Running 10+ AI coding agents in parallel: the bottleneck is human attention, not AI speed

Short summary

A developer running 10+ AI coding agents in parallel (Claude Code, Codex, Kimi) reports that the bottleneck shifted from AI speed to human attention. With agents spread across multiple machines, the real cost is context-switching between terminal sessions to check each agent's status. The author argues this is a new workflow problem—supervising autonomous processes—closer to managing a small team than traditional programming, but offers no concrete solution.

• •Running 10+ AI coding agents shifts the bottleneck to human attention
• •Context-switching between sessions is the dominant cost, not agent capability
• •tmux keeps sessions alive but doesn't solve attention management across agents

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