Introduction

HR systems in enterprises handle:

• Recruitment
• Employee onboarding
• Payroll queries
• Policy management
• Performance tracking
• Employee support

Most of these tasks are repetitive and rule-heavy.

So we build:

HR AI Agent

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What We Are Building

An HR AI Agent that can:

• Screen resumes
• Answer HR policy questions
• Automate onboarding
• Manage employee queries
• Generate HR reports
• Assist in recruitment decisions

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Architecture Overview

flowchart TD

User

SpringBoot_API

HRAgent

PlannerAgent

ExecutorAgent

HRTool

RecruitmentTool

LLM

MCP_Server

User --> SpringBoot_API
SpringBoot_API --> HRAgent

HRAgent --> PlannerAgent
HRAgent --> ExecutorAgent

PlannerAgent --> MCP_Server
ExecutorAgent --> MCP_Server

MCP_Server --> HRTool
MCP_Server --> RecruitmentTool
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: HR Request Model

public class HRRequest {
    private String employeeId;
    private String action; // recruit, onboard, policy, payroll
    private String input;
}

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Step 3: HR Response Model

public class HRResponse {
    private String result;
}

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Step 4: HR Controller

@RestController
@RequestMapping("/api/hr")
public class HRController {

    private final HRAgentService hrAgentService;

    public HRController(HRAgentService hrAgentService) {
        this.hrAgentService = hrAgentService;
    }

    @PostMapping("/run")
    public HRResponse run(@RequestBody HRRequest request) {
        return hrAgentService.process(request);
    }
}

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Step 5: HR Agent Service

@Service
public class HRAgentService {

    private final PlannerAgent plannerAgent;
    private final ExecutorAgent executorAgent;

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

    public HRResponse process(HRRequest request) {

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

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

        // 3. Return response
        HRResponse response = new HRResponse();
        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 "recruit":
                return "RECRUITMENT_PLAN";

            case "onboard":
                return "ONBOARDING_PLAN";

            case "policy":
                return "HR_POLICY_PLAN";

            case "payroll":
                return "PAYROLL_QUERY_PLAN";

            default:
                return "GENERAL_HR_PLAN";
        }
    }
}

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Step 7: Executor Agent

@Service
public class ExecutorAgent {

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

        switch (plan) {

            case "RECRUITMENT_PLAN":
                return "Candidate screened for role: " + input;

            case "ONBOARDING_PLAN":
                return "Onboarding process started for employee: " + employeeId;

            case "HR_POLICY_PLAN":
                return "HR policy details fetched for query: " + input;

            case "PAYROLL_QUERY_PLAN":
                return "Payroll details retrieved for employee: " + employeeId;

            default:
                return "HR request processed successfully";
        }
    }
}

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Step 8: MCP Integration (Advanced Layer)

Now we upgrade HR Agent using MCP:

ExecutorAgent → MCP Server → HR Tools + Recruitment Engine + LLM

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MCP Enhanced Architecture

flowchart TD

HRAgent

PlannerAgent

ExecutorAgent

MCP_Client

MCP_Server

HRTool

RecruitmentTool

PayrollTool

LLM

HRAgent --> PlannerAgent
HRAgent --> ExecutorAgent

ExecutorAgent --> MCP_Client
MCP_Client --> MCP_Server

MCP_Server --> HRTool
MCP_Server --> RecruitmentTool
MCP_Server --> PayrollTool
MCP_Server --> LLM

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HR AI Agent Workflow

flowchart TD

UserRequest

PlanGeneration

HRActionSelection

ToolExecution

LLMReasoning

ResponseFormatting

FinalResponse

UserRequest --> PlanGeneration
PlanGeneration --> HRActionSelection
HRActionSelection --> ToolExecution
ToolExecution --> LLMReasoning
LLMReasoning --> ResponseFormatting
ResponseFormatting --> FinalResponse

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Example 1: Recruitment

Input:

Find candidates for Java developer role

Flow:

1. Planner selects RECRUITMENT_PLAN
2. MCP recruitment tool filters candidates
3. LLM ranks profiles
4. Response returned

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Example 2: Onboarding

Input:

Onboard new employee E123

Flow:

1. Planner selects ONBOARDING_PLAN
2. MCP onboarding tool triggered
3. HR system updated
4. Response returned

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Example 3: Policy Query

Input:

What is maternity leave policy?

Flow:

1. Planner selects HR_POLICY_PLAN
2. MCP tool fetches HR policy docs
3. LLM summarizes policy
4. Response returned

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Enterprise Architecture

flowchart LR

Client

API_Gateway

HRAgent

PlannerAgent

ExecutorAgent

MCP_Layer

HRSystem

RecruitmentSystem

LLMServices

Client --> API_Gateway
API_Gateway --> HRAgent

HRAgent --> PlannerAgent
PlannerAgent --> ExecutorAgent

ExecutorAgent --> MCP_Layer

MCP_Layer --> HRSystem
MCP_Layer --> RecruitmentSystem
MCP_Layer --> LLMServices

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Benefits of HR AI Agent

1. Recruitment Automation

• Faster candidate screening

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2. Employee Support

• Instant HR responses

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3. Onboarding Efficiency

• Automated workflows

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4. Policy Access

• AI-powered HR assistant

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5. Scalability

• Handles large employee base

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Challenges

❌ Sensitive employee data handling
❌ Compliance with labor laws
❌ HR policy accuracy
❌ MCP tool orchestration complexity
❌ Security and privacy concerns

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Best Practices

✅ Use MCP for all HR operations
✅ Secure employee data
✅ Add audit logging
✅ Separate recruitment and payroll logic
✅ Use role-based access control
✅ Validate all HR outputs

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Common Mistakes

❌ Hardcoding HR rules
❌ No compliance checks
❌ Direct database access without MCP
❌ Missing audit trails
❌ No data encryption

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When to Use HR AI Agents

Use when:

• Large workforce management is needed
• Recruitment volume is high
• HR queries are frequent
• Onboarding automation required

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When NOT to Use

Avoid when:

• Small teams
• Manual HR processes preferred
• High legal constraints without automation readiness

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Summary

In this article, you learned:

• How to build an HR AI Agent
• Planner + Executor architecture
• MCP integration for HR automation
• Recruitment, onboarding, and payroll workflows
• Enterprise architecture design
• Best practices and challenges

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

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