Job Assistant AI - Backend System

An intelligent job application management system powered by AI agents that help users optimize their resumes, generate tailored application responses, and track job applications through an orchestrated multi-agent architecture.

![Node.js] ![Express] ![PostgreSQL] ![License]

🎯 Overview

Job Assistant AI is a comprehensive backend system designed to streamline the job application process through intelligent automation. The system employs an Agentic AI architecture where multiple specialized AI agents work collaboratively through a central orchestrator to provide:

Resume analysis and matching against job descriptions
Intelligent answer generation for application questions
Automated profile extraction from resumes
Application tracking and analytics dashboard
Secure user authentication and data management

The Problem We Solve

Job seekers face several challenges:

❌ Difficulty tailoring resumes for each position
❌ Time-consuming application processes
❌ Uncertainty about resume-job fit
❌ Writing compelling application responses
❌ Tracking multiple applications

Our Solution

✅ AI-powered resume scoring and recommendations
✅ Automated profile data extraction for forms
✅ Personalized answer generation
✅ Comprehensive application tracking
✅ Data-driven insights and analytics

✨ Key Features

1. Resume Analysis & Scoring

Analyzes resume content against job descriptions
Calculates match scores (0-100)
Identifies matched and missing skills
Provides actionable recommendations
Analyzes experience level and education match

2. AI-Powered Answer Generation

Generates personalized responses to application questions
Context-aware based on resume and job description
Multiple question types supported
Provides answer improvement tips
Customizable tone and style

3. Profile Autofill

Extracts structured data from resumes
Parses personal information, contact details
Identifies work experience and education
Categorizes skills by type
Confidence scoring for extracted data

4. Application Tracking

Comprehensive application management (CRUD operations)
Status tracking through application lifecycle
Timeline visualization
Notes and attachments support
Filter and search capabilities

5. Dashboard & Analytics

Real-time application statistics
Status distribution analytics
Agent usage metrics
Application pipeline visualization
Time-based trend analysis

6. Secure Authentication

JWT-based authentication
Bcrypt password hashing
Protected API endpoints
Token expiration management
User session handling

🏗️ System Architecture

High-Level Architecture

┌─────────────────────────────────────────────────────────┐
│                    Client Layer                          │
│              (Frontend Application)                      │
└────────────────────┬────────────────────────────────────┘
                     │ HTTP/REST
                     ▼
┌─────────────────────────────────────────────────────────┐
│                   API Gateway                            │
│              (Express.js Server)                         │
│  ┌────────────────────────────────────────────────┐    │
│  │         Authentication Middleware              │    │
│  │              (JWT Validation)                  │    │
│  └────────────────────────────────────────────────┘    │
│  ┌────────────────────────────────────────────────┐    │
│  │            Route Handlers                      │    │
│  │  • Auth Routes    • Resume Routes              │    │
│  │  • Application Routes • Dashboard Routes       │    │
│  └────────────────────────────────────────────────┘    │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│              Agent Orchestration Layer                   │
│                                                          │
│  ┌──────────────────────────────────────────────────┐  │
│  │          Agent Orchestrator                      │  │
│  │      (Coordinates AI Agents)                     │  │
│  │  • Task routing                                  │  │
│  │  • Workflow management                           │  │
│  │  • Result aggregation                            │  │
│  └──────────┬───────────────┬──────────┬────────────┘  │
│             │               │          │                │
│             ▼               ▼          ▼                │
│  ┌──────────────┐ ┌─────────────┐ ┌──────────────┐   │
│  │   Resume     │ │   Answer    │ │   Autofill   │   │
│  │   Scorer     │ │  Generator  │ │    Agent     │   │
│  │   Agent      │ │    Agent    │ │              │   │
│  └──────────────┘ └─────────────┘ └──────────────┘   │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│                  Data Layer                              │
│                                                          │
│  ┌──────────────────────────────────────────────────┐  │
│  │         PostgreSQL Database                      │  │
│  │  • users              • resumes                  │  │
│  │  • job_applications   • agent_results            │  │
│  └──────────────────────────────────────────────────┘  │
│                                                          │
│  ┌──────────────────────────────────────────────────┐  │
│  │         File Storage                             │  │
│  │  • Resume files (PDF, DOCX, TXT)                │  │
│  └──────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────┘

