Back to Blog

How Multi-Agent AI Actually Works: The BuildGreenfield Approach

Bálint Horváth
technical multi-agent ai-architecture transparency

How Multi-Agent AI Actually Works: The BuildGreenfield Approach

A deep dive into how BuildGreenfield’s multi-agent system creates transparent, familiar AI development workflows that teams can trust and understand.

The Problem with Traditional AI Development

Most AI development tools treat the process as a black box. You input a prompt, wait, and code appears. But that’s not how real development works.

Real development teams have:

  • Specialized roles (PM, backend, frontend, database)
  • Clear communication (standups, reviews, documentation)
  • Transparent workflows (kanban boards, activity feeds)
  • Familiar tools (project management, version control)

The Multi-Agent Solution

BuildGreenfield simulates how exceptional development teams actually work - with specialized AI agents for different roles, clear communication protocols, and transparent workflows managed on a visual kanban board.

The Agent Team

PM Agent

Role: Project management and requirements

  • Analyzes project descriptions
  • Breaks down features into tasks
  • Creates user stories and acceptance criteria
  • Manages project timeline and priorities

Backend Agent

Role: Server-side development

  • Creates API endpoints and business logic
  • Implements authentication and authorization
  • Sets up database connections and queries
  • Handles data validation and error handling

Frontend Agent

Role: User interface development

  • Creates components and layouts
  • Implements user interactions
  • Connects to backend APIs
  • Handles state management and routing

Database Agent

Role: Data architecture and management

  • Designs database schemas
  • Creates migrations and indexes
  • Optimizes queries and performance
  • Manages data relationships and constraints

How They Work Together

1. Project Planning Phase

PM Agent starts by analyzing your project description:

“Build a task management app for small teams”

Output: Detailed project plan with features, user stories, and technical requirements

2. Architecture Design

PM Agent collaborates with Backend Agent to design the system:

  • API endpoints needed
  • Database schema requirements
  • Authentication strategy
  • Data flow between components

3. Development Phase

Each agent works on their specialized area:

Backend Agent creates:

  • REST API endpoints
  • Business logic
  • Authentication middleware
  • Database models

Frontend Agent creates:

  • React components
  • User interfaces
  • API integration
  • State management

Database Agent creates:

  • Database schema
  • Migration files
  • Indexes and constraints
  • Seed data

4. Integration & Testing

All agents coordinate to ensure:

  • Frontend connects properly to backend APIs
  • Database schema matches application needs
  • Authentication flows work end-to-end
  • All components integrate seamlessly

The Transparency Layer

Kanban Board Visualization

Instead of mysterious AI processing, you see your project evolve on a familiar kanban board:

BACKLOG → TODO → IN PROGRESS → DONE

Tasks move through these stages as agents work on them, just like any development team.

Agent Activity Feed

See exactly what each agent is doing:

  • PM Agent: “Creating user stories for task management”
  • Backend Agent: “Implementing task CRUD endpoints”
  • Frontend Agent: “Building task list component”
  • Database Agent: “Creating tasks table schema”

Project Dashboard

Track progress with familiar project management tools:

  • Burndown charts
  • Progress indicators
  • Timeline views
  • Resource allocation

Communication Protocols

Inter-Agent Communication

Agents communicate through structured protocols:

  • API contracts: Backend defines endpoints, frontend consumes them
  • Data schemas: Database defines structure, all agents use it
  • Component interfaces: Frontend defines props, backend provides data
  • Error handling: Consistent error responses across all agents

Human-Agent Communication

You can chat with any agent:

  • PM Agent: “Add a deadline feature to tasks”
  • Backend Agent: “Make the API more RESTful”
  • Frontend Agent: “Improve the mobile layout”
  • Database Agent: “Add an index for better performance”

The Familiar Tools Approach

Why This Works

Instead of learning new AI-specific workflows, you use tools you already know:

  • Kanban boards (like Jira, Trello)
  • Activity feeds (like Slack, GitHub)
  • Project dashboards (like Monday.com, Asana)
  • Chat interfaces (like Discord, Teams)

The Learning Curve

There’s no learning curve because you’re not learning new tools - you’re using familiar tools that happen to be powered by AI agents instead of human developers.

Real-World Example

Let’s say you want to build a simple blog platform:

1. PM Agent Analysis

Project: Blog Platform
Features:
- User authentication
- Post creation and editing
- Comment system
- Admin dashboard

Tasks Created:
- Set up user authentication
- Create post management system
- Implement comment functionality
- Build admin interface

2. Development Process

Backend Agent creates:

  • User registration/login endpoints
  • Post CRUD operations
  • Comment system APIs
  • Admin authentication

Frontend Agent creates:

  • Login/signup forms
  • Post editor interface
  • Comment display components
  • Admin dashboard

Database Agent creates:

  • Users table with authentication
  • Posts table with relationships
  • Comments table with foreign keys
  • Admin permissions system

3. Integration

All agents coordinate to ensure:

  • Frontend forms connect to backend APIs
  • Database relationships work correctly
  • Authentication flows are seamless
  • Admin permissions are properly enforced

The Meta-Proof

What’s fascinating is that BuildGreenfield itself demonstrates these principles:

  • Multi-agent collaboration: Different AI agents working together
  • Transparent workflows: You can see every change and decision
  • Familiar tools: Using standard development practices
  • Rapid iteration: The platform evolves constantly

Why This Matters

For Enterprise Teams

  • Transparency: See exactly what AI is doing
  • Control: Review and approve every change
  • Familiarity: Use tools you already know
  • Reliability: Consistent, predictable results

For Solo Entrepreneurs

  • Speed: Build MVPs in days, not months
  • Understanding: Know what’s being built and why
  • Control: Make changes without technical expertise
  • Confidence: Trust the development process

For Developers

  • Efficiency: Skip boilerplate, focus on features
  • Transparency: See AI decisions and reasoning
  • Familiarity: Use standard development workflows
  • Learning: Understand how AI approaches problems

The Future

This multi-agent approach is just the beginning. We’re constantly improving:

  • Smarter agents: Better understanding of requirements
  • More specialization: Additional agent types for specific domains
  • Better coordination: Improved inter-agent communication
  • Human-in-the-loop: More collaboration between humans and AI

Try It Yourself

Experience multi-agent AI development that makes sense: Request alpha access

See how AI development can be transparent, familiar, and trustworthy - just like working with a great development team.

This is how AI development should work - transparent, familiar, and trustworthy. The future of development is already here.