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Freedom251 › Blog › AI agents

An Agent That Counts My Receipts, Not My Claims: Evidence Boundaries in a Sanity-Backed Demo

By Freedom251 Team · · 6 min read

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The agent counts locatable receipts, not recorded claims. In the project’s Finding B8, three outside finders are recorded, but the agent could locate only one matching comment in the comment trees it searched. That gap is the point of the design: a name in a record is not the same thing as a source the agent can retrieve and cite.

What the record holds

The project author describes a public content dataset in Sanity with 120 documents: 74 articles, 14 findings, 10 people, 3 patches, and 19 claims. This is the author’s count for the snapshot described in the project article, posted September 21, 2026, not a live figure.

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The data model keeps distinctions apart that prose tends to blur. Each claim document stores asOf, status, and expiryStatus as separate fields. Each finding separates the article where a comment appeared from the article where the issue was written up. A reader can therefore ask whether a claim was standing at a given date without the answer being folded into a single label.

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Recorded finders versus locatable receipts

Finding B8 is the clearest example of the boundary. The record names three outside finders. The agent, searching the comment trees available to it, located one matching comment: 3ee98, by Pushpendra. The other two finders’ receipts were not found in the searched trees.

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Item for Finding B8 Count or value What it establishes
Recorded outside finders 3 What the record says. Not checked against comments on its own.
Locatable matching comments in searched trees 1 (3ee98, Pushpendra) What the retrieval instrument found in the evidence it searched.
Recorded finders without a located receipt 2 Not established by the searched trees. This does not show that those people did not find the defect.

The project author states it directly: “It does not turn three recorded names into three verified receipts.” The sentence is the working rule of the whole system. A count the agent reports is a count of what it could locate, and it should be read that way.

How the agent routes a question

The author first tried a Knowledge Base route. It handled four prose-shaped questions in the graded run. It could not reliably return the status and expiry of a specific claim bound to the identifier claim-ledger-population. The indexed content was 24 Knowledge Base entries totalling 190,503 characters. The identifier appeared once, as a label in a Sources list. The field values for that claim were present in the content, but they were not reliably bound to that identifier.

The fix was a second route: a Context endpoint that runs GROQ queries over the same dataset. For that structured claim lookup, the agent returned the following record result:

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  • status: standing
  • expiryStatus: no_expiry_set
  • asOf: 2026-09-11
Question shape Endpoint Reason given by the author
Prose questions (four of five frozen questions) Knowledge Base Retrieval suits open-ended text.
Field lookup on a specific claim identifier GROQ on the Context endpoint The field is bound to a stable identifier in structured data.
Any token beginning claim- Dataset endpoint This is broader than the intended rule. The author acknowledges it.

The practical lesson is that prompt instructions cannot repair a missing binding. If a question asks for a field on a particular structured record, query the structured dataset by identifier and field. Asking a model to infer that relationship from unbound prose is the failure this design was built to avoid.

What a clean answer contains

A clean answer carries five fields:

  • ANSWER: the response to the question.
  • SOURCES: the citations the answer relies on.
  • EVIDENCE DATE: taken from the record’s asOf field, not from the current date.
  • VERDICT: a claim or finding state, an expiry state, or INSUFFICIENT_EVIDENCE.
  • UNCERTAINTY: what the sources do not establish.

The author notes one weakness in the verdict vocabulary. The enum combines concepts that the underlying data model keeps distinct, so a single verdict value can carry more than one meaning. Readers should look at the separate fields when precision matters.

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When retrieval fails or authorization is refused, the endpoint returns an error rather than an answer. The browser never receives server-side keys.

What the checks do not prove

The validator confirms that required fields are present and that citations follow the expected format. It does not resolve URLs. It does not establish that every citation belongs to the evidence the agent actually retrieved. It also does not yet re-verify each returned value against its source document, field by field.

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So a citation that is present and well formed has passed a format check only. It has not been shown to support the answer. The author’s own term for this is an unresolved validator limitation, and it should be read as one. Citation syntax and retrieval success are not entailment checks.

Abstention has a similar limit. For a Kubernetes question, the agent returns INSUFFICIENT_EVIDENCE rather than a count, because the Knowledge Base evidence available does not establish one. The author does not claim that the dataset could never yield a count, or that every abstention is free of unsupported claims.

Snapshot limits on record-state questions

The article gives three project-record examples. Patch B1 is recorded as not merged into origin/main. Two of fourteen findings are recorded as implemented. The system says it cannot see what happened to a branch after the record snapshot.

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These are statements about the author’s record as of its snapshot. They are not an independent check of current repository state. An agent that answers a mutable-state question well should bound the answer to the snapshot date and say what it cannot observe after that date.

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What the five-of-five result means

The author reports that a corrected v8 CLI harness held all five frozen questions on one independent graded run, with exit status 0, using gemini-3.6-flash. Earlier runs exposed wrong-object routing, failed retrieval reaching the model, weak citation enforcement, and an unparseable verdict that bypassed verdict-dependent checks. The author says those versions and transcripts were kept.

An earlier blocked run used a different model. The author does not claim the same model failed and then passed, so the comparison should not be read as a before-and-after test of one model.

The narrow conclusion is the right one. The reported version passed that five-question run under the described harness. That does not establish general reliability across other prompts, datasets, models, or records that change over time. Deployed answer latency was reported at 13.8–33.8 seconds across the five questions. This is the author’s deployment measurement, not an independent benchmark.

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Questions to ask and what the agent can and cannot establish

The project’s interface takes plain questions. The literal prompt in the article is “Ask the agent how many locatable receipts support that row.” Other questions that fit the demonstrated design include:

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  • How many receipts can the agent actually locate for this finding?
  • Does the evidence support the number recorded in the ledger?
  • Is this patch in main, or only pushed?
  • What is the claim’s status and expiry status as of the recorded date?
  • What can the agent not establish from the sources it retrieved?

For each of these, a sound answer names the count it found, the date it is bound to, and the gap. A count of one with two unlocated finders is a complete answer. It is not a failure.

The project is built on Sanity’s content platform and its Context functionality. The article describes software, data, and a web demo. It does not recommend specific hardware, tools, or other products, and this article does not either.

Because the demo, repository, and platform behaviour can change, check their current status before describing any of them as live or unchanged. The project article is primary evidence for what the author says was built and measured. It is not an independent audit of the implementation.

For a reader building something similar, the author’s comparison points are useful: whether the data is structured and bound to stable identifiers; whether the retrieval route suits the question shape; whether the validator checks citation presence or claim-to-evidence support; how abstention and uncertainty are represented; and how evaluation results are scoped across versions, prompts, and models.

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Keep the count separate from the claim. A recorded name, a located comment, and a verified fact are three different things, and an agent that treats them as one will be wrong in a way that looks like confidence.

What the agent cannot tell you is whatever its searched evidence does not contain. For Finding B8, that means the two other finders may have found the defect, and the searched comment trees simply do not show it.

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AI agentsCitationsevidenceGroqretrievalSanity

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