Claims adjudication as a governed agent workflow

A claim — insurance, warranty, expense, reimbursement — is deceptively simple: some documents come in, a decision goes out, and money moves. The reading and matching is the part everyone wants to automate. But the reason claims teams are cautious about automation isn’t the reading. It’s accountability: when a claim is paid or denied, someone has to be able to say why, point to the evidence, and show a human was in the loop where it mattered. A model that adjudicates confidently but can’t be audited isn’t a solution; it’s a liability with a fast turnaround.

So the right frame isn’t “an AI that decides claims.” It’s a governed workflow where each step is legible, evidenced, and gated.

Extraction that cites its source

The workflow begins by pulling the fields a claim turns on — policy or claim numbers, amounts, dates, parties — from documents that arrive as PDFs, scans and images. Crucially, extraction is source-grounded: every value comes back with a character-level span pointing to exactly where in the document it was found. That’s the difference between “the model says the amount is X” and “the amount is X, here it is on page two.”

No citation, no claim. A value the system can’t point to in the source doesn’t get to influence a decision.

Rules decide what a human sees

Not every claim needs a person, and not every claim should skip one. A confidence-scored rules engine decides which is which — matching on the workflow step, the content, the risk level and the model’s own confidence, with reasoning attached to each match. High-confidence, low-risk, well-cited claims can proceed; anything ambiguous, high-value, or below a confidence bar is held for review.

Approval is a step, not an afterthought

When a human is needed, the workflow raises an approval card — the claim, the extracted-and-cited fields, the reason it stopped — and genuinely pauses until a decision comes back: approve, reject, or modify. That decision is recorded, feeds back into the rules, and the run resumes. The whole path — what was read, what was checked, who decided — is audit-logged, so an adjudication can be replayed rather than reconstructed.

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Claims automation earns trust the same way a good adjuster does: by showing its work. Ground every extracted fact in the source, let rules decide what a human must see, make approval a real gate, and log the trail — and you get adjudication that’s both faster and defensible, instead of trading one for the other.