Agents

Token saving in agent systems is often framed as an efficiency concern. A better framing is signal-to-noise control: every unnecessary token competes for attention, increases inference cost, and makes the next decision less precise. Current work on context compression, KV-cache compression, and long-running interactions points in the same direction.

The intuition that feeding a language model more information improves its outputs is wrong often enough to matter. Here is why, and what to do about it.