Agentic document processing: Where AI has a date with document capturing
Despite our highly digital world, we still often receive information in document formats that lack structured data. Both our physical and digital mailboxes routinely contain items like purchase orders, requiring us to manually extract important details such as order date, numbr of items, item-IDs and -names.
The challenge of traditional document capturing tools
For organizations handling hundreds of these documents each week, specialized software solutions like Kofax, Ephesoft, and OpenText Intelligent Capture have been available for some time. These tools integrate with mailboxes to automate the information extraction process. More recently, document management platforms have introduced similar capabilities for processing documents (such as SharePoint Premium) when they enter these systems.
The challenge has always been the effort required to set up these tools. Traditional solutions need to be configured or trained in detail. A team must define where in each document the important information is located. This becomes complicated because every sender formats their documents differently. Whenever a new document layout appears, the configuration work begins again.
How AI changes the landscape
This is where AI proves valuable. Unlike traditional tools, AI offers context and semantic understanding. It can analyse your document repository, recognize documents as purchase orders, extract key information such as amounts and order dates, and interpret the context, all without long training cycles.
These context-aware agents do more than just classify and extract data. By leveraging the semantic understanding of documents, they can enhance your data, going well beyond basic field recognition and extraction. Their capabilities extend beyond simply extracting and enriching information: with semantic intelligence, these agents uncover the underlying intent and make decisions that fit the context, autonomously initiating downstream workflows, e.g. adding a line in SAP to start the actual purchase process. In this way, agents facilitate complete workflows, including decision-making, exception handling, and integration across different systems.
To maintain quality, many AI solutions use a human in the loop approach. This principle applies equally to document processing agents, which can generate two types of confidence scores to evaluate their performance:
The confidence level for text recognition
The confidence level for accuracy of the extracted data
If either score falls below a predetermined threshold (such as 90%), a validation step can be initiated within the extraction process. At this stage, a human reviewer may inspect the extracted values and make any necessary corrections. Additionally, these systems benefit from a further advantage: they have the capacity to learn from human feedback. Corrections provided by human reviewers are incorporated into the agent’s contextual knowledge base, enhancing the accuracy of future extractions.
Clear and specific responsibilities are vital for effective and high-quality agentic AI. In document processing, this can be achieved by creating agents with well-defined responsibilities. For example, a specialised classification agent can identify various document types, including contracts, meeting notes, or purchase orders. Other agents may handle the unique requirements of each type. Some agents can split combined documents, for example separating a bundle of purchase orders saved in a single file to ensure individual processing. Others may focus solely on extracting data from electronic signatures. When these agents work together within a workflow, they can fully automate the capture process, much like a well coordinated kitchen where each chef contributes a specialised skill to create an excellent result.
Conclusion
AI agents are transforming how organisations capture, extract, and manage information. This marks a fundamental shift in how organizations can utilize AI to achieve impactful business results, especially in environments that work with large volumes of content.
Providers are already adopting this approach. Hyland is integrating these agents into its new Content Innovation Cloud, while Microsoft has launched its first Document Processing Agent for SharePoint, currently in preview.
The industry is evolving from intelligent document processing to agent-driven solutions, transitioning from rule-based automation toward more adaptive and context aware agentic automation.
