What DEEP Agent is: a complete guide to the AI Agent that carries work through after documents are read

DEEP Agent is an AI Agent built by Korea Deep Learning that understands unstructured data, makes judgments based on business rules and context, and then carries out real work such as review, verification and registration in business systems.

Why DEEP Agent became necessary

Extracting the values did not finish the work.

Even in organisations that have adopted document recognition and field extraction, the workload of the people in charge often falls less than expected. Someone still has to look at the extracted values, judge whether they meet the conditions, compare them against other documents, and register the result in the business system.

Take a review process. It involves confirming that every required document has arrived, comparing whether the same information is written consistently across documents, and approving or requesting supplementary material according to the criteria. Technology that reads documents solves only the front of that chain.

Korea Deep Learning's product line is organised around that gap. DEEP OCR reads the document, DEEP Parser restores its structure, and KIE finds the fields that are needed. DEEP Agent takes those results and covers the final segment, where judgment and execution happen.

What DEEP Agent actually is: how it differs from a conversational AI assistant

Conversational AI assistants and DEEP Agent across three axes

First, the starting point differs. A conversational AI assistant acts only when a person types a question. DEEP Agent starts processing along a defined flow as soon as a document is received by the business system.

Second, the output differs. A conversational AI assistant shows an answer or a summary on screen. DEEP Agent records its judgment and the grounds for it, and goes on to register values in ERP or internal systems or to draft reply emails.

Third, the basis for judgment differs. A conversational AI assistant aims for an answer the user is satisfied with. DEEP Agent judges against the business rules and validation conditions the organisation has set, and separates out as exceptions the cases those criteria cannot decide.

The two serve different purposes. A conversational tool is convenient when staff are looking up or organising material, while an Agent that carries the defined flow through to the end suits review and registration work that repeats every day.

The four processing stages that make up DEEP Agent

Four stages from judgment to execution

First, structuring. The results produced by OCR, Parser and KIE are organised into the fields and values the business uses. If this stage wavers, the judgment that follows wavers too.

Second, reasoning. The context of the document is read to understand what a value means and how documents relate to one another. Telling whether the same name belongs to the applicant or to the representative falls here.

Third, condition checking. Whether every required field is present, whether information agrees across documents, and whether a contract contains risk clauses are checked against the business rules.

Fourth, execution. The judgment is registered in ERP, the document management system or internal business systems, and where necessary an email requesting supplementary material is drafted or a reply is sent.

The four stages run in order, but results do not flow in only one direction. If condition checking finds a document missing, the execution stage sends a request for supplementary material, and when the new document arrives, processing starts again from structuring.

How DEEP Agent is applied in practice

Define the work to hand over as a single case

Adoption starts from one case of work rather than from a list of features. Choosing work with a clear start and end, such as merchant review or document verification, makes it easier to set the criteria for what finishes automatically and what goes to a person, and results can be measured case by case.

Handing over several processes at once mixes their exception criteria together and makes operations difficult. The realistic order is to stabilise the scope of automatic processing and the exception criteria in one process, then widen to neighbouring processes that use the same documents.

Record the rules and grounds together with the judgment

An Agent's judgment has to be verifiable afterwards. Recording which values were used as grounds and under which rule the conclusion was reached, together with the result, lets reviewers check quickly and gives a basis for revising rules when the same error recurs.

It is better to write rules as sentences the people in charge can read and understand. That makes it clear who has to change what when business criteria change.

Design the connection to existing systems first

If it is not settled where a judgment goes, staff end up entering the result again. In financial work it is common for validated data to pass through the document management system and an automation tool into the core system, so this connection point and the input format are fixed early in adoption.

The way to roll back and the retry rules when the integration segment fails are decided at the same time. A registration left half complete only increases what staff have to check.

DEEP Agent in the Korean environment

In Korean finance and the public sector, many environments restrict external communication, so the installation method and the path for model updates have to be confirmed early in the adoption review. A configuration that records the grounds for judgment, the processing history and the record of human approval is also required as an audit requirement. Where the Agent is used for work that may fall under high-impact AI, such as review, it is safer to build human oversight procedures into the flow, taking into account the duties under Korea's AI Basic Act.

Frequently asked questions