Request Flow Example

User uploads resume → API receives file → Stores in database
                                       ↓
User requests analysis → Orchestrator invokes Resume Scorer
                                       ↓
Resume Scorer analyzes → Returns score and recommendations
                                       ↓
Orchestrator stores results → Returns to user via API

Agentic Architecture Principles

Modularity: Each agent is independent and self-contained
Orchestration: Central coordinator manages agent interactions
Scalability: New agents can be added without modifying existing ones
Reusability: Agents can be combined in different workflows
Observability: All agent actions are logged and tracked

🛠️ Technology Stack

Backend Core

Runtime: Node.js v16+
Framework: Express.js 4.18
Language: JavaScript (ES6+)
Architecture: RESTful API, Agentic AI Pattern

Database

Primary Database: PostgreSQL 12+
ORM: Native pg driver with parameterized queries
Schema: Relational with foreign key constraints

Authentication & Security

Authentication: JSON Web Tokens (JWT)
Password Hashing: bcrypt with salt rounds
CORS: Configurable cross-origin resource sharing
Input Validation: Server-side validation

File Handling

Upload Middleware: Multer
Supported Formats: PDF, DOC, DOCX, TXT
Storage: Local filesystem (production: cloud storage)

Development Tools

Process Manager: Nodemon (development)
Environment Variables: dotenv
API Testing: curl, Postman, PowerShell scripts

🚀 Getting Started

Prerequisites

Ensure you have the following installed:

Node.js (v16 or higher) - Download
PostgreSQL (v12 or higher) - Download
npm (comes with Node.js) or yarn
Git (optional, for version control)

Installation Steps

1. Clone/Download the Project

# If using Git
git clone <repository-url>
cd job-assistant-backend

# Or download and extract ZIP file
cd job-assistant-backend

2. Install Dependencies

npm install

This will install:

express, cors, dotenv
pg (PostgreSQL driver)
bcryptjs, jsonwebtoken
multer (file uploads)
nodemon (dev dependency)

3. Set Up PostgreSQL Database

Option A: Using psql command line

# Connect to PostgreSQL
psql -U postgres

# Create database and user
CREATE DATABASE job_assistant;
CREATE USER job_user WITH PASSWORD 'your_secure_password';
GRANT ALL PRIVILEGES ON DATABASE job_assistant TO job_user;

# Grant schema permissions
\c job_assistant
GRANT ALL PRIVILEGES ON SCHEMA public TO job_user;
GRANT CREATE ON SCHEMA public TO job_user;

# Exit
\q

Option B: Using pgAdmin

Open pgAdmin
Right-click "Databases" → Create → Database
Name: job_assistant
Right-click "Login/Group Roles" → Create → Login/Group Role
Name: job_user, Password: your choice
In SQL tab, run:

GRANT ALL PRIVILEGES ON DATABASE job_assistant TO job_user;
GRANT ALL PRIVILEGES ON SCHEMA public TO job_user;

4. Configure Environment Variables

Create a .env file in the project root:

# Server Configuration
PORT=3000
NODE_ENV=development

# Database Configuration
DB_HOST=localhost
DB_PORT=5432
DB_NAME=job_assistant
DB_USER=job_user
DB_PASSWORD=your_secure_password

# JWT Secret (generate a secure random string)
# You can generate one using: node -e "console.log(require('crypto').randomBytes(32).toString('hex'))"
JWT_SECRET=your-super-secret-jwt-key-change-this-in-production

# File Upload Configuration
MAX_FILE_SIZE=5242880

Security Note:

Never commit .env to version control
Use strong passwords in production
Generate a secure JWT_SECRET

5. Start the Server

Development mode (with auto-restart):

npm run dev

Production mode:

npm start

You should see:

📊 Connected to PostgreSQL database
✅ Database tables initialized successfully
🚀 Job Assistant API server running on port 3000
📍 Health check: http://localhost:3000/api/health
🌍 Environment: development

6. Verify Installation

Test the health endpoint:

curl http://localhost:3000/api/health

Expected response:

{
  "status": "OK",
  "message": "Job Assistant API is running",
  "timestamp": "2025-09-27T10:00:00.000Z"
}

Run the test suite:

# Windows PowerShell
.\simple-test.ps1

# Or use the comprehensive test suite
.\test-all-endpoints.ps1

📚 API Documentation

Base URL

http://localhost:3000

Authentication

Most endpoints require authentication via JWT token:

Authorization: Bearer <your_jwt_token>

Obtain a token by registering or logging in.

