Your agent pays twice for every search: how we cut token spend by 90+% with better accuracy

Every agent that searches the web pays twice: once for the search call, and again for the tokens that search puts into the model's context. The answer is usually a few sentences. The agent pays for the whole page, and in a multi-step loop it pays for it again on every turn.

We'll show real production traces of where the tokens go and why retrieval, not generation, is the biggest line on most agent bills. Then we'll show what we changed: return the passages a query needs instead of whole pages, tune extraction to the question, and cap how much context the agent can take. We tested this across 12 frontier models and 11,000 blind-graded answers. Token spend fell by more than 90% while accuracy went up.

You get a metric (cost per correct answer, not cost per request), an open calculator to run the numbers on your own traffic, and a five-point checklist for finding token waste in your pipeline.