The central argument is that tokens are not interchangeable merely because they cost the same. A conventional agent spends its whole budget executing a task, while a structured strategy assigns some tokens to other jobs that can improve the executor's decisions, evaluate its output, or retain lessons for later runs.
An advising strategy lets the executor request guidance while it works. A grading strategy checks each attempt against a defined rubric and repeats when the result falls short. A dreaming strategy reviews completed work, writes lessons to memory, and feeds those lessons into the next run. The examples span sales, customer support, and recruiting workflows.
A financial-analysis benchmark compares these strategies first with self-selected budgets and then with a fixed token ceiling. The fixed-budget results show a performance difference between plain execution and role-based strategies, while a stricter analysis counts only perfectly accurate outputs as useful and estimates the repeated-run token cost of reaching one.
The practical choice depends on the objective. Advising may be more token-efficient, while grading or dreaming may be preferable when the highest priority is increasing the share of fully correct runs. Larger systems can compose these roles into coordinated loops, but the benchmark does not imply that one strategy will dominate every domain.
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