Effective multi-agent systems allow users to send instructions to a specific task without first navigating to its interface. This 'fire-and-forget' communication removes significant friction and context-switching costs.
Traditional tab-based management relies on recency. Effective AI agent supervision requires a model that categorizes tasks by state (e.g., blocked, working, ready for review) to direct human attention where it is most needed.
As AI coding agents become more autonomous, the primary developer interface will transition from a single conversational chat to a dashboard for supervising a queue of active, blocked, and completed tasks.