Agentic Document Processing
From pipeline to colleague: systems that decide what a document needs, not just execute a preset sequence on it.
Agentic document processing is the evolution of document automation from fixed pipelines to goal-driven agents: instead of every document passing through the same predetermined sequence of classify → extract → validate, an AI agent is given the objective — "process this loan file", "adjudicate this claim pack" — and decides what steps the specific document set requires. It might split a combined PDF, order extraction by dependency (read the ID before verifying the application against it), request a missing document, reconcile inconsistencies across files, and assemble the final case output.
What makes this possible is the pairing of large language models' planning ability with tool use. The agent reasons over intermediate results and chooses among tools — OCR, classifiers, extractors, database lookups, business-rule validators, email or API actions — rather than being hard-wired to them. The practical wins come on case-shaped work: multi-document packs where completeness matters, exception handling that previously required a person to figure out "what's wrong with this one", and long-tail document types that never justified their own dedicated pipeline.
The governance question is proportionally larger. A fixed pipeline is easy to audit because it always does the same thing; an agent's behavior varies with the input, so control comes from constraints and evidence: bounded tool permissions, confidence and validation gates it cannot bypass, human approval for consequential actions, and full trajectory logging of every decision and tool call. In regulated document flows, that logged reasoning trail — what the agent saw, what it did, and why — becomes part of the compliance record, and running the whole loop inside the institution's own perimeter keeps sensitive content out of third-party hands.
A parser that doesn't just read once and hope — it zooms in, re-checks, and tries again like a careful analyst.
The flowchart writes itself at runtime — workflows that branch, retry, and escalate based on what the documents actually contain.
Given the case, not the steps — agents that carry document work from arrival to outcome on their own.
Proof Perimeter runs document AI inside your own perimeter — with a provenance record on every field.
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