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Planner-Executor Architectures as Mission Systems for AI
Planner-executor architectures separate deciding what to do from carrying out actions, making complex AI missions easier to constrain, observe, revise, and evaluate.
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Planner-executor architectures separate deciding what to do from carrying out actions, making complex AI missions easier to constrain, observe, revise, and evaluate.
Tool-using AI turns motivation design from advice into execution, making permissions, visible state, recovery, and user control part of the motivational experience.
MCP can connect motivational AI to calendars, tasks, health data, and workflows, but useful interoperability depends on scoped authorization, trusted servers, and action-aware UX.