A Review Ops Agent is an AI agent that does review work — the checks people make on documents and data before they approve or reject a case. It applies review criteria written in plain language to each case, checks and cross-checks the evidence, returns a decision with the reasoning, and sends unclear cases to a person.
Policy
The company name, registration number and representative on the business registration must match the application.
Decision
The Review Ops Agent from ARGOS Identity
ARGOS Omni is a Review Ops Agent that reviews documents and data and decides each case.
How it works
It takes review criteria as sentences
Reviewers write the rules they already use, such as “the company name on the business registration must match the application”. The internal policy documents behind them can be attached. No code or rule trees.
It turns the criteria into a playbook
The criteria are split into review steps, and each step gets the work it needs: reading documents, extracting data, cross-checking documents, or looking up external sources such as AML watchlists.
It checks and cross-checks each case
For every case it extracts values from the submitted documents and checks them against other documents and external sources, recording where each value came from.
It decides, with the evidence
Each case is approved, rejected or marked for human review, with the reasoning for every field. Cases it cannot decide with confidence go to a reviewer instead of being decided automatically.
Output
Compared
A Review Ops Agent does not replace these tools so much as bring their outputs together to answer the question reviewers actually ask: can this case go through?
| Tool | What it does | How a Review Ops Agent differs |
|---|---|---|
| OCR / IDP | Reads text and values from documents. | Stops at extraction. A Review Ops Agent checks the extracted values against your criteria and other sources and decides. |
| RPA | Repeats fixed sequences of screen actions. | Does not interpret document content and stops on exceptions. A Review Ops Agent reads each document and reasons about it. |
| Rules engine | Returns results from conditions written in code. | Conditions are maintained by developers. A Review Ops Agent uses the criteria reviewers write in plain language. |
| KYC / KYB platform | Runs a fixed set of identity or business checks. | The checks are defined by the product. A Review Ops Agent applies each company’s own criteria; KYB is one use case among many. |
Use cases
Business customer onboarding (KYB): business registration, power of attorney, representative ID
Corporate telecom subscription review
Insurance claim document review
Vendor and supplier onboarding documents
AML review
Any other work where people read documents and approve or reject
Checklist
Can you set review criteria in plain language, without code?
Does every decision come with evidence — which document, which value?
Is there a step that routes low-confidence cases to people?
Does it accept the files you actually receive (PDF, images, Office documents, email)?
Can you get results through an API and reports, and connect them to your systems?
Do criteria changes take effect without a redeploy?
Common questions from teams looking at a Review Ops Agent for the first time.
ARGOS Identity offers one called ARGOS Omni. When comparing any product, use the checklist above: plain-language criteria, evidence for each decision, a human review step, supported file types and API access.
It takes over the repetitive checks and hands cases that need judgment to reviewers, so people spend their time on final decisions with the evidence already laid out.
No. KYB is one kind of review a Review Ops Agent can handle. The same approach works for insurance claims, telecom subscription review, vendor onboarding and other reviews with different criteria.
You edit the criteria sentences and save. ARGOS Omni rebuilds the playbook from the new criteria and applies it from the next analysis, with no development work. Finished analyses keep the playbook they ran with, so you can always see which criteria produced a decision.
Sales
Tell us which reviews your team does by hand today and the criteria behind them, and we will set up a test project with those criteria.
Every deployment starts with a 20-minute meeting. Share the policies and plans that shift with your business and the operating model you need — we'll deliver an agent orchestration that fits.
