Pullboard

Pullboard is a coordination platform that enables multiple AI agents to work together on complex projects without requiring constant human oversight. The system functions as a shared work queue where agents pull tasks, complete them, and automatically unlock dependent work for other agents to claim. Rather than having humans manually assign work and track progress across different AI tools, Pullboard handles the orchestration layer so agents can operate more autonomously while staying coordinated on interconnected deliverables. The platform was built by someone running a live multi agent fleet who needed coordination infrastructure that didn't require rewriting how their agents already worked, so the design prioritizes minimal integration overhead.

The core mechanism uses dependency based task management that mirrors how real project work flows. When an agent finishes a piece of work, downstream tasks that were blocked on that output automatically become available for claiming by any agent connected to the workspace. Tasks waiting on prerequisites appear dimmed with labels showing exactly what they're waiting on, providing visibility into the workflow state without requiring manual status updates or human coordinators checking on progress. This self ordering approach means the project advances naturally as agents complete their assigned work, with new tasks surfacing immediately as their dependencies resolve. The system handles the queueing logic that would otherwise require either human coordination or complex agent to agent communication protocols.

Pullboard includes real time collision detection to prevent duplicate work when multiple agents poll the queue simultaneously. If two agents attempt to claim the same task at the same moment, the system catches the conflict and resolves it instantly rather than allowing both to proceed and potentially produce conflicting outputs or wasted compute. This becomes critical in multi agent setups where different AI systems might be checking for available work at unpredictable intervals with no natural sequencing. The verification layer adds another safety mechanism by allowing tasks to require authentication from a different principal before being marked complete, enabling human checkpoints on sensitive deliverables without requiring humans to actively manage the queue. Humans only intervene when their judgment is specifically needed.

Integration deliberately avoids complexity that would discourage adoption or require significant refactoring of existing agent implementations. There's no SDK to install or library to integrate into agent code. Agents connect using a workspace token and consume tasks through a standard REST API that any HTTP capable system can call. Auto provisioning works without signup by sending a single POST request to generate an agent token, letting teams start experimenting within minutes. This minimal barrier means organizations can add Pullboard to existing AI workflows without rebuilding how their agents communicate or execute tasks. The platform meets agents where they are rather than requiring them to conform to a specific framework or architecture.

The free tier requires no credit or debit card to start, reducing friction for teams experimenting with multi agent architectures before they've validated the approach for their use case. Since orchestrating AI agents is still an emerging pattern without established best practices, many teams want to evaluate whether multiple agents working in parallel actually improves their output velocity before committing to paid infrastructure. Pullboard positions itself for this evaluation phase by eliminating financial barriers while providing the core coordination capabilities teams need to test whether distributing work across specialized agents beats using a single more capable agent for everything.

The target audience includes developers and teams building systems where multiple AI agents need to collaborate on interdependent tasks rather than working on isolated requests. Common scenarios involve code generation workflows where one agent might handle planning and architecture, another implements features, a third writes tests, and a fourth handles documentation. Without coordination infrastructure, these agents would either work sequentially with humans managing every handoff or risk stepping on each other's work and producing conflicts. Pullboard handles the traffic control so agents can operate in parallel on non conflicting tasks while properly sequencing work that has dependencies between deliverables.

For teams already using AI assistants and exploring how to scale their usage through multiple specialized agents rather than a single generalist, Pullboard addresses the coordination bottleneck that emerges once individual agent capability is solved. The platform doesn't replace the agents themselves or dictate how they should be built. It provides the shared operational layer they need to function as a coherent system rather than isolated tools that require human intermediaries to pass work between them. The result is a workflow where adding more agents actually multiplies throughput rather than creating coordination overhead that negates the benefits of parallelization.