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In Claim Bench, an AI agent can extract a rule claim and flag conflicting evidence, but it cannot approve the claim. The project’s write-up describes a separate human approval step: an agent’s attempt to approve returns HTTP 403, while a human approval records the human as decision maker. These are the author’s reported design and demo results, not independently verified behavior or evidence of production deployment. Read the project write-up.
What Claim Bench separates
Claim Bench is described as a review workflow for CabinClaim, a desk for answering cabin-rules questions from dated rule claims. It keeps the evidence and the review decision distinct: a ruleClaim holds the rule sentence and its supporting details, while a separate claimWorkflow document tracks the claim’s progress through review.
The claim record is described as containing the official sentence, operator, value, dates, source URL and quotation. The workflow record holds the state, last actor, decision maker, decision time and agent attempt count. That separation is intended to let an agent advance a review without rewriting the source sentence.
How a claim moves through review
The reported sequence is ingested → extracted → disputed or approved → superseded. The agent may extract a claim or mark a conflict as disputed; approval is reserved for a person.
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When an agent tries to approve
In the described two-seat demo, the agent seat attempts approval and receives HTTP 403. The claim stays extracted, the decision-maker field remains empty, and the attempt counter increases. The author’s stated API rule is: “The agent’s hand cannot hold the stamp. Only a person can approve.”
When a person approves
Switching to the human seat and approving changes the claim state to approved and records the human as decision maker. This makes the distinction more than a label in the interface: the write-up attributes the rejection to an API transition route.
When sources conflict
The demo also shows a spare-count pair from United and TSA sources remaining disputed rather than having the agent choose one. That is the project author’s description of the example, not a current determination of either United’s or TSA’s baggage policy.
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The author identifies Sanity as the content platform, with separate ruleClaim and claimWorkflow schemas, and describes a custom Next.js bench plus an API transition route that blocks agent approval. The write-up also reports that the bench polled a queue endpoint every four seconds because the then-current App SDK live bindings assumed Sanity Dashboard authentication, which would not work for the no-login contest demo. That polling interval and SDK explanation are time-sensitive implementation details, not general guidance about current Sanity behavior.
A separate workflow record can make it possible to query claims by review state, while retaining actor and decision-time fields for the approval record. The account supports describing the intended data model and server-side guard; it does not establish independent security testing, reliability under production conditions, or that the demo remains available or unchanged.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to take from the design
- Extraction and approval are separate capabilities in this project; permission to extract does not grant the agent authority to approve.
- A source conflict can remain visible as disputed instead of being silently resolved by the agent.
- The author describes approval as a server-side transition check, rather than relying solely on hiding or disabling an approval control in the interface.
Claim Bench illustrates one concrete human-review gate for claims based on dated rules. It is not evidence that every AI workflow needs the same state model; the appropriate controls depend on what is being reviewed and the consequences of an incorrect approval.
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