Neuralwatt on how agent harnesses affect energy use and token efficiency

Why harnesses matter for energy (and token) efficiency

Neuralwatt analyzed cloud traffic and experimental requests and found that an agent harness editing an existing prompt breaks the cache, adding recomputation and energy use. With the same model and similar input and output lengths, requests that mostly miss the cache use about twice the energy of cache-hit requests, and about 2.5 times for GLM-5.3. When context passes 128k tokens, one broad re-read costs prefill compute roughly equal to 600 ordinary turns.

Why it matters: Neuralwatt traces cache misses request by request and frames stable prompt prefixes and appended state changes as ways to cut an agent's repeated computation.