How Clawdbot Coordinates 6 AI Agents with a Production-Stable Work Queue

Clawdbot's team shared their work queue architecture that coordinates 6 AI agents running an AI-operated store. They found the coordination problem harder than individual agent logic, with the system going through several iterations before reaching production stability.

Core System Features

The work queue implements several key mechanisms:

• Atomic task claiming: Prevents two agents from grabbing the same task
• State machine: Tasks move through states: pending → ready → in_progress → review → complete
• Retry logic: 3 failures with backoff, then permanent failure to prevent runaway retry loops
• Task chains: Parent completion auto-spawns children via a next_tasks field
• Heartbeat tracking: Stale claims (from agent crashes) auto-reset after timeout
• Daemon orchestrator: Polls every 60 seconds and spawns agents for ready tasks

Production Lessons

The team notes that failure mode handling wasn't obvious until they had real production incidents to learn from. They've published a full architecture writeup with lessons from running this in production.

The system coordinates multiple agents working concurrently: design, code, marketing, and operations agents. The team is open to discussing tradeoffs, particularly around the failure mode handling that emerged from production experience.