Intelligent document processing (IDP) is the use of software to read documents, work out what they are, extract the data they contain and pass it to other systems, with people checking anything it is unsure about. It turns invoices, forms, statements and letters into structured data. An AI agent may use IDP, then decide and act on the result.
Why does intelligent document processing matter for agents?
Most regulated work still arrives as documents: payslips for a loan, invoices for payment, claim forms, identity documents, correspondence. An agent can only act well on what it reads correctly. If extraction misreads an account number or a date, everything the agent does next rests on a wrong fact.
Document input is also an attack route. A document can carry hidden text meant to instruct the agent that reads it. IDP that simply extracts everything and passes it on can carry the attack with it.
How does it work in practice?
A typical IDP flow has five stages:
- Capture: documents arrive by email, upload, scan or post.
- Classify: identify the type (invoice, bank statement, passport).
- Extract: pull out the fields that matter, using OCR for images and language models for layout and meaning.
- Validate: check fields against rules and other records; flag low-confidence fields for a person.
- Store and route: file the document and send the data to the next system or person.
Filing well matters as much as reading well. Indexing documents into a document store with an AI agent covers classification and metadata, and connecting an AI agent to a document management system covers the integration side.
What does an example look like?
A lender receives payslips and bank statements with personal loan applications. IDP classifies each document, extracts employer, pay dates, net pay and regular outgoings, and flags a statement where the balance does not reconcile. An underwriting agent then uses the extracted figures to draft an affordability summary at depth 2, citing which document each figure came from. An underwriter reviews the summary and decides. The extracted figures are declared evidence: supplied by the applicant, not yet verified against a bank's own records.
How is IDP different from an AI agent?
IDP reads and extracts. An agent decides what to do and does it. IDP produces data; the agent uses data to draft a letter, update a record or prepare a payment. That makes the boundary important. IDP output should carry its confidence and its origin, so the agent and the controls around it know how far to trust each field. A figure read from a customer's uploaded statement should not be treated like a figure from the bank's own ledger.
It also differs from straight-through processing, which describes cases passing end to end with no manual touch. IDP is one ingredient; straight-through processing is the outcome you get when extraction, validation and decision all clear without exception.
What records does it create?
IDP processes personal data at scale, so it belongs in your records of processing: what documents, what fields, for what purpose, kept how long. The agent-specific view is in GDPR Article 30 records of processing for AI agents. For where document handling fits in running agents, see our guide to deploying AI agents.
Where Pryme fits
Through the Pryme Context Protocol, each extracted fact keeps its provenance class. A figure from a customer's upload is declared; one from an emailed invoice is external. Neither can authorise a payment on its own, however cleanly it was extracted.
Questions people ask
Is IDP the same as OCR?
No. OCR turns images of text into machine-readable text. IDP adds classification, extraction of meaning, validation and routing. OCR is one step inside it.
Should a person check every extracted document?
Not every one. Check low-confidence fields, documents that fail validation, and anything feeding an action that is hard to undo. Sample the rest to confirm accuracy holds.