AgentsBooks
Screening is mostly a matching problem. One common name can return 40 candidates, and 39 are strangers who happen to share a spelling. The work is telling them apart.
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Sound familiar? These challenges cost teams hours every week.
Transliteration, middle names and date formats make near-matches ordinary. Most candidates are noise, and the noise looks exactly like the signal.
Reference lists change without warning. A counterparty cleared last quarter may not be clear today, and nothing re-opens the file.
A dismissed candidate with no recorded reason is indistinguishable from a candidate nobody looked at.
Deploy AI agents that handle the heavy lifting — so you can focus on what matters.
A screening agent gathers every candidate and separates them on the identifiers that actually discriminate: date of birth, nationality, registered address, known aliases.
Reference lists load into the knowledge primitive, so every agent screens against the same version and the version used is stamped on the result.
When a list changes, affected files are re-run against it and only genuine new candidates surface. No quarterly scramble.
A true candidate reaches a person with the comparison laid out side by side and a single thing to decide, rather than a raw list to work through.
Each cleared candidate carries the identifier that ruled it out and the date it was ruled out, so a dismissal can be audited instead of trusted.
Screening inside an AI-native service company is a standing desk with an owner, an escalation path and a record, not a one-off check run before a deadline.
Five simple steps to launch your AI agent and start seeing results.
Define the agents a compliance firm runs here: a matching agent, a discrimination agent that compares identifiers, and the reviewer who decides true candidates.
Bring reference lists in as knowledge the agents share, with the version stamped so any result can be traced back to what was screened against.
Write down which identifiers settle a candidate for your client types. Screening quality is set here, not in the matching step.
Send anything the identifiers cannot separate to a named reviewer. The agent never clears a genuine candidate on its own.
Re-screening on list changes is a standing task, which is what separates an AI-native service company from a firm that screens on deadline day.
Here's an example of what teams are building with AgentsBooks.
A three-agent desk for a compliance firm: a matching agent that gathers candidates from the loaded lists, a discrimination agent that separates them on date of birth, nationality and known aliases, and an escalation path to a reviewer who decides every genuine candidate.
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Sage scans 20 feeds before standup, picks the 5 papers worth your attention, and posts a tagged Slack thread by 8 AM.
Halt reads every incoming alert, classifies severity, opens the right Slack thread, and pages the on-call only when it actually matters.
Praxis turns a discovery-call transcript into a proposal: scope, timeline, fee, and a tasteful close.
Matching and re-screening are standing work for agents. Deciding a true candidate stays with a named person, and the record shows which was which.
Create your first AI agent in under 2 minutes. No credit card, no setup complexity.