentService.process(request);
    }
}

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Step 5: Code Review Agent Service

@Service
public class CodeReviewAgentService {

    private final PlannerAgent plannerAgent;
    private final ExecutorAgent executorAgent;

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

    public CodeReviewResponse process(CodeReviewRequest request) {

        // 1. Plan review strategy
        String plan = plannerAgent.createPlan(request.getDiff());

        // 2. Execute review
        String result = executorAgent.execute(plan,
                request.getRepoName(),
                request.getDiff());

        // 3. Build response
        CodeReviewResponse response = new CodeReviewResponse();
        response.setSummary(result);
        response.setQualityScore("8.5/10");

        return response;
    }
}

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Step 6: Planner Agent

@Service
public class PlannerAgent {

    public String createPlan(String diff) {

        if (diff.contains("security")) {
            return "SECURITY_REVIEW_PLAN";
        }

        if (diff.contains("performance")) {
            return "PERFORMANCE_REVIEW_PLAN";
        }

        if (diff.contains("bug")) {
            return "BUG_DETECTION_PLAN";
        }

        return "GENERAL_CODE_REVIEW_PLAN";
    }
}

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Step 7: Executor Agent

@Service
public class ExecutorAgent {

    public String execute(String plan,
                          String repoName,
                          String diff) {

        switch (plan) {

            case "SECURITY_REVIEW_PLAN":
                return "Security issues detected in repository: " + repoName;

            case "PERFORMANCE_REVIEW_PLAN":
                return "Performance improvements suggested for repo: " + repoName;

            case "BUG_DETECTION_PLAN":
                return "Potential bugs found in code diff";

            default:
                return "Code review completed successfully for repo: " + repoName;
        }
    }
}

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Step 8: MCP Integration (Advanced Layer)

Now we upgrade with MCP:

ExecutorAgent → MCP Server → GitDiff Tool + Static Analysis Tool + LLM

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MCP Enhanced Architecture

flowchart TD

CodeReviewAgent

PlannerAgent

ExecutorAgent

MCP_Client

MCP_Server

GitDiffTool

StaticAnalysisTool

SecurityScanner

LLM

CodeReviewAgent --> PlannerAgent
CodeReviewAgent --> ExecutorAgent

ExecutorAgent --> MCP_Client
MCP_Client --> MCP_Server

MCP_Server --> GitDiffTool
MCP_Server --> StaticAnalysisTool
MCP_Server --> SecurityScanner
MCP_Server --> LLM

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Code Review Workflow

flowchart TD

PullRequest

DiffAnalysis

PlanGeneration

ToolExecution

LLMReview

ScoreCalculation

FinalReport

PullRequest --> DiffAnalysis
DiffAnalysis --> PlanGeneration
PlanGeneration --> ToolExecution
ToolExecution --> LLMReview
LLMReview --> ScoreCalculation
ScoreCalculation --> FinalReport

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Example 1: Security Review

Input:

PR contains authentication changes

Flow:

1. Planner selects SECURITY_REVIEW_PLAN
2. MCP security tool scans code
3. LLM identifies vulnerabilities
4. Report generated

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Example 2: Performance Review

Input:

PR improves API performance

Flow:

1. Planner selects PERFORMANCE_REVIEW_PLAN
2. Static analysis tool executed
3. LLM suggests optimizations
4. Feedback generated

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Example 3: Bug Detection

Input:

PR fixes null pointer issue

Flow:

1. Planner selects BUG_DETECTION_PLAN
2. MCP diff tool analyzes changes
3. LLM validates fix correctness
4. Review completed

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Enterprise Architecture

flowchart LR

Client

API_Gateway

CodeReviewAgent

PlannerAgent

ExecutorAgent

MCP_Layer

GitHubPR

AnalysisTools

LLMServices

Client --> API_Gateway
API_Gateway --> CodeReviewAgent

CodeReviewAgent --> PlannerAgent
PlannerAgent --> ExecutorAgent

ExecutorAgent --> MCP_Layer

MCP_Layer --> GitHubPR
MCP_Layer --> AnalysisTools
MCP_Layer --> LLMServices

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Benefits of Code Review AI Agent

1. Faster Reviews

• Instant PR analysis

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2. Better Code Quality

• Standardized feedback

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3. Security Enforcement

• Automatic vulnerability detection

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4. Scalability

• Handles large repositories

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5. Consistency

• Uniform review standards

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Challenges

❌ False positives in analysis
❌ Complex code context understanding
❌ Large diff handling
❌ Tool integration complexity
❌ LLM accuracy limitations

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Best Practices

✅ Use MCP for all analysis tools
✅ Combine static + LLM analysis
✅ Maintain review history
✅ Add scoring system
✅ Enable human override
✅ Cache repository context

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Common Mistakes

❌ Only using LLM without static tools
❌ No diff preprocessing
❌ Missing security layer
❌ No scoring mechanism
❌ No fallback to human review

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When to Use Code Review AI Agent

Use when:

• Large development teams exist
• High PR volume
• Security enforcement needed
• Continuous integration pipelines exist

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When NOT to Use

Avoid when:

• Small projects
• Low PR activity
• Highly sensitive manual review required

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Summary

In this article, you learned:

• How to build a Code Review AI Agent
• Planner + Executor architecture
• MCP integration for DevOps automation
• Security, performance, and bug detection workflows
• Enterprise architecture design
• Best practices and challenges

You now have a complete Code Review AI Agent system, which can evolve into a full enterprise DevSecOps platform using Java, Spring Boot, and MCP.

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