Agentic IDP
A long-document data extraction pattern that combines agent planning capabilities with Intelligent Document Processing (IDP). It first performs page-by-page parallel OCR and layout parsing, after which the extractor acts like an agent to judge across pages whether table fragments are continuous, stitch tables together, and read document-level fields with backtracking as needed.
Timeline (last 90 days)
The value of Agentic IDP can only be realized in scenarios with diverse and frequently changing document formats, such as supply chain invoice processing, cross-border contract review, and multi-source report consolidation.
A common engineering practice is to set different confidence thresholds for different field types: high-risk fields such as amounts and account numbers are given high thresholds, while low-risk fields such as notes and addresses are given low thresholds.
For deploying Agentic IDP, it is recommended to start with a high human intervention rate and gradually relax the thresholds as the Agent's memory accumulates.
The Agent's memory expands as processing volume grows, and if outdated business rules are not cleaned up, they may mislead subsequent judgments, so a periodic review and elimination mechanism needs to be designed.
The workflow of an Agentic IDP system is roughly divided into four steps: classification, extraction, validation, and learning.
The technical foundation that enables Agentic IDP systems to work without manual template configuration has three layers: semantic understanding replacing position matching, dynamic tool orchestration, and a feedback loop.
In an Agentic IDP system, the Agent usually gives a confidence score for each extracted field, which may come from model token probabilities, the consistency of results from multiple extraction tools, or the degree of agreement with business rules.
In Agentic IDP, table fields are object types with nested columns, including subfields such as description, quantity, unit_price, and line_total, and the returned result is a row-by-row list with each value carrying a confidence score, source text, and page number.
The "Agentic" in Agentic IDP means the extractor actively plans the continuity of table fragments and the concatenation order, and backtracks to any page chunk as needed to read document-level fields; IDP is the abbreviation of Intelligent Document Processing.
Agentic IDP adopts a two-layer architecture: the first layer parses page by page in parallel (OCR and layout analysis split into chunks), and the second layer, across all page chunks, has the Agentic extractor determine the continuity of table fragments and concatenate them in order, while also reading document-level fields, returning both types of Schema in one run.
All Facts (10)
The value of Agentic IDP can only be realized in scenarios with diverse and frequently changing document formats, such as supply chain invoice processing, cross-border contract review, and multi-source report consolidation.
50%UnverifiedA common engineering practice is to set different confidence thresholds for different field types: high-risk fields such as amounts and account numbers are given high thresholds, while low-risk fields such as notes and addresses are given low thresholds.
50%UnverifiedFor deploying Agentic IDP, it is recommended to start with a high human intervention rate and gradually relax the thresholds as the Agent's memory accumulates.
50%UnverifiedThe Agent's memory expands as processing volume grows, and if outdated business rules are not cleaned up, they may mislead subsequent judgments, so a periodic review and elimination mechanism needs to be designed.
50%UnverifiedThe workflow of an Agentic IDP system is roughly divided into four steps: classification, extraction, validation, and learning.
50%UnverifiedThe technical foundation that enables Agentic IDP systems to work without manual template configuration has three layers: semantic understanding replacing position matching, dynamic tool orchestration, and a feedback loop.
50%UnverifiedIn an Agentic IDP system, the Agent usually gives a confidence score for each extracted field, which may come from model token probabilities, the consistency of results from multiple extraction tools, or the degree of agreement with business rules.
50%UnverifiedIn Agentic IDP, table fields are object types with nested columns, including subfields such as description, quantity, unit_price, and line_total, and the returned result is a row-by-row list with each value carrying a confidence score, source text, and page number.
50%UnverifiedThe "Agentic" in Agentic IDP means the extractor actively plans the continuity of table fragments and the concatenation order, and backtracks to any page chunk as needed to read document-level fields; IDP is the abbreviation of Intelligent Document Processing.
50%UnverifiedAgentic IDP adopts a two-layer architecture: the first layer parses page by page in parallel (OCR and layout analysis split into chunks), and the second layer, across all page chunks, has the Agentic extractor determine the continuity of table fragments and concatenate them in order, while also reading document-level fields, returning both types of Schema in one run.
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