Mark Geene•August 6, 2025
Documents underlie every business process. Traditionally, businesses fully depended on people to understand and process them, before their approach evolved to incorporate AI and automation. With the advent of AI agents—AI-based software entities able to plan, work, and make decisions independently—document-driven processes can now be automated end to end, freeing people for more important tasks.
However, AI agents struggle with consistency and scale. Typical AI agents perform well when asked to understand and process a small number of simple documents. Yet, accuracy and performance degrade at an enterprise scale of hundreds, thousands, or even millions. Furthermore, complex documents—containing elements like embedded tables, graphs, and inferred values—can be a real challenge for agents to understand.
In this blog, I’ll explain why intelligent document processing (IDP) capabilities are the missing piece in the agentic automation of document-based processes. I’ll show how IDP enables AI agents to understand and process enterprise documents—consistently, accurately, at speed and scale.
How does IDP enhance agentic automation?
AI agents are similar to real workers in that they need a lot of different tools to do their job well. Similarly, agents should call on a specific ‘tool’ when they encounter a complex document, or escalate to a human if no tool is available.
Agents are most effective when they use tools that are tuned for a specific task. You can give a document to an agent and hope it extracts the right data each time. But the better option is to fine-tune an extractor and let the agent use it as a high precision tool for the task.
This is where IDP comes in.
IDP solutions, like UiPath IXP (Intelligent Xtraction & Processing), provide important document processing capabilities that agents lack. They typically:
- Output consistent, structured data that can be used in automations
- Offer tools to measure the accuracy and precision of AI models, how to gather ground-truth data, and how to compare different model versions
- Provide methods to quickly iterate and improve model performance and fine-tune the model at an individual field level
- Provide version controls for models, schemas, and prompts, etc.
You can see how IDP consistently and reliably extracts important data from even the most complex document types in this demo:
Agents use IDP as a tool to accurately understand and process complex documents into structured, consistent data. It’s then easy for agents to use their reasoning capabilities to leverage the IDP output and complete the rest of the workflow.
IDP is a vital tool in the toolbox of any agent that needs to process documents as part of its workflow. It reduces the need for manual document review and ensures document-based processes can run smoothly and largely autonomously.
Can you use large language models for document processing?
An IDP solution is one of several tools an AI agent might call on to execute an E2E document-based process. However, could you replace an IDP ‘tool’ with a large language model (LLM) like ChatGPT or Claude?
AI models have typically required significant upfront training, with employees manually annotating many documents. However, the latest LLMs have shown strong performance in smaller use cases, using their native understanding and reasoning capabilities to extract the correct data with no training. Yet, larger enterprise-scale processes need much more rigor and reliability.
IDP solutions are more than just LLMs. After all, a strong data extractor is just one component in a complete IDP solution. Enterprises must also consider:
- Digitization
- Classification
- Splitting packets and large documents
- Extraction (template, machine learning, generative AI)
- Fine-tuning
- Data validation and reinforcement learning
- Model hosting
- Systems integration and workflow processing
- Access control
- Security
- Governance and compliance
LLMs excel at creative, unstructured work, but they struggle to maintain accuracy in the long term. If an agent calls on an LLM to extract specific information from a complex document, it might succeed on the first few attempts. However, mistakes are inevitable. It might hallucinate an incorrect output and, without monitoring capabilities, you have no way of knowing without manually reviewing every document. At that stage, you might as well be processing them all manually.
It’s also difficult to get consistent, structured outputs from LLMs. This usually takes many hours of trial-and-error prompt engineering, and even then, there’s no guarantee the model won’t hallucinate or deviate from the output you’ve asked for.
Chat-based LLMs are ideal for ad-hoc use, but out-of-the-box they don’t provide the confidence or reliability that an enterprise needs for high-volume repeatable document extraction without significant tuning. They excel in tasks where there’s a lot of flexibility and uncertainty involved, and you don’t always need a consistent output. But when you’re in a business setting, processing thousands of documents for the exact same goal, you really need reliable, repeatable, and structured outputs. The challenge is to turn models that are non-deterministic by their very nature; and turn them into more deterministic and predictable tools for repeatable processes.
UiPath IXP: enabling agentic data extraction
The latest IDP solutions use one or more LLMs at their core. This may include external LLMs, but also, most importantly, specialized LLMs like UiPath DocPath and CommPath. These LLMs are specifically trained for data extraction from distinct formats like complex documents and communications. The latest IDP also provides many tools, integrations, and capabilities to increase the consistency and reliability of their outputs far beyond what a single LLM can do alone.
