🤖 AI Engineering Team

A multi-agent AI software engineering system built with CrewAI, Python, and OpenAI that simulates how a real engineering team collaborates to design, develop, review, and test software.

Instead of relying on a single AI agent to complete an entire software project, this system divides responsibilities among specialized agents, producing more structured, maintainable, and realistic software development workflows.

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Overview

Modern software development involves multiple specialists working together rather than one person doing everything.

This project recreates that workflow using AI agents.

Given a software request such as:

"Build a Trading Simulation Platform"

the system automatically coordinates multiple AI agents that collaborate to:

• Understand requirements
• Design the solution
• Generate production-ready code
• Review implementation quality
• Test the generated application

Each agent performs a dedicated responsibility before passing its output to the next agent, closely simulating a real software engineering team.

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Architecture

                    User Request
                          │
                          ▼
              Engineering Lead Agent
        (Requirement Analysis & Planning)
                          │
                          ▼
               Frontend Engineer Agent
               (UI Implementation)
                          │
                          ▼
               Backend Engineer Agent
          (Business Logic & APIs)
                          │
                          ▼
                Testing Engineer Agent
          (Validation & Quality Assurance)
                          │
                          ▼
                 Final Software Output

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Docker

Build

docker build -t ai-engineering-team .

Run

docker run -e OPENAI_API_KEY=YOUR_OPENAI_API_KEY ai-engineering-team

The application launches the CrewAI multi-agent workflow inside a Docker container.

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AI Agents

Engineering Lead

Responsible for:

• Understanding user requirements
• Breaking complex problems into development tasks
• Planning implementation strategy
• Coordinating the engineering workflow

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Frontend Engineer

Responsible for:

• UI development
• User experience
• Component generation
• Frontend architecture

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Backend Engineer

Responsible for:

• Business logic
• API implementation
• Database interactions
• Backend architecture

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Testing Engineer

Responsible for:

• Reviewing generated code
• Identifying defects
• Testing functionality
• Validating software quality

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Technology Stack

• Python
• CrewAI
• OpenAI GPT Models
• Docker
• YAML Configuration
• UV Package Manager

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Workflow

1. User submits a software development request.

2. Engineering Lead analyzes the requirements.

3. Frontend Engineer develops the user interface.

4. Backend Engineer builds the application logic.

5. Testing Engineer validates the final implementation.

6. Final software solution is produced.

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Project Structure

ai-engineering-team/

├── knowledge/
├── sandbox/
├── src/
│   └── engineering_team/
│       ├── config/
│       │   ├── agents.yaml
│       │   └── tasks.yaml
│       ├── tools/
│       ├── crew.py
│       └── main.py
├── AGENTS.md
├── pyproject.toml
└── README.md

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Installation

Prerequisites

• Python 3.12 or 3.13
• UV Package Manager
• OpenAI API Key

Clone the repository:

git clone https://github.com/NeelPawar-01/ai-engineering-team.git
cd ai-engineering-team

Install UV (if not already installed):

py -m pip install uv

Install project dependencies:

py -m uv sync

Create a .env file in the project root:

OPENAI_API_KEY=your_api_key_here

Run the project:

crewai run

Note: Some Windows environments may experience issues with the CrewAI CLI launcher. If this occurs, run the project from the virtual environment or directly through the Python entry point.

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Example Input

Build a Trading Simulation Platform

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Example Workflow

Engineering Lead
        ↓
Requirement Analysis

        ↓

Frontend Engineer
        ↓
User Interface

        ↓

Backend Engineer
        ↓
Business Logic

        ↓

Testing Engineer
        ↓
Validation

        ↓

Final Software

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Skills Demonstrated

• Multi-Agent AI Systems
• Agent Orchestration
• LLM Workflow Design
• CrewAI Framework
• Software Architecture
• AI-assisted Software Engineering
• Prompt Engineering
• Python Development
• AI Collaboration Patterns

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Future Improvements

• Local LLM support using Ollama
• CI/CD integration
• Automated code deployment
• GitHub Actions pipeline
• Human-in-the-loop approval workflow
• Multi-model support
• Performance monitoring

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About

This project was developed to explore how specialized AI agents can collaboratively perform software engineering tasks by simulating real-world engineering team workflows using CrewAI.

It demonstrates principles of agent orchestration, autonomous task delegation, and collaborative AI systems for modern software development.