The latest articles on Forem by Rudra (@rudtes). https://forem.com/rudtes https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3811666%2Ff0f8e31d-85be-4a11-9f7e-87178a22ca5a.png https://forem.com/rudtes en Rudra Sat, 07 Mar 2026 14:42:07 +0000 https://forem.com/rudtes/designing-a-12-agent-ai-coding-architecture-open-spec-310m https://forem.com/rudtes/designing-a-12-agent-ai-coding-architecture-open-spec-310m <p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F1oop0ijdzias9beqdff9.jpg" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F1oop0ijdzias9beqdff9.jpg" alt=" " width="680" height="340"></a>Most AI coding tools today rely on a single large language model to generate everything. That approach works for small tasks, but it often produces fragmented results when projects become complex.</p> <p>To explore a different direction, I designed a <strong>multi-agent AI coding architecture</strong> that mirrors how real software teams operate.</p> <p>Instead of one model doing everything, the system is structured around specialized roles that collaborate through routing and orchestration.</p> <h2> Core Idea </h2> <p>The architecture simulates a development team using role-based agents:</p> <ul> <li> <strong>Leader Agent</strong> — task decomposition and coordination</li> <li> <strong>Builder Agent</strong> — implementation and code generation</li> <li> <strong>UI/UX Agent</strong> — interface structure and design decisions</li> <li> <strong>Security Agent</strong> — vulnerability review and safety checks</li> <li> <strong>Tester Agent</strong> — validation and testing logic</li> <li> <strong>Memory Keeper</strong> — persistent context and system memory</li> </ul> <p>All of these agents communicate through structured <strong>LLM routing</strong>.</p> <p>At the center of the system is something called a <strong>Living Feature Tree</strong> — a structured map of features and tasks that acts as the single source of truth for the project.</p> <h2> Why This Architecture </h2> <p>The goal is to move beyond fragmented outputs and explore a <strong>production-style workflow for AI development</strong>.</p> <p>Real engineering teams separate responsibilities, validate outputs, and coordinate work through shared planning structures. This design attempts to replicate those patterns in an AI-driven environment.</p> <h2> Open Design Spec </h2> <p>This project is not a startup or product.<br> It’s an <strong>open architecture specification</strong> created as a reference for developers, researchers, or builders who want to experiment with multi-agent systems.</p> <p>The full design document is available in a single Markdown repository.</p> <h2> Looking for Feedback </h2> <p>I’d especially appreciate feedback on a few points:</p> <ol> <li>Does separating responsibilities into multiple agents make sense architecturally?</li> <li>What would be the best strategy for routing tasks between LLM agents?</li> <li>Could a <strong>Living Feature Tree</strong> realistically function as the coordination backbone?</li> </ol> <p>If you work on AI tooling, orchestration systems, or developer automation, your perspective would be valuable.</p> <p>Thanks for reading.</p> <p><a href="https://github.com/rudtest6/vibe-coding-architecture.git" rel="noopener noreferrer">Project link</a></p> ai agents vibecoding programming