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Ninety’s travel-allowance agent uses Sanity Context to retrieve candidate territory and presence rules, then hands day-count arithmetic to a deterministic TypeScript engine. Its central design choice is to separate what the system can retrieve, what it can classify with confidence, and what it is allowed to calculate. The implementation and evaluation described here are the project author’s account, not an independent audit—and the demo is not legal advice.
What the agent does—and what it does not decide
Ninety is designed for a question such as, “I have eleven trips logged this year and one booked — am I still legal?” It accepts a natural-language travel history, extracts dated stays, retrieves possible territories and presence rules, classifies the candidates, and calculates an allowance result.
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In the project author’s description, the model retrieves, classifies, explains, and cites. A pure TypeScript engine owns the arithmetic: the author’s stated constraint is, “Every figure it reports comes from that engine.” That is a design claim in the project article, not independently verified production behavior.
The distinction matters because a corpus of rules cannot, by itself, connect a traveler’s itinerary to the relevant dates. As Kiter puts it, “The corpus holds the rules, and never held the traveller.” Ninety’s calculation layer is intended to join the retrieved rules to the extracted itinerary and calendar rather than ask a language model to generate the final number.
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How the request moves through the system
- Extract dates and stays. The system parses the traveler’s text into dated travel segments using deterministic date extraction.
- Retrieve rule candidates. At request time, Sanity Context retrieves possible territories and presence rules from structured content. The project author says the agent retrieves the candidate set rather than inventing it.
- Classify candidates. A typed classification step, described as using TypeSafe System One, chooses among candidate rule interpretations and returns probabilities rather than free-form numerical claims. The author says results below a 0.62 confidence threshold are surfaced as uncertain.
- Calculate with the engine. The deterministic engine applies the relevant rules to the itinerary and produces any reported day count.
- Explain and cite. The agent presents the result with supporting source references from the rule records, or qualifies/refuses when the available classification is uncertain or disputed.
Sanity’s vendor guide describes Context as schema-aware retrieval over typed, related Content Lake documents using GROQ, with agent instructions managed in Studio and staged through Content Releases. Sanity’s separate guide describes the Vercel AI SDK as an orchestration and tool-calling layer while Context provides retrieval. Those are vendor descriptions of product capabilities, not an independent evaluation of Ninety.
Why the content model uses dates and separate territories
Date-banded rules
The project represents membership as rules with start and end windows, a counting status, and travel modes. This structure lets the agent retrieve a rule for the relevant period instead of treating a rule as timeless. The project author reports that date-banded access rules are queried as structured data; the rules themselves should not be treated as current travel guidance.
Geographic carve-outs
Geographic exceptions are represented as their own territory documents. That gives retrieval a way to distinguish a territory from a nearby or associated area rather than silently treating a broad geographic label as sufficient.
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Counted, excluded, and disputed presence
Presence rules can be marked counted, not_counted, or disputed. In the described design, a disputed case is not collapsed into one confident answer: the engine refuses to resolve the disagreement as if it were settled. Rule records also carry source references so the answer can point back to its basis.
What happens when the itinerary is ambiguous
The project article illustrates the design with travel involving Tenerife, an airport layover without clearing immigration, and a train journey into Bulgaria. Ninety separates the legs, applies the project’s date-banded rule to the Bulgaria dates, treats an unnamed airside transit as not counted, and refuses to resolve a fragment it considers too uncertain.
This example demonstrates the intended behavior of the system, not the validity of the underlying travel interpretation. The author says the tool covers only a handful of passport classes and refuses cases outside that scope. It is not a source of current eligibility guidance or legal advice.
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Why the calculator must not trust the classifier for numbers
The author describes a Bulgaria-by-train bug that exposed a boundary between interpretation and calculation. The classifier returned a misleading presence label, but the engine correctly determined the counted days from the ledger. The fix was to display the count from the engine output rather than derive it from the classifier’s opinion about whether the travel was chargeable.
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What the project’s evaluation does—and does not—show
Kiter reports a 23-case adversarial evaluation in 2026. The figures below describe the project’s own setup and are not an independently audited benchmark or a general comparison of retrieval systems.
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| Approach in the project evaluation | Exact answers | Produced a day count | Scope |
|---|---|---|---|
| Structured retrieval plus deterministic engine | 23/23 correct | 21/23 | Project-author-reported result on 23 adversarial histories, 2026 |
| Keyword/BM25 retrieval over the same corpus flattened into prose | 1/23 correct | Not stated in the project article | Project-author-reported comparison on the same 23-case setup, 2026 |
The 21/23 day-count result is not evidence that every case was answerable: the project’s design explicitly allows uncertainty and refusal. The author also reports 56 unit tests, without publishing an independently verifiable test run in the cited article. The evaluation offers a small example of how this particular structured system behaved against a flattened-corpus comparison; it does not establish that structured retrieval will outperform keyword retrieval in every domain or setup.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Sanity Context’s role and the Knowledge Base boundary
Ninety uses Sanity Context for structured rules but, according to its author, does not use a Sanity Knowledge Base. EU guidance documents are referenced as sources rather than ingested as searchable text. Consequently, the described agent cannot retrieve and quote passages from those underlying guidance documents through its knowledge base.
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Sanity’s vendor materials also describe hybrid retrieval combining semantic similarity with exact-term matching and say dataset embeddings are tied to content updates. Those statements describe the vendor’s product framing; they should not be read as guarantees about latency, price, or service availability.
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Implementation lessons from the author’s account
- Deployment can be a prerequisite. The author says the Context endpoint initially required a deployed Studio.
- Batch results need explicit binding. Batched classification questions were not reliably associated with their corresponding stays.
- Small parsing assumptions can break dates. A date-stripping regular expression did not behave as intended.
- Validate identifiers independently. An incorrect territory code let a case pass for the wrong reason.
- Test mutation and reference behavior. The author reports Sanity Function guard issues involving lazy patches, transaction reuse, and resolving a referenced dispute kind.
These are implementation issues reported by the project author, not a claim that every Context integration will encounter the same failures. Together, they point to concrete checks for a similar system: verify deployment assumptions, preserve the mapping between each stay and its classification, test date parsing against varied inputs, validate identifiers, and exercise function behavior with realistic patches and references.
When this pattern is useful
This architecture is a fit when a system needs to answer questions from maintained, structured rules and then perform calculations that must be reproducible. The design is strongest when the source of truth exposes the fields and relationships retrieval needs, rule changes are time-bounded, and uncertain or conflicting cases can be surfaced rather than forced into a single answer.
It is not enough to add retrieval and call the result reliable. Teams still need to decide which source owns each value, how rule provenance is carried through, who reviews agent instructions, and what happens when classification does not meet the confidence threshold. Sanity’s vendor guidance presents schema-aware retrieval and Studio-managed instructions as capabilities intended to support those concerns; Ninety’s reported results remain specific to its own implementation and test set.
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