Authentication Endpoints

Register User

POST /api/auth/register
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "securePassword123",
  "firstName": "John",
  "lastName": "Doe",
  "phone": "555-1234" (optional)
}

Success Response (201):

{
  "success": true,
  "message": "User registered successfully",
  "data": {
    "user": {
      "id": 1,
      "email": "user@example.com",
      "firstName": "John",
      "lastName": "Doe"
    },
    "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
  }
}

Error Response (400):

{
  "success": false,
  "message": "Email and password are required"
}

Login

POST /api/auth/login
Content-Type: application/json

{
  "email": "user@example.com",
  "password": "securePassword123"
}

Success Response (200): Same as register

Error Response (401):

{
  "success": false,
  "message": "Invalid email or password"
}

Resume Management Endpoints

Upload Resume

POST /api/resumes/upload
Authorization: Bearer <token>
Content-Type: multipart/form-data

resume: <file> (PDF, DOC, DOCX, TXT - max 5MB)

Success Response (200):

{
  "success": true,
  "message": "Resume uploaded successfully",
  "data": {
    "id": 1,
    "filename": "john_doe_resume.pdf",
    "size": 245678,
    "uploadDate": "2025-09-27T10:30:00Z"
  }
}

Get User's Resumes

GET /api/resumes/my-resumes
Authorization: Bearer <token>

Success Response (200):

{
  "success": true,
  "data": {
    "resumes": [
      {
        "id": 1,
        "filename": "resume_v1.pdf",
        "created_at": "2025-09-27T10:30:00Z"
      },
      {
        "id": 2,
        "filename": "resume_v2.pdf",
        "created_at": "2025-09-28T14:20:00Z"
      }
    ],
    "count": 2
  }
}

Get Resume Details

GET /api/resumes/:id
Authorization: Bearer <token>

Success Response (200):

{
  "success": true,
  "data": {
    "id": 1,
    "filename": "resume.pdf",
    "contentText": "John Doe\nSoftware Engineer...",
    "createdAt": "2025-09-27T10:30:00Z"
  }
}

Delete Resume

DELETE /api/resumes/:id
Authorization: Bearer <token>

Success Response (200):

{
  "success": true,
  "message": "Resume deleted successfully"
}

Job Application Endpoints

Create Application

POST /api/applications
Authorization: Bearer <token>
Content-Type: application/json

{
  "resumeId": 1,
  "companyName": "Tech Corp",
  "jobTitle": "Senior Software Engineer",
  "jobDescription": "We are seeking an experienced developer...",
  "applicationUrl": "https://techcorp.com/careers/12345",
  "notes": "Applied via LinkedIn"
}

Success Response (201):

{
  "success": true,
  "message": "Job application created successfully",
  "data": {
    "id": 1,
    "company_name": "Tech Corp",
    "job_title": "Senior Software Engineer",
    "status": "not_submitted",
    "created_at": "2025-09-27T11:00:00Z"
  }
}

Get User's Applications

GET /api/applications/my-applications
Authorization: Bearer <token>

Query Parameters:
  - status: Filter by status (optional)
  - limit: Number of results (default: 50)
  - offset: Pagination offset (default: 0)

Example:

GET /api/applications/my-applications?status=submitted&limit=10

Success Response (200):

{
  "success": true,
  "data": {
    "applications": [
      {
        "id": 1,
        "company_name": "Tech Corp",
        "job_title": "Software Engineer",
        "status": "submitted",
        "resume_filename": "resume.pdf",
        "created_at": "2025-09-27T11:00:00Z",
        "updated_at": "2025-09-27T12:00:00Z"
      }
    ],
    "count": 1
  }
}

Update Application Status

PATCH /api/applications/:id/status
Authorization: Bearer <token>
Content-Type: application/json

{
  "status": "interview_requested",
  "notes": "Phone screen scheduled for tomorrow at 2 PM"
}

Valid Status Values:

not_submitted - Draft stage
submitted - Application sent
received_response - Company replied
interview_requested - Interview scheduled
rejected_after_interview - Post-interview rejection
onsite_interview_requested - On-site interview
offer_received - Job offer received
rejected - Application rejected
accepted - Offer accepted

