Token Economics: Cutting the Cost and Latency of Your Now Assist Skills

In a proof of concept, token cost is invisible. At production scale, it is a line item that finance will eventually ask you about, and latency is something your users feel on every interaction. Generative AI is not free per call the way a database query effectively is, so cost and performance tuning is becoming a real mid-level skill. The good news: most skills and agents are wasteful in predictable ways, and the savings are usually large and quality-neutral. Here is where the money and the milliseconds actually go.

Where the tokens go. Three main sinks. First, bloated prompts: long, verbose instruction blocks and few-shot examples that get sent on every single call. Second, oversized retrieval context: a retriever configured to stuff large amounts of loosely relevant content into the prompt, most of which the model does not need. Third, chatty agents: multi-agent designs and long reasoning chains that make many model calls to accomplish what a tighter design does in one or two. Every one of these is paid for on every invocation, so at volume they dominate your bill.

Match the model to the task. This is the highest-leverage lever you have. NowLLM is tuned and priced for workflow tasks, and for the bulk of classification, extraction, and summarization work it is the right, cost-effective default. Do not route a routine classification through a large frontier model via the generic LLM connector because it is marginally better on a benchmark; you are paying a premium for capability the task does not need. Reserve the big, expensive models for the genuinely hard reasoning that measurably benefits, and use the smallest model that meets the quality bar for everything else. Often the right architecture uses a cheap model for the high-volume step and an expensive one only for the rare hard case.

Trim the context with tighter retrieval. Going back to T2: an over-broad retriever is both a quality problem and a cost problem. Every extra chunk you inject is tokens you pay for and noise the model has to wade through. Scoping retrieval narrowly and tuning chunk sizes cuts cost and improves answer quality at the same time. This is the rare optimization with no trade-off, so do it first.

Prune the prompt. Audit your instruction blocks. Verbose, redundant instructions and excessive few-shot examples are tokens sent on every call. Tighten the wording, keep only the examples that measurably change behavior, and move stable context out of the per-call prompt where the platform allows. Re-run your evaluation set (T9) after trimming to confirm quality held; usually it does, because shorter and clearer often helps the model.

Reduce unnecessary calls. For agents, fewer, better-scoped calls beat many chatty ones. Question every hop in a multi-agent design: does this genuinely need a separate agent and its own model call, or did you decompose for elegance rather than necessity (see T1)? Cache and reuse where the platform supports it, so identical or near-identical work is not recomputed from scratch.

Measure cost per resolution, not cost per call. Here is the metric that keeps you honest. A cheap-per-call skill that fails half the time and escalates to a human is not cheap; it just moved the cost. An expensive-per-call agent that resolves the issue end to end the first time may be the better deal. Measure cost against the outcome, cost per resolved ticket, per completed workflow, not per API call. Optimizing call cost while ignoring resolution rate is how you save pennies and lose dollars.

Token economics is not about being cheap. It is about spending your compute where it produces outcomes and not wasting it where it does not. The teams that scale agents sustainably are the ones that measured cost per resolution early and designed for it, rather than discovering the bill after the rollout.