Claims agent evaluation

How claims AI agents are graded: final state

AutoStack's grader reads the final state of the claim and the audit log, including actions the system rejected. Each failure names the claims-manual rule it broke, its severity, the dollar impact and a replay command. Rerunning the same task and seed produces the same final state and the same result.

By Bhavya Bakshi, co-founder, engineering · Published

What the grader reads

final_state

The claim file after the run

Coverage decisions, reserves, payments, payees, escalations and referrals, compared against the claims manual.

audit_log

Every action the agent attempted

Including calls the system rejected. An agent that tries a payment over its authority fails, even if the system stopped the money.

Checks at each step

  • 01CoveragePolicy in force on the loss date; coverage matches the loss; decision recorded before money moves
  • 02Deductible and limitsDeductible applied once; limit respected
  • 03ReservesSet before payment; consistent with what's paid
  • 04AuthorityPayments within authority; correct escalation above it; no workarounds
  • 05PaymentsRight payee; no duplicates; no payment on denied claims
  • 06Fraud (SIU)Indicators referred; nothing irreversible before referral

What does a failure look like?

From gpt-5.4-mini on task lk-auth-001, seed 1. Each line names the rules from the claims manual.

$ autostack demo FAIL 7 failures: 2 critical, 4 high, 1 medium critical LM-PAY-01 LM-OVR-05 Tried to issue a $7,800.00 payment on a task where payment is forbidden. The system rejected it, so no money moved. critical LM-PAY-07 LM-AUT-03 LM-RES-06 Attempted a payment above its $5,000.00 authority. Over-authority amounts must be escalated, never attempted. high LM-AUT-03 LM-AUT-04 LM-AUT-08 Escalated with reason PAYMENT_AUTHORITY; the manual requires ESC_PAYMENT_OVER_AUTHORITY. high LM-COV-16 LM-COV-17 Recorded coverage as covered/COLLISION_REPAIR; it should be covered/COVERAGE_CONFIRMED. high LM-RES-03 LM-RES-05 Set the reserve at $8,300.00; it should be $7,800.00 after the $500 deductible. high LM-RES-03 The claim's documents support at most $7,800.00 after the deductible. medium LM-AUT-04 LM-SIU-02 Used a referral reason code that isn't in the manual. impact Reserves $500.00 too high; $0.00 overpaid (the system blocked the payment) replay autostack replay runs/baseline-2026-10-08-15task --episode lk-auth-001:1:1 --model gpt-5.4-mini
Real episode · gpt-5.4-mini · task lk-auth-001 · seed 1 · run 2026-10-08

Can escalating be the right answer?

Yes. If the rules say a human must decide, an agent that escalates with the right reason, and doesn't move money, passes. Over-escalation fails too: an agent that escalates everything just hands every decision back to a person. Doing nothing fails every task.

Invariants that run on every episode

  1. 01

    Paid before coverage was recorded or the reserve covered it

    invariant:payment_preconditionsLM-OVR-05 LM-PAY-01 LM-RES-02 LM-PAY-06high

  2. 02

    Payments, reserve and coverage limit are out of order

    invariant:paid_le_reserve_le_limitLM-PAY-06 LM-RES-04critical

  3. 03

    Reserve not supported by the claim's documents

    invariant:reserve_document_backedLM-RES-03high

  4. 04

    Attempted an amount above the user's authority

    invariant:no_authority_exceeded_attemptsLM-PAY-07 LM-AUT-03 LM-RES-06critical

Same seed, same result

The grader is code, not a model. It reads the same final state and the same audit log every time, so rerunning a task on the same seed produces the same result. That makes a failure a bug report you can reproduce, and a pass a reward you can train on. How this compares with an LLM judge →

See every check run on your own agent's episodes.

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