Success Response (200):

{
  "success": true,
  "message": "Application status updated successfully",
  "data": {
    "id": 1,
    "company_name": "Tech Corp",
    "job_title": "Software Engineer",
    "status": "interview_requested",
    "updated_at": "2025-09-27T15:30:00Z"
  }
}

AI Agent Endpoints

Score Resume

POST /api/agents/resume-scorer
Authorization: Bearer <token>
Content-Type: application/json

{
  "resumeText": "John Doe, Software Engineer with 5 years of experience in JavaScript, React, Node.js, Python...",
  "jobDescription": "We are seeking a Full-Stack Developer with 3+ years of experience in JavaScript frameworks..."
}

Success Response (200):

{
  "success": true,
  "task": "score-resume",
  "executionTime": 1234,
  "data": {
    "agentType": "resume-scorer",
    "score": 87,
    "matchedSkills": ["JavaScript", "React", "Node.js", "Python"],
    "missingSkills": ["Docker", "AWS"],
    "recommendations": [
      "Excellent match! Your resume aligns well with this job posting",
      "Great match on these skills: JavaScript, React, Node.js - make sure they're prominent",
      "Consider highlighting these missing skills if you have them: Docker, AWS"
    ],
    "details": {
      "skillMatch": {
        "score": 90,
        "matched": ["JavaScript", "React", "Node.js"],
        "missing": ["Docker", "AWS"]
      },
      "experienceMatch": {
        "score": 100,
        "resumeYears": 5,
        "requiredYears": 3
      },
      "educationMatch": {
        "score": 75
      },
      "keywordMatch": {
        "score": 85
      }
    }
  }
}

Generate Answers

POST /api/agents/generate-answers
Authorization: Bearer <token>
Content-Type: application/json

{
  "resumeText": "Software Engineer with expertise in JavaScript...",
  "jobDescription": "Looking for a developer to join our team...",
  "questions": [
    "Why are you interested in this role?",
    "What makes you a good fit for this position?",
    "What is your greatest strength?"
  ]
}

Success Response (200):

{
  "success": true,
  "task": "generate-answers",
  "executionTime": 2100,
  "data": {
    "agentType": "answer-generator",
    "questions": [
      "Why are you interested in this role?",
      "What makes you a good fit for this position?",
      "What is your greatest strength?"
    ],
    "answers": [
      "I'm excited about this opportunity because it aligns perfectly with my technical expertise in JavaScript, React, and Node.js...",
      "I believe I'm an excellent fit for this role because of my strong background in full-stack development...",
      "My greatest strength is my ability to combine technical skills with strong problem-solving capabilities..."
    ],
    "tips": [
      "Customize each answer to reflect the specific company and role",
      "Use specific examples from your experience when possible",
      "Keep answers concise but comprehensive (2-3 minutes when spoken)"
    ]
  }
}

Extract Profile

POST /api/agents/extract-profile
Authorization: Bearer <token>
Content-Type: application/json

{
  "resumeText": "John Doe\njohn.doe@example.com\n555-1234\nSoftware Engineer\n\nEXPERIENCE:\nSenior Developer at Tech Corp..."
}

Success Response (200):

{
  "success": true,
  "task": "prepare-autofill",
  "executionTime": 890,
  "data": {
    "agentType": "autofill-agent",
    "profileData": {
      "personalInfo": {
        "firstName": "John",
        "lastName": "Doe",
        "fullName": "John Doe"
      },
      "contact": {
        "email": "john.doe@example.com",
        "phone": "555-1234",
        "linkedin": null,
        "github": null
      },
      "workExperience": [
        {
          "title": "Senior Developer",
          "company": "Tech Corp",
          "startDate": "2020",
          "endDate": "Present"
        }
      ],
      "education": [
        {
          "degree": "Bachelor of Science",
          "school": "University of Technology",
          "major": "Computer Science",
          "graduationYear": "2018"
        }
      ],
      "skills": {
        "programming": ["JavaScript", "Python"],
        "frameworks": ["React", "Node.js"],
        "databases": ["PostgreSQL", "MongoDB"],
        "tools": ["Git", "Docker"]
      }
    },
    "confidence": 85,
    "extractedFields": [
      "personalInfo.firstName",
      "personalInfo.lastName",
      "contact.email",
      "contact.phone",
      "workExperience",
      "education",
      "skills.programming"
    ]
  }
}

