KM4BI

You can be ambitious without the huge token bill. Here's how.

Cutting agent costs starts by removing needless work, then matching models, tools, and evaluations to each task.

Published

Agents are becoming capable enough to run longer, more complex jobs, but that autonomy can multiply token consumption. The answer is not simply to buy a cheaper model. It starts by removing work that no longer needs to exist.

Start from the business outcome

Legacy workflows often preserve summaries, approvals, and handoffs created when teams and systems could not share information directly. Adding an agent at every stop makes the workflow look modern while preserving the underlying operational debt.

For a customer quote, the real requirements are limited: produce an accurate, authorized price, keep a record, and ask for missing information. The workflow should be rebuilt around those outcomes with as few handoffs as possible. That cuts cost, delay, and failure points at the same time.

Match intelligence to the task

Not every operation needs a frontier model. Pricing rules, field checks, and approvals should remain deterministic. A less expensive model can understand the request, extract the relevant details, and call those tools, while ambiguous exceptions are routed to a more capable model.

That routing requires a reliable classifier and a suitable harness. Routine work benefits from a thick, explicit structure of data, tools, and checks. Difficult investigations need a thinner harness so the advanced model has room to reason.

Measure value, not just tokens

Evaluations should test the actual result: whether the price is correct, approval happened, the record changed, or asking the customer a question was the right next step. They also reveal runs that keep consuming resources without creating value.

The goal is not to minimize every model call, but to spend intelligence where it improves the service. A larger AI bill can make sense when teams answer faster and serve more customers. It does not make sense when several agents generate reports nobody uses.

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