What Is DEEP Agent? The AI Agent That Reads Documents and Then Carries Out the Work

DEEP Agent is an AI agent made by Korea Deep Learning. It understands unstructured data, makes judgments based on business rules and context, and carries out real work such as screening, review and system registration.

Why DEEP Agent Is Needed

Extracting values did not finish the work.

Even organizations that have adopted document recognition and item extraction often find that staff workloads do not drop as much as expected. The reason is that people still have to look at the extracted values to judge whether they meet the criteria, cross-check them against other documents, and register the results in the business system.

Take screening as an example. What follows is checking that all the required documents have arrived, comparing whether the same information is written correctly across documents, and approving or requesting supplementary documents according to the criteria. Technology for reading documents resolves only the front part of this chain.

Korea Deep Learning's product lineup is built around filling that gap. DEEP OCR reads the documents, DEEP Parser restores their structure, and KIE finds the required items. DEEP Agent takes the results and handles the final stage of judging and acting on them.

The Precise Definition of DEEP Agent: How It Differs from a Conversational AI Assistant

Three Axes Along Which a Conversational AI Assistant and DEEP Agent Differ

First, the starting point differs. A conversational AI assistant moves only when a person types a question. DEEP Agent starts processing along a set flow when a document arrives in the business system.

Second, the output differs. A conversational AI assistant shows answers or summaries on the screen. DEEP Agent records its judgment and the basis for it, and carries the work through to registering values in an ERP or internal system, or writing a reply email.

Third, the basis for judgment differs. A conversational AI assistant aims for an answer the user is satisfied with. DEEP Agent judges according to the business rules and validation conditions the organization has set, and separates out cases that cannot be concluded under those rules as exceptions.

The two approaches suit different uses. Conversational tools are convenient when staff are looking for or organizing material, while an agent that carries repetitive daily screening and registration work through to the end along a fixed flow is the right fit for those tasks.

Four Processing Stages of DEEP Agent

Four Stages from Judgment to Execution

First, structuring. The results produced by OCR, the Parser and KIE are organized into the items and value forms used in the work. If this stage is unstable, the judgments after it will be unstable too.

Second, inference. The context of the document is read to work out what each value means and how the documents relate to each other. Distinguishing whether a given name belongs to the applicant or to the representative falls in this stage.

Third, condition judgment. The rules of the business are applied to check whether all required items are present, whether information matches across documents, and whether a contract contains risk clauses.

Fourth, execution. The judgment is registered in the ERP, the document management system or another internal business system, and where needed a supplementary-request email is written or a reply is sent.

The four stages run in sequence, but results do not flow in one direction only. If condition judgment finds that documents are missing, the execution stage sends a supplementary request, and when new documents arrive, processing starts again from structuring.

How to Apply DEEP Agent in Practice

Choose the Work One Case at a Time

Adoption starts from a single case of work, not from a list of features. Choosing a task with a clear beginning and end, such as merchant screening or document review, makes it easier to set the boundary between what is finished automatically and what goes to a person, and performance can be measured per case.

Handing over many tasks at once mixes up the exception criteria and makes operation hard. The realistic order is to let the automation scope and exception criteria stabilize in one task, and then widen to adjacent tasks that use the same documents.

Keep the Judgment Rules and the Basis Together

The agent's judgments must be possible to check later. Recording which values were used as the basis and which rule led to the conclusion, alongside the result, lets reviewers check quickly. It also becomes the basis for revising a rule when the same error keeps recurring.

It helps to write the rules as sentences that the staff responsible can read and understand. Then, when the business criteria change, it is clear who needs to change what.

Design the Connection to Existing Systems First

If the destination of the judgment is not set, staff end up re-entering the results. In finance, it is common for validated data to flow through a document management system and automation tools into the core banking system, so the connection points and input formats are fixed early in adoption.

Settle in advance how to roll back when an error occurs at the connection, and the retry rules too. If a registration is left half done, the staff member has even more to check.

DEEP Agent in the Domestic Setting

In domestic finance and the public sector, many environments restrict external communication, so the installation method and the route for model updates must be checked early in the evaluation. Configurations that keep the basis for judgments, the processing history and records of approvals by people are also required as audit obligations. When the work may fall under high-impact AI, such as screening, it is safer to build human management and oversight procedures into the flow, in line with the obligations of the Framework Act on Artificial Intelligence.