The API bill is part of the research question

A research agent can ask for another page indefinitely. Your data provider can bill for it just as patiently.

Before automating collection, define what evidence is needed for the decision and how much you are willing to collect to obtain it.

Set limits in the workflow

Choose maximum records, pages, runtime, concurrency and spend where the provider exposes pricing or budget controls. Persist cursors so a restart resumes rather than beginning the same collection again.

For a hypothetical niche study, collecting a carefully selected cohort may answer the question better than repeatedly downloading a broad catalogue. The right boundary depends on the claim you intend to make.

Count attempts accurately

Distinguish requested runs, accepted runs, completed runs and usable records. A failed request may or may not incur cost depending on the provider. Reconcile usage using the provider’s actual reporting rather than assuming every error is free.

Cache stable observations when permitted and record their age. Do not refresh unchanged metadata at the same frequency as a time-sensitive ranking merely because one scheduler handles both.

Give the agent a stop condition

The condition should be related to the task: enough comparable records, a defined sample complete, a provider refusal, or a budget reached. “Keep researching until confident” is difficult to operate safely because confidence has no billable ceiling.

When a source refuses access or a plan limit is reached, pause that dependency and preserve the reason. Do not hide the refusal behind repeated retries or seek an unauthorized route around it.

At the end, report coverage and cost together. A thousand retrieved rows are not necessarily a thousand valid observations, and a successful collection is not automatically a useful study.

Automation should make a bounded research method repeatable. It should not turn curiosity into an unbounded loop with a credit card attached.