From Single Agent to Specialized Teams
The shift from using a single AI coding assistant to orchestrating specialized agent teams has fundamentally changed how I approach software development. After years of building products—from initial MVPs to scaling production systems—I've learned that the same principles that make human development teams successful apply to AI agents as well.
The traditional approach has been heavily reliant on human labor, but if you haven't heard, that landscape is changing rapidly.
Over the past couple months, I've been using Claude Code to handle my coding changes. I've thoroughly enjoyed how it breaks out prompt requests into a set of tasks, and then allows you to review the code along the way and help guide it into the proper direction. For technical users, it provides benefits over some of the earlier code editors which tend to be better for non-technical users (cursor, windsurf, co-pilot).
That being said, there still is a lot of human oversight required to build products, especially if you want it to be quality work that is easily maintained and expanded upon. Until now, I had been using a single agent (just the built-in claude code, co-pilot, cursor, windsurf, etc) in my IDE to work through coding changes. However, in the past few days I found the /agents command in claude code.
The Vision: Specialized Agents, Unified Goal
Traditional development teams succeed because of specialization. You have frontend developers who live and breathe React, backend engineers who architect robust APIs, QA specialists who think in edge cases, and DevOps engineers who understand infrastructure at scale. My intended approach with Claude Code mirrors this structure, but with AI agents filling each role.
Currently, I've built agents for my projects that specialize in:
- React Development: Handles component architecture, state management, and modern frontend patterns
- Supabase Operations: Database design, real-time subscriptions, and authentication workflows
- Django REST Framework: API design, serialization, and backend architecture
- Quality Assurance: Test planning, edge case identification, and validation strategies
- Code Review: Standards enforcement, security audits, and optimization suggestions
Each agent operates with instructions that encode years of my product development experience. When a React agent consistently produces components with poor structure or continually repeats state management code, I update its prompt to emphasize proper standards which lead to a maintainable system. When the QA agent creates flaky tests for trivial edge cases, I enhance its testing methodology to focus more on actual usage cases that won't easily break with non-breaking code changes.
The problem with the single agent approach is that the instructions file becomes far too large and broad. It gets split up into several sections, however there is too much context scope for a single agent to handle everything and that in turn results in more extensive human reviews and more guidance required.
The Orchestration Challenge
Right now, I serve as the conductor of this orchestra. I formulate task requirements, determine which agents need to collaborate, and sequence their work to avoid conflicts. A typical workflow might involve:
- The Supabase agent creating the database migration and updating the frontend types
- The React agent building the service layer and components that consume the API
- The QA agent creating tests for the new coverage
- The Code Review agent auditing everything to ensure project standards are met
This orchestration role mirrors what I've been doing manually previously—analyzing requirements, sequencing development phases, managing dependencies, and ensuring quality at each step. The difference now is scale and consistency. What once required weeks of careful coordination can now happen in hours, with the same attention to detail and best practices I've developed through years of product development.
The Self-Serving Future
The natural evolution is automating the methodology I've developed over years of building products. I'm currently developing a project that will deploy this entire agent ecosystem as a self-serving system—essentially codifying my entire product development methodology into an automated pipeline. Imagine describing a product idea and watching as the system automatically applies all the lessons, patterns, and quality standards I've learned through hands-on experience:
- Breaks down requirements into specialized tasks
- Assigns work to appropriate agents
- Manages inter-agent communication and dependencies
- Deploys the final product
The technical challenges are significant, but the challenge is good. I'm able to build this incrementally, one step at a time, driving closer to making myself obsolete and my team of agents more useful in building products. Agent coordination requires sophisticated workflow management, shared context understanding and proper oversight for improvement. These are rapidly evolving concepts that the entire industry is still figuring out, so there is much evolution taking place constantly.
Building with AI agents has fundamentally changed how I approach software development. If you're experimenting with similar workflows or would like to build a product together, I'd love to see if we can collaborate.
