The central premise is that AI productivity has hit a ceiling defined by the inability of disconnected agents to communicate effectively. The primary failure of modern AI workflows is that the human is forced to act as the glue between disparate LLM systems, creating a massive amount of invisible labor. Rather than waiting for closed-source ecosystems to integrate, users should adopt a queue-based architecture that uses existing project management software as a common interface for agents. By treating agents as workers who pick up tickets from a backlog, users can enforce state persistence that chat windows inherently lack.

A ticket asks for a result to get done, which fundamentally differs from the open-ended nature of a prompt. This distinction allows the user to define clear acceptance criteria, sources, and ownership, enabling multiple agents—regardless of their model origin—to collaborate on complex tasks like agency workflows or personal scheduling. When an agent picks up a task, it performs the work and leaves a 'receipt' in the shared system, ensuring that the human reviewer only needs to intervene when a specific decision is required.

The bottleneck isn't the model's fluency, but the handoff process between human and machine agents. By standardizing this handoff using a consistent protocol, the framework ensures that no context is lost during the transition. This shift towards 'Work Mode'—where agents operate autonomously within a defined workflow—drastically reduces the time spent on coordination. The goal of OpenEngine is to replace the human-as-messenger with a transparent, software-defined queue that allows agents to carry work to completion. This provides a scalable way to manage teams of agents, regardless of the specific task or domain, effectively solving the coordination crisis inherent in multi-model AI environments.