Full Analysis (Multi-Agent Workflow)

POST /api/agents/full-analysis
Authorization: Bearer <token>
Content-Type: application/json

{
  "resumeText": "Complete resume content...",
  "jobDescription": "Complete job description..."
}

Success Response (200): Combines results from all three agents

Dashboard Endpoints

Dashboard Overview

GET /api/dashboard/overview
Authorization: Bearer <token>

Success Response (200):

{
  "success": true,
  "data": {
    "summary": {
      "totalApplications": 15,
      "totalResumes": 3,
      "statusBreakdown": {
        "not_submitted": 2,
        "submitted": 8,
        "interview_requested": 3,
        "rejected": 2
      }
    },
    "recentActivity": [
      {
        "id": 15,
        "company_name": "Tech Corp",
        "job_title": "Software Engineer",
        "status": "interview_requested",
        "created_at": "2025-09-27T10:00:00Z",
        "updated_at": "2025-09-28T14:30:00Z"
      }
    ],
    "agentUsage": [
      {
        "agent_type": "score-resume",
        "usage_count": "25",
        "avg_score": "78.5"
      }
    ]
  }
}

Status Analytics

GET /api/dashboard/analytics/status?timeframe=30
Authorization: Bearer <token>

Query Parameters:
  - timeframe: Number of days (default: 30)

Success Response (200):

{
  "success": true,
  "data": {
    "timeSeriesData": [...],
    "statusDistribution": [
      { "status": "submitted", "count": "10" },
      { "status": "interview_requested", "count": "5" }
    ],
    "timeframe": "30 days"
  }
}

Application Pipeline

GET /api/dashboard/pipeline
Authorization: Bearer <token>

Success Response (200):

{
  "success": true,
  "data": {
    "pipeline": [
      {
        "stage": "not_submitted",
        "count": 2,
        "applications": [...]
      },
      {
        "stage": "submitted",
        "count": 8,
        "applications": [...]
      }
    ],
    "rejected": 3
  }
}

Error Responses

All error responses follow this format:

{
  "success": false,
  "message": "Error description"
}

Common HTTP Status Codes:

200 - Success
201 - Created
400 - Bad Request (invalid input)
401 - Unauthorized (missing/invalid token)
404 - Not Found
409 - Conflict (duplicate resource)
500 - Internal Server Error

🤖 Agentic AI System

Architecture Overview

The system implements an Agentic AI architecture where multiple specialized agents work together under the coordination of a central orchestrator. This design pattern provides:

Modularity: Each agent is self-contained and focused on a specific task
Reusability: Agents can be combined in different workflows
Scalability: New agents can be added without affecting existing ones
Maintainability: Easy to update individual agents
Testability: Agents can be tested independently

Agent Orchestrator

Location: src/agents/orchestrator/index.js

The orchestrator is the central coordinator that:

Receives task requests from API endpoints
Determines which agent(s) to invoke
Manages agent execution (sequential or parallel)
Aggregates and formats results
Stores results in the database
Returns formatted responses

Key Methods:

executeTask(task, context)  // Main entry point
scoreResumeWorkflow()        // Single agent workflow
generateAnswersWorkflow()    // Multi-agent sequential
fullAnalysisWorkflow()       // Multi-agent parallel

Individual Agents

1. Resume Scorer Agent

Location: src/agents/resume-scorer/index.js

Purpose: Analyzes resume against job description and provides match scoring

Algorithm:

1. Extract skills from resume and job description
2. Calculate skill match percentage (40% weight)
3. Analyze experience level match (30% weight)
4. Evaluate education match (15% weight)
5. Calculate keyword density (15% weight)
6. Generate overall score (0-100)
7. Provide recommendations

Input:

resumeText: String containing resume content
jobDescription: String containing job posting

Output:

{
  score: 85,  // Overall match score
  matchedSkills: ["JavaScript", "React"],
  missingSkills: ["Docker", "AWS"],
  recommendations: ["..."],
  details: {
    skillMatch: {...},
    experienceMatch: {...},
    educationMatch: {...},
    keywordMatch: {...}
  }
}

Key Features:

Keyword-based skill extraction
Experience years calculation
Education requirement matching
Actionable recommendations

