AutoPilot Dev Agent was inspired by the repetitive and time‑consuming setup work developers face whenever starting a new project. We realized that teams often lose valuable time creating folder structures, boilerplate code, and basic documentation instead of focusing on real innovation. This led us to imagine a tool that could automate the entire setup phase from a single prompt. Using the Gemini API, we built a multi‑agent system that takes any project idea and instantly transforms it into a complete, ready‑to-run codebase.
Our system uses three specialized AI agents working together: a Planner Agent that breaks the user’s idea into modules and outputs a clear JSON blueprint, a Coder Agent that generates runnable code for every required file, and a Reviewer Agent that analyzes the full project and offers improvement suggestions. We implemented a multi-agent pipeline in Python to orchestrate these interactions and built a Streamlit-based interface that lets users enter prompts, preview generated code, and download the final project.
Building AutoPilot Dev Agent taught us a lot about prompt engineering, reliable structured outputs, coordinating multiple AI agents, and designing AI workflows that balance flexibility with predictable results. We also encountered challenges, such as ensuring the Planner Agent consistently produced valid JSON and stabilizing the code generation process across different frameworks. Another challenge was feeding large generated codebases to the Reviewer Agent without losing coherence.
Looking ahead, we plan to expand AutoPilot Dev Agent with new capabilities such as automatic test generation, deployment‑ready configurations, support for more languages and frameworks, and real‑time collaborative generation. Our goal is to make it the ultimate AI assistant that helps developers move from idea to execution instantly.
Log in or sign up for Devpost to join the conversation.