Agentic Context Grounding for Complex Document Automation

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Solution Overview

Problem

Conventional software automations struggle with deciphering industry-specific terminology and complex document structures, leading to inefficient and inaccurate responses, especially when utilizing large language models (LLMs) for complex business processes.

Innovation Solution

Implementing agentic extraction and searching methods using AI agents to generate context grounding, perform multistage processing of text and images, and convert output to vectors for enhanced context embeddings, which are stored in agentic memory for improved accuracy in AI model responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional software automations use LLMs to process complex business processes with industry terminology and complex document structures, then the system can handle diverse business tasks, but the responses become wildly inaccurate and take extremely long to generate

Engineering Contradiction:

Improveability to handle diverse business tasksVSAvoidaccuracy of responses

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary extraction and processing of industry terminology and document structures before the LLM generates responses. AI agents pre-process the complex document structures and extract relevant information, creating a prepared context that the LLM can then use to generate accurate responses without the complexity affecting generation time or accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces intermediary AI agents that act as mediators between the complex business process data and the LLM. These agents extract, clean, and prepare the data into a format suitable for LLM processing, preventing the LLM from being directly overwhelmed by complex document structures and industry terminology.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Quantity of substance

If conventional software automations statically execute hundreds of actions across large swaths of processing resources, then the system can process large amounts of data, but the system becomes inefficient and requires excessive processing power

Engineering Contradiction:

Improveamount of data processedVSAvoidefficiency of processing

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The system extracts only the necessary information from large datasets using AI agents that identify and pull out relevant data points. Instead of processing entire datasets through hundreds of static actions, the extraction mechanism selectively retrieves only the pertinent information needed for the task, significantly reducing processing requirements while maintaining data processing capability.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The processing workload is segmented into discrete AI agent tasks that handle specific portions of data processing. Rather than a monolithic static execution of hundreds of actions, the system divides the processing into manageable segments handled by specialized agents, improving overall efficiency and reducing total processing power requirements.

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If LLMs are used for complex business processes with specific goals and industry terminology, then the system can perform general purpose tasks, but the functionality becomes wildly inaccurate when facing unique or complex business processes

Engineering Contradiction:

Improvegeneral purpose capabilityVSAvoidfunctional accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system performs preliminary extraction and contextualization of industry terminology and document structures before LLM processing. This pre-processing creates accurate contextual representations that preserve the precision of complex business processes while maintaining the LLM's general purpose capabilities.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

AI agents serve as intermediaries that translate complex business process terminology and structures into formats the LLM can accurately process. These intermediaries preserve the precision and specificity of industry terminology while enabling the LLM to handle complex business processes accurately.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12554758B1Advanced agentic extraction for context grounding within automations

Publication Date: 2026.02.17 UIPATH INC

AI summary

An agentic extraction method is provided. The agentic extraction method is implemented by artificial intelligence (AI) agents to generate and provide a context grounding for an AI model. The agentic extraction method includes extracting text data from documents including complex document structures and capturing images of the documents. The agentic extraction method includes performing a multistage processing of the text data and the images utilizing large language models to generate an output set including a contextualization and one or more keywords. The agentic extraction method includes converting the output set to vectors including context embeddings to provide the context grounding.