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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Use System One (Jev) as a typed relevance gate before a .NET LLM tool loop: score each discovered agent skill against the latest request and recent conversation, then expose only skills that clear an application-defined threshold. In Oleh Halay’s implementation, this selection happens at skill level rather than handing the model every discovered agent card. The pattern can narrow the tools and capability descriptions offered downstream, but it adds a hosted API call and has no independent performance benchmark in the cited sources.
Why filter at the skill level?
An agent card can describe multiple capabilities. If an orchestrator converts every discovered AgentCard into an AIFunction, the LLM must route among all of those descriptions, including ones unrelated to the current request. Halay’s alternative is to score each skill separately, then make the selected skills determine which agents or tools—and which narrower capability descriptions—enter the tool loop. He describes the approach as a “pre-LLM gate: score each agent skill against the request, expose only the winners.” Read the implementation tutorial.
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The boundary matters: Jev returns structured relevance judgments; application code applies the threshold and controls tool exposure. In this flow Jev is not generating the chat response.
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- Discover agents. Retrieve the remote agents and their cards.
- Flatten cards into skill rubrics. For each skill, retain its agent name, skill name, description, and available tags.
- Build one question per skill. Use the latest user request and recent conversation as the shared state, so follow-ups such as “and the shipments?” are not judged without their context.
- Batch the questions. Send the questions together in one request to
POST https://api.typesafe.ai/v1/systemone. - Apply the gate in your code. Compare each returned score with the configured threshold.
- Build the downstream tool loop. Offer only the selected skills and their relevant descriptions to the LLM.
The official API reference specifies a request with state, model, and a map of named typed questions; its response contains an answer under each matching question key. Authenticate with a Bearer API key. See the System One API reference.
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Choose a typed question and a rubric
System One supports three question types: Noul returns a yes/no probability, Choice selects an option and provides its distribution, and Score returns a probability-weighted value across ordered levels. A Score can fall between levels; its answer includes a score, legend, probabilities, and confidence. For relevance ranking against an ordered scale, