The Swarm Protocol: Orchestrating 10 Autonomous Agents

There's a specific kind of satisfaction that comes from watching a complex system work exactly as designed. For a long time, my experience with AI development was pair-programming with a single agent. It was fast, but it still felt like I was holding its hand through every file.

I wanted to see what happened if I scaled that out.

I recently launched a session with ten agents working simultaneously on a single repository. The goal was to build a professional-grade web application from scratch—not just a few pages, but a complete, production-ready frontend system.

In just 7.5 hours, the system ripped through 81 professional work items. We're talking about a full data table system with virtual scrolling, a responsive grid, complex form validation, and interactive visualizations—all built, tested, and verified in parallel.

Here is how I organized the work to keep ten agents from stepping on each other's toes.

The Problem with Parallel AI

When you have more than two agents working on the same codebase, things usually collapse. You run into merge conflicts, agents losing track of their state after a restart, and inconsistent styling.

To make this work, I had to stop "prompting" and start building a real orchestration framework.

I use a hierarchical model: Agent Zero (the architect) audits the repo and sets the rules, and Worker Agents (the builders) execute the specific tasks. They communicate through four core markdown files that act as the single source of truth.

The System Artifacts

Consistency comes from the artifacts, not the prompts.

1. The Manifest (SWARM_MANIFEST.md)

This is the rulebook. It defines the tech stack, the design tokens (like my standard 12px border radius), and the "Forbidden Actions" (like modification of package.json). It ensures the code looks like it was written by the same person.

# Swarm Manifest
## Project Rules
- **Stack**: React 19, TypeScript, styled-components
- **Radius**: 12px (Standard), 8px (Buttons)
- **Imports**: React first, then Tokens, then Components
## Forbidden Actions
- Do not modify package.json
- Do not use inline hex colors

2. The Board (WORK_BOARD.md)

This is a live, markdown-based work queue. Agents have to "claim" a task by marking it as IN_PROGRESS and attaching their ID. If an item is locked, other agents move on. No collisions, no overlapping work.

# Work Board
| Task ID | Status | Assignee | Notes |
|---------|--------|----------|-------|
| 2.1-base-table | ✅ DONE | A-001 Phoenix | Created DataTable.tsx |
| 2.2-row-styles | 🔄 IN_PROGRESS | A-002 Nova | Styling + hover |
| 2.3-cell-format | 🔴 OPEN | - | Formatting utilities |

3. The Registry (AGENT_REGISTRY.md)

This solves the "memory loss" problem. Every agent has a codename—Storm, Bolt, Quasar. The registry tracks their current task and heartbeats. If an agent disconnects, it just looks up its codename and resumes where it left off.

# Agent Registry
| Agent ID | Codename | Status | Current Task | Heartbeat |
|----------|----------|--------|--------------|-----------|
| A-001 | Phoenix | 🟢 ACTIVE | 2.1-base-table | 10:45 AM |
| A-002 | Nova | 🟢 ACTIVE | 2.2-row-styles | 10:48 AM |

The Workflow

The cycle is direct: Register -> Claim -> Execute -> Verify -> Release.

Every task requires passing tests before it can be marked as DONE. By the time I checked the progress at lunch, the swarm had delivered a week's worth of engineering output.

The future of dev isn't just about "using AI." It's about building systems that let AI agents work together at scale. When you get the orchestration right, the velocity is staggering.

Interesting in the coordination spec? The full protocols are open in the repo. Reach out if you want to dive into the patterns.