Running multiple AI coding agents often leads to friction because they operate in isolated "bubbles," unaware of each other's actions or decisions. This lack of shared context causes agents to undo each other's work or duplicate efforts, forcing humans to act as intermediaries. The problem is not the models themselves, but their inability to communicate and coordinate. Common workarounds like static instruction files or constantly pasted decision logs fail to address the dynamic nature of live project states. Larger context windows also do not solve the fundamental issue of inter-agent awareness. The real challenge is a coordination problem, not a memory one, requiring a shared record of truth. This shared record should be append-only for transparency, track current state effectively, and provide agents with relevant information rather than overwhelming them. Such a system enables agents and humans to work from a single, up-to-date source of truth, significantly improving collaboration. The future lies in this coordination layer, allowing multiple agents and humans to work cohesively on projects. Providing agents with a shared, current record of decisions and completed tasks is the most impactful step toward this goal.