The tech world is in deep with a question that brings shudders to some and evokes excitement in others: Can AI Software Engineering possibly take over the human software engineers? Although the idea of fully automated development teams may still look unrealistic to many, the fast developments in AI Software Engineering are no doubt turning it into an even more possible thing. Moving on to the point, we want to investigate thoroughly the shining and dark sides of this great debate, and with that in mind, we want to have a close look at the MGX (Meta GPTX) machine learning community and also the ways it has already been influencing the world of software development. In this way, it’s possible to illustrate step by step how MGX can be utilised in the creation of a developer portfolio website, which in turn is meant to uncover AI Software Engineering’s potential for simplifying and accelerating the development process. So, terrific, fasten your seat belts, and let’s make an exploration to find out the new role of AI in software engineering — replacement, a tool, or something else?
Let’s dive into one fascinating example: the MGX (Meta GPTX) platform, where a full AI-powered software engineering team was tested on a real-world project.
What is MGX?
MGX is an advanced Multi-Agent AI stage where a set of individual AI agents are on the team and they work together in a very organized way, mirroring the functions and procedures of a professional software company. Each agent has a distinct role, mimicking a real software team:
Team Leader (Mike) – Manages communication and requirements
Product Manager (Emma) – Gathers needs and creates product documents
Architect (Bob) – Designs the technical framework
Full Stack Developer (Alex) – Builds the actual product
Data Analyst (David) – Validates and improves the system
Together, they function just like a human development team — but powered entirely by AI.
Real Test: Building a Portfolio Website
We challenged the MGX AI software engineering team to build a responsive portfolio website.
Here’s a detailed walkthrough of what happened:
1. Requirements Gathering
The Team Leader (Mike) initiated the project by defining the basic structure:
- Hero Section with name and tagline
- About Section with a bio
- Skills Section with tech stack
- Projects Gallery
- Contact Form
2. Product Documentation
The Product Manager (Emma) produced a detailed Product Requirements Document (PRD), outlining the core features, user flows, and technical constraints:
- User needs
- Functional and non-functional requirements
- Technology choices (Tailwindcss, React.js)
3. Architecture Planning
The Architect (Bob) designed the technical blueprint:
- Component hierarchy
- API integration plan (if needed)
- Database and hosting considerations
4. Development Phase
The Full Stack Developer (Alex) took the lead:
- Built the front-end with Tailwind CSS for fast, responsive styling.
- Implemented animations and responsiveness
- Structured reusable components for easy updates
5. Quality Testing and Improvements
The Data Analyst (David) reviewed the product:
- Identified bugs like image loading failures and form validation errors.
- Suggested code optimisations
- Ensured accessibility standards (ARIA roles, alt text)
After several iterations among the agents, the final website was polished, fully responsive, and ready for production deployment — all without human intervention!
Advantages of AI Software Engineering
Working with an AI Software Engineering team showed major advantages:
Speed: The project was completed in hours, not days.
Cost Efficiency: No human salaries, no HR processes.
Consistency: AI maintained perfect coding standards throughout.
24/7 Productivity: No breaks, no holidays, non-stop execution.
Limitations and Challenges
Despite the impressive output, some clear limitations still exist:
Creativity Gaps:
AI often struggles with truly innovative ideas that defy conventional patterns.
Empathy and UX Understanding:
AI lacks the human ability to understand emotional needs, aesthetics, or cultural sensitivities.
Complex Customisation:
Highly complex business logics or abstract problem-solving still require human ingenuity.
Accountability:
When errors occur, accountability and troubleshooting require human oversight.
In short, AI is brilliant at following instructions, but not (yet) great at creative leadership.
Will AI Software Engineering Replace Human Developers?
- The future of AI in software engineering isn’t about replacing developers, but enhancing their capabilities.
- Routine tasks like frontend coding, documentation, and testing will increasingly be handled by AI.
- Developers will focus more on creative architecture, human-centred design, ethical considerations, and advanced problem-solving.
Humans + AI is the winning formula for the next decade.
What’s Next for AI Software Engineering?
We can expect:
AI as Junior Developers: AI handling grunt work, freeing up senior developers.
Smart Code Assistants: Advanced pair programming with AI.
Fully Automated DevOps: AI managing deployments, monitoring, and scaling.
AI Project Managers: Planning sprints and tasks based on historical data.
The era of AI-assisted software teams has begun — and those who learn to work alongside AI will thrive.
Conclusion
AI Software Engineering is no longer just theory; it’s real, practical, and rapidly improving.
Platforms like MGX show that AI teams can successfully build production-ready software.
However, the human developer’s creativity, empathy, ethical judgment, and innovation are still irreplaceable.
Instead of fearing AI, the smartest move is to embrace AI as your new coding partner —Because the future belongs to developers who know how to collaborate with machines.
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