Building a 13-Agent Claude Team with Peer Review Workflow

A developer shared their setup for creating a 13-agent Claude team that handles marketing for their AI video generation platform, Fruityo. The system runs every 15 minutes with agents reviewing each other's work before anything reaches the human operator.

Architecture Components

The infrastructure uses:

• OpenClaw - provides agents with web browsing, command execution, file management, and API interaction capabilities
• Cron - schedules agent heartbeats
• Telegram - notification layer for when agents need human attention
• PocketBase - database storing tasks, comments, documents, activity logs, and goals
• Claude Max - the AI model powering the agents

Workflow States

Tasks move through specific states with gates:

• backlog → todo → in_progress → peer_review → review → approved → done

Agents cannot skip peer review, and the boss agent cannot approve work without all reviewers signing off. Only the human operator can move tasks to "done."

The Agent Team

Each agent has a Game of Thrones-themed identity with specific roles defined in their SOUL.md file:

• Jon Snow (Boss) - Creates tasks, coordinates workflow, promotes peer-reviewed work to final review
• Tyrion (Content Writer) - Writes tweets, threads, blog posts, landing pages in the operator's tone
• Varys (Researcher) - Web research, competitor analysis, data mining
• Daenerys (Strategist) - Campaign planning, positioning, goal setting
• Arya (Executor) - Publishes content, runs automation, ships work
• Sansa (Designer) - Creates design briefs, visual concepts
• Sandor (Devil's Advocate) - Gives brutal, honest feedback, catches BS
• ... plus 6 more agents

The personality matters: Sandor reviews content like a skeptic, Tyrion writes with wit, and Varys digs for hidden data. Their SOUL files define behavior - Sandor will roast bad writing, Daenerys will flag strategic misalignment.

Heartbeat Protocol

Each agent runs in its own OpenClaw workspace with a scheduled heartbeat every 10 minutes (scattered by 1 minute each to avoid hitting the database simultaneously).

During a heartbeat:

1. Agent authenticates and sets status to "working"
2. Agent checks for tasks in its queue
3. Agent performs its specialized work
4. When one agent drafts content, others critique it before the human sees it
5. When an agent gets stuck, it pings the boss agent
6. When something is ready or stuck, it shows up in the operator's Telegram

The developer notes this approach works better than single-shot AI workflows for complex work that needs multiple steps across days, research that builds on previous findings, different specialized perspectives, quality review before shipping, and tracking what's done, blocked, or next.