New Google paper makes a strong case for replacing ever-growing agent histories with a small, explicit execution state.

Long-horizon agents may need far less conversation history than we give them: this paper finds that replacing the growing transcript with explicit current state cuts token use and often improves accuracy.

SKILL.state gives the model only the skill instructions, a structured state, and the latest observation. After each step, the reasoning trace is discarded and only a validated state update survives.

That keeps prompt size roughly constant as the task gets longer.

On a 100-step warehouse task with Gemini-3-Flash, SKILL.state scored 0.94 while using 65,408 tokens. The LangGraph-style baseline scored 0.91 and used 1,062,387 tokens, a 16.2X difference.

The design does have a boundary: it works only when everything needed later can be captured in the state schema.