This video walks through how Aisle Tasks can cut token consumption by up to 50x compared to a fully agentic setup, by trading runtime reasoning for a deterministic Python script that only calls the model where a decision actually needs to be made.
The 50x is not a tuning trick, it is a difference in shape. An agent re-reasons about the whole problem on every turn, and every tool call carries that reasoning overhead with it. A task defines the process once, in code, and spends tokens only on the steps that genuinely need a model's judgment - so the bill tracks the actual work instead of the model's exploration of it.
For more on why this distinction matters as models get more expensive and more variable, see Token Burn is a Design Problem and Announcing Tasks.