Most teams building AI agents default to running every step through the same frontier model. That is the most expensive possible architecture and often not the best-performing one either. The Plan-and-Execute pattern fixes this by separating the work that actually requires expensive reasoning from the work that does not. A capable planner model breaks the task into steps. Cheaper executor models carry those steps out. In practice this means routing 70 to 90% of your token volume to models costin...