2. Answer Generator Agent

Location: src/agents/answer-generator/index.js

Purpose: Generates personalized responses to application questions

Algorithm:

1. Analyze question type (interest, fit, strength, experience)
2. Extract relevant context from resume and job description
3. Generate answer using template + customization
4. Provide improvement tips

Input:

resumeText: Resume content
jobDescription: Job posting
questions: Array of questions
resumeAnalysis: Optional analysis from Resume Scorer

Output:

{
  questions: ["Why are you interested?", ...],
  answers: ["I'm excited because...", ...],
  tips: ["Customize each answer...", ...]
}

Question Types Supported:

Interest/motivation questions
Fit/qualification questions
Strength/skills questions
Experience/background questions
Generic application questions

3. Autofill Agent

Location: src/agents/autofill-agent/index.js

Purpose: Extracts structured profile data from resume text

Algorithm:

1. Parse resume text by sections
2. Extract personal information (name, contact)
3. Identify work experience entries
4. Parse education details
5. Categorize skills by type
6. Calculate extraction confidence

Input:

resumeText: Resume content

Output:

{
  profileData: {
    personalInfo: {...},
    contact: {...},
    workExperience: [...],
    education: [...],
    skills: {...}
  },
  confidence: 85,
  extractedFields: [...]
}

Extraction Patterns:

Email: Regex pattern matching
Phone: Multiple format support
Dates: Various date format parsing
Skills: Keyword matching against common skills database
Education: Degree and institution extraction

Workflow Patterns

1. Single Agent Workflow

User Request → Orchestrator → Single Agent → Result → Response

Example: Resume Scoring

2. Sequential Multi-Agent Workflow

User Request → Orchestrator → Agent A → Agent B (uses A's output) → Result

Example: Answer Generation (uses Resume Scorer results)

3. Parallel Multi-Agent Workflow

User Request → Orchestrator → [Agent A, Agent B, Agent C] → Aggregate → Result

Example: Full Analysis (runs all agents simultaneously)

Agent Communication

Agents communicate through standardized data structures:

// Agent Input (Context)
{
  userId: 123,
  resumeText: "...",
  jobDescription: "...",
  // ... task-specific data
}

// Agent Output

Name		Name	Last commit message	Last commit date
Latest commit History 12 Commits
node_modules		node_modules
src		src
.gitignore		.gitignore
API endpoints.ini		API endpoints.ini
API endpoints.md		API endpoints.md
API endpoints.mot		API endpoints.mot
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
parser.js		parser.js
sample-resume.pdf		sample-resume.pdf
test-agent.js		test-agent.js
test-all-endpoints.ps1		test-all-endpoints.ps1

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Job Assistant AI - Backend System

📑 Table of Contents

🎯 Overview

The Problem We Solve

Our Solution

✨ Key Features

1. Resume Analysis & Scoring

2. AI-Powered Answer Generation

3. Profile Autofill

4. Application Tracking

5. Dashboard & Analytics

6. Secure Authentication

🏗️ System Architecture

High-Level Architecture

Request Flow Example

Agentic Architecture Principles

🛠️ Technology Stack

Backend Core

Database

Authentication & Security

File Handling

Development Tools

🚀 Getting Started

Prerequisites

Installation Steps

1. Clone/Download the Project

2. Install Dependencies

3. Set Up PostgreSQL Database

4. Configure Environment Variables

5. Start the Server

6. Verify Installation

📚 API Documentation

Base URL

Authentication

Authentication Endpoints

Register User

Login

Resume Management Endpoints

Upload Resume

Get User's Resumes

Get Resume Details

Delete Resume

Job Application Endpoints

Create Application

Get User's Applications

Update Application Status

AI Agent Endpoints

Score Resume

Generate Answers

Extract Profile

Full Analysis (Multi-Agent Workflow)

Dashboard Endpoints

Dashboard Overview

Status Analytics

Application Pipeline

Error Responses

🤖 Agentic AI System

Architecture Overview

Agent Orchestrator

Individual Agents

1. Resume Scorer Agent

2. Answer Generator Agent

3. Autofill Agent

Workflow Patterns

1. Single Agent Workflow

2. Sequential Multi-Agent Workflow

3. Parallel Multi-Agent Workflow

Agent Communication

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