Introduction

Modern software development heavily depends on GitHub for:

• Code management
• Pull requests
• Issue tracking
• CI/CD workflows

But developers spend a lot of time on repetitive tasks.

So we build:

GitHub AI Agent

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What We Are Building

A GitHub AI Agent that can:

• Review pull requests
• Analyze code quality
• Create and manage issues
• Summarize repositories
• Suggest improvements
• Automate DevOps tasks

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Architecture Overview

flowchart TD

User

SpringBoot_API

GitHubAgent

PlannerAgent

ExecutorAgent

GitHubTool

CodeReviewTool

LLM

MCP_Server

User --> SpringBoot_API
SpringBoot_API --> GitHubAgent

GitHubAgent --> PlannerAgent
GitHubAgent --> ExecutorAgent

PlannerAgent --> MCP_Server
ExecutorAgent --> MCP_Server

MCP_Server --> GitHubTool
MCP_Server --> CodeReviewTool
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: GitHub Request Model

public class GitHubRequest {
    private String repoName;
    private String action; // review, issue, summary
    private String input;
}

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Step 3: GitHub Response Model

public class GitHubResponse {
    private String result;
}

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Step 4: GitHub Controller

@RestController
@RequestMapping("/api/github")
public class GitHubController {

    private final GitHubAgentService gitHubAgentService;

    public GitHubController(GitHubAgentService gitHubAgentService) {
        this.gitHubAgentService = gitHubAgentService;
    }

    @PostMapping("/run")
    public GitHubResponse run(@RequestBody GitHubRequest request) {
        return gitHubAgentService.process(request);
    }
}

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Step 5: GitHub Agent Service

@Service
public class GitHubAgentService {

    private final PlannerAgent plannerAgent;
    private final ExecutorAgent executorAgent;

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

    public GitHubResponse process(GitHubRequest request) {

        // 1. Create execution plan
        String plan = plannerAgent.createPlan(request.getAction());

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

        // 3. Return response
        GitHubResponse response = new GitHubResponse();
        response.setResult(result);

        return response;
    }
}

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Step 6: Planner Agent

@Service
public class PlannerAgent {

    public String createPlan(String action) {

        switch (action.toLowerCase()) {

            case "review":
                return "CODE_REVIEW_PLAN";

            case "issue":
                return "ISSUE_MANAGEMENT_PLAN";

            case "summary":
                return "REPO_SUMMARY_PLAN";

            default:
                return "GENERAL_GITHUB_PLAN";
        }
    }
}

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Step 7: Executor Agent

@Service
public class ExecutorAgent {

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

        switch (plan) {

            case "CODE_REVIEW_PLAN":
                return "Code review completed for repo: " + repoName;

            case "ISSUE_MANAGEMENT_PLAN":
                return "Issue created/updated in repo: " + repoName;

            case "REPO_SUMMARY_PLAN":
                return "Repository summary generated for: " + repoName;

            default:
                return "GitHub operation completed";
        }
    }
}

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Step 8: MCP Integration (Advanced Layer)

Now we upgrade using MCP:

ExecutorAgent → MCP Server → GitHub API Tool + LLM + Code Analyzer

---

MCP Enhanced Architecture

flowchart TD

GitHubAgent

PlannerAgent

ExecutorAgent

MCP_Client

MCP_Server

GitHubTool

PRReviewTool

IssueTool

LLM

GitHubAgent --> PlannerAgent
GitHubAgent --> ExecutorAgent

ExecutorAgent --> MCP_Client
MCP_Client --> MCP_Server

MCP_Server --> GitHubTool
MCP_Server --> PRReviewTool
MCP_Server --> IssueTool
MCP_Server --> LLM

---

GitHub AI Agent Workflow

flowchart TD

UserRequest

PlanGeneration

GitHubActionSelection

ToolExecution

LLMAnalysis

ResultFormatting

Response

UserRequest --> PlanGeneration
PlanGeneration --> GitHubActionSelection
GitHubActionSelection --> ToolExecution
ToolExecution --> LLMAnalysis
LLMAnalysis --> ResultFormatting
ResultFormatting --> Response

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Example 1: Code Review

Input:

Review pull request #45

Flow:

1. Planner selects CODE_REVIEW_PLAN
2. MCP CodeReview tool analyzes changes
3. LLM generates feedback
4. Response returned

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Example 2: Issue Management

Input:

Create bug issue for login failure

Flow:

1. Planner selects ISSUE_MANAGEMENT_PLAN
2. MCP Issue tool creates ticket
3. GitHub API called
4. Confirmation returned

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Example 3: Repository Summary

Input:

Summarize repository codebase

Flow:

1. Planner selects REPO_SUMMARY_PLAN
2. MCP tool scans repository
3. LLM generates summary
4. Response returned

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Enterprise Architecture

flowchart LR

Client

API_Gateway

GitHubAgent

PlannerAgent

ExecutorAgent

MCP_Layer

GitHubAPI

CodeAnalysisTool

LLMServices

Client --> API_Gateway
API_Gateway --> GitHubAgent

GitHubAgent --> PlannerAgent
PlannerAgent --> ExecutorAgent

ExecutorAgent --> MCP_Layer

MCP_Layer --> GitHubAPI
MCP_Layer --> CodeAnalysisTool
MCP_Layer --> LLMServices

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Benefits of GitHub AI Agent

1. Developer Productivity

• Automates repetitive tasks

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2. Code Quality

• AI-powered reviews

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3. Faster DevOps

• Issue automation

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4. Scalability

• Works across repositories

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5. Intelligence

• LLM-based reasoning for code

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Challenges

❌ GitHub API rate limits
❌ Code review accuracy
❌ Permission and security handling
❌ Large repository processing
❌ MCP tool orchestration complexity

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Best Practices

✅ Use MCP for all GitHub operations
✅ Add authentication layer (GitHub OAuth)
✅ Cache repository metadata
✅ Use async processing for large repos
✅ Log all agent decisions
✅ Separate review and execution logic

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Common Mistakes

❌ Direct GitHub API calls from agents
❌ No review validation step
❌ No rate limit handling
❌ Mixing business logic in executor
❌ No fallback strategy

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When to Use GitHub AI Agents

Use when:

• Large development teams exist
• Frequent PR reviews needed
• DevOps automation required
• Multi-repo management needed

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When NOT to Use

Avoid when:

• Small personal projects
• Low repository activity
• Simple static repositories

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Summary

In this article, you learned:

• How to build a GitHub AI Agent
• Planner + Executor architecture
• MCP integration for DevOps automation
• Code review and issue automation
• Repository analysis workflows
• Enterprise architecture design
• Best practices and challenges

You now have a complete GitHub AI Agent system, which can evolve into a full enterprise DevOps automation platform using Java, Spring Boot, and MCP.

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