We've all seen the posts and tutorials proclaiming that AI is getting so smart it's leaving developers in the dust. This experiment set out to put that claim to the test.
The goal was to build a fully autonomous AI team and evaluate whether it could handle a real-world project from start to finish. Hereβs a closer look at the experiment, the surprising results, and what it all means.
The Goal: An AI-Powered Workflow
The objective was to create a reusable workflow where a team of AI agents could manage daily development tasks, with human involvement limited to final validation.
The Team
- 1 Coordinator Agent
- 2 Developer Agents
- 2 Tester Agents
- 1 UX Agent
The Tool
Gemini-CLI was used, primarily due to its generous free tier. While other models might produce different outputs, the core challenges are likely to remain consistent.
The Setup: Building the AI Crew
Preparing the team was a project in itself. Here's the breakdown:
Agent Creation: Gemini was used to generate a profile for each role, defining core functions and relationships.
Skill Enhancement: Each profile was manually refined by adding project-specific capabilities and defining a consistent coding style.
Workflow Design: An initial process was generated and then refined to include key practices such as creating new Git branches, committing frequently, and maintaining progress.MD file to track tasks.
Rule Enforcement: A gemini.md file was created with strict guidelines for communication, task assignment, and coordinator behavior to minimize token usage.
User Stories First: To maintain focus, agents were required to write and store all user stories in a dedicated folder before starting development.
With everything in place, the team was assigned its first project: building a To-Do application with sorting, filtering, image uploads, and scheduling features. The requirements were provided, and the Coordinator agent was allowed to take full control.
The Execution: High Hopes and Harsh Realities
The initial phase was promising.
The AI team successfully:
- Created a logical folder structure
- Outlined requirements clearly
- Assigned tasks efficiently
- Provided time estimates
Planning appeared flawless.
Then, issues began to surface.
Tooling Challenges
Real-world development constraints quickly emerged. The agents became stuck in continuous processes triggered by tools like Vite, effectively blocking the workflow.
As a workaround, Docker was introduced. While this resolved the immediate issue, the solution felt more like a patch than a proper fix.
The Final Wall
The most critical failure occurred during the integration of a C# API with a React application.
Despite multiple attempts, the agents were unable to successfully connect the systems. Progress stalled completely, signaling the need for human involvement.
Conclusion: Are We There Yet?
This experiment provided several key insights:
AI as a Co-Pilot
AI proves to be highly effective as a development assistant. It excels at:
- Bootstrapping projects
- Handling isolated tasks
- Increasing overall productivity
- Not an Autonomous Team (Yet)
AI is not ready to function as a fully autonomous development team. Critical gaps remain in:
- Communication
- Context awareness
- Decision-making consistency
Software development extends beyond writing codeβit requires oversight, adaptability, and intuition, all of which remain inherently human strengths.
Final Thought
AI is not replacing developers.
It is becoming one of the most powerful tools available to them.
If this experiment was insightful and there is interest in exploring the source code or final output, further details can be shared. Happy coding!
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