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Architectural analysis demonstrates a task-first control plane for parallel multi-agent systems, suggesting human cognitive burden remains stable as autonomous agent fleets scale.
Agent Task Control Plane (ATCP) is a reference architecture for governing parallel AI-agent work. It addresses the growing human cognitive burden of managing multiple asynchronous agents, including task-state loss, context contamination, missing deployment or verification steps, goal drift, and weak traceability. ATCP introduces a task-first control-plane model built around authoritative state machines, coordination, observability, human approval, verification, traceability, and human-attention routing. Its central principle is: scaling agents should not scale human cognitive load.
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Qinghua Ran (2026) studied this question.
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