Pullboard Launches Self-Ordering Work Queue for Multi-Agent Systems

Coordinating multiple AI agents on a shared project often descends into a tangle of polling loops and orphaned tasks. A new open-source project called Pullboard aims to solve this with a self-ordering work queue that lets agents claim tasks immediately after their dependencies are met.

Pullboard works by maintaining a dependency graph of tasks. When an agent completes a task, the system automatically unblocks any tasks waiting on that result, making them "claimable." Agents poll the queue and claim tasks, but crucially they only see and claim work that is ready — not blocked tasks. This reduces the need for agents to repeatedly check the status of dependencies or waste compute cycles on jobs that can't proceed.

The project is designed to be lightweight and protocol-agnostic, fitting into existing agent frameworks without heavy orchestration. The core mechanic is a simple HTTP API: agents push tasks with dependency metadata, the queue resolves order, and agents pull the next actionable item.

For teams building multi-step agentic workflows — from code generation pipelines to research synthesis chains — Pullboard offers a clean primitive for avoiding coordination bloat.