Multi-Agent System

Parallel development with multiple AI agents

Portfolio OS includes a sophisticated multi-agent development system that enables parallel development by multiple AI agents working simultaneously on different features.

Overview

The multi-agent system uses Git worktrees to create isolated development environments where each agent can work independently without conflicts. This dramatically accelerates development by allowing multiple features to be built in parallel.

Key Benefits

  • Parallel Development: Multiple agents work simultaneously on different features
  • Zero Conflicts: Each agent has its own isolated workspace
  • Easy Integration: Merge completed work through standard PR process
  • State Management: Track agent status and task assignments
  • Coordination: Automated orchestration manages agent workflows

How It Works

Core Components

1. Git Worktrees

Git worktrees create separate working directories that share the same repository. The system organizes agent workspaces in a structured hierarchy where each agent has a dedicated, isolated working copy with its own feature branch, while all workspaces share a common Git database.

2. Coordination Layer

Automated orchestration manages the multi-agent system:

  • Workspace Management - Create and configure isolated agent environments
  • System Coordination - Orchestrate multiple agents working in parallel
  • Workflow Automation - Execute complete development workflows end-to-end
  • Progress Tracking - Monitor agent status and task completion

3. Task Assignment

Agents are assigned specific tasks based on their specialization:

Assignment Criteria

  • Role Matching: Frontend tasks → Frontend specialists, Backend → Backend specialists
  • Clear Boundaries: Each agent has distinct, non-overlapping responsibilities
  • Tracked Status: All assignments are monitored and tracked
  • Branch Isolation: Each agent works on a dedicated feature branch

4. State Synchronization

The system maintains comprehensive state tracking:

Tracked Information

  • Current branch and workspace location
  • Assigned tasks and priorities
  • Work status progression (pending → in-progress → review → completed)
  • Dependencies and blockers

Typical Workflow

1

Create Agent Worktrees

The system initializes isolated workspaces for each agent:

  • Each agent receives a dedicated worktree
  • Workspaces are configured with proper isolation
  • Dependencies are installed automatically
  • Agents are ready to begin work
2

Assign Tasks to Agents

Each agent receives specific issues or features to implement:

  • Tasks are assigned based on agent specialization
  • Assignments are tracked in system configuration
  • Clear boundaries prevent overlap
  • Dependencies are identified upfront
3

Agents Work in Parallel

Each agent works independently in their isolated workspace:

  • Work proceeds in isolated worktrees
  • Commits are made to dedicated feature branches
  • No conflicts occur during development
  • Progress is monitored continuously
4

Create Pull Requests

Pull requests are generated automatically when work completes:

  • PRs include comprehensive descriptions
  • Testing and validation are triggered
  • Reviewers are assigned appropriately
  • Integration process begins
5

Review and Merge

PRs flow through the standard review process:

  • Code review ensures quality
  • Tests validate functionality
  • PRs merge to develop branch independently
  • Agents sync and become available for new work

Use Cases

Parallel Feature Development

Two agents implement different features simultaneously:

  • Agent 1: User authentication system
  • Agent 2: Dashboard analytics

Bug Fixes + Features

Mix urgent fixes with feature work:

  • Agent 1: Critical security patch
  • Agent 2: New feature development

Experimentation

Try different approaches in parallel:

  • Agent 1: Implement solution A
  • Agent 2: Implement solution B
  • Compare and choose the best approach

Getting Started

Ready to use the multi-agent system?

Quick Start Guide

Set up your first multi-agent workflow

System Architecture

Deep dive into how it works

Agent Scripts

Available automation scripts

Best Practices

Note:

Tips for Effective Multi-Agent Development:

  1. Clear task boundaries - Minimize overlap between agents
  2. Regular syncing - Keep worktrees updated with develop
  3. Status tracking - Update agent status regularly
  4. Communication - Document decisions in PRs
  5. Small PRs - Easier to review and merge

Limitations

Note:

Be aware of these constraints:

  • Disk space: Each worktree is a full copy (~100MB each)
  • Coordination overhead: More agents = more coordination needed
  • Merge conflicts: Can still occur if agents touch same files
  • Resource usage: Multiple Node processes if running dev servers

Next Steps