LOE Agent - AI-Powered Effort Estimation & Sprint Planning A full-stack application combining an AI agent with a modern web dashboard to predict project effort levels, analyze team capacity, and provide data-driven sprint planning recommendations.
🎯 Overview The LOE (Level of Effort) Agent uses OpenAI's GPT models to intelligently estimate project effort by analyzing:
Calendar availability (working days, holidays) Team capacity and meeting overhead Project task type and complexity Historical utilization patterns Result: Risk assessment, effort predictions, and actionable AI suggestions for sprint planning.
📁 Project Structure 🚀 Quick Start Prerequisites Python 3.8+ Node.js 16+ OpenAI API Key (get one here) Backend Setup Navigate to backend directory
Create .env file with your API keys
Install dependencies
Run the API server
Server runs at: http://localhost:8000
Frontend Setup Navigate to frontend directory
Install dependencies
Run development server
Dashboard opens at: http://localhost:5173
🔧 API Reference Health Check Returns server status.
Analyze Effort Response:
📊 Dashboard Features Sprint Configuration: Input working days, holidays, capacity, and meetings Real-time Calculations: Instant LOE estimates with visual feedback Risk Assessment: Color-coded risk levels (🟢 Low / 🟡 Medium / 🔴 High) Utilization Tracking: Visual progress bar showing capacity utilization AI Suggestions: 3 actionable recommendations from the AI agent Responsive Design: Works on desktop and mobile devices 🤖 How It Works User Input: Enter sprint parameters (days, holidays, capacity, meetings, task type) Calculation: Backend calculates working days and base LOE in hours AI Analysis: GPT-4o-mini analyzes the data and generates: Meeting overhead estimation Adjusted LOE after accounting for meetings Risk classification (low/medium/high) Team-specific recommendations Results Display: Dashboard visualizes metrics and risk level 🛠️ Configuration Supported Task Types development testing design research deployment planning Risk Thresholds Low: < 70% utilization Medium: 70-90% utilization High: > 90% utilization 📦 Dependencies Backend:
fastapi - Web framework uvicorn - ASGI server openai - GPT API client pydantic - Data validation python-dotenv - Environment variables Frontend:
react - UI library vite - Build tool Pure CSS (no external UI library) 🔐 Security Notes Keep .env files private (listed in .gitignore) Never commit API keys to version control Use environment variables for sensitive data CORS enabled for local development only 📈 Example Usage Input:
30-day sprint 8 holiday days 2 extra holidays 80% team capacity 3 meetings/week Development task type Output:
🐛 Troubleshooting Backend connection error?
Ensure backend is running on http://localhost:8000 Check firewall/port availability API key error?
Verify OPEN_AI_KEY is set correctly in .env Check OpenAI account has active billing No suggestions appearing?
Verify GPT-4o-mini model is available in your OpenAI account Check API response in browser DevTools 📝 License Internal project - use for team planning and estimation.
👤 Author Built with ❤️ for intelligent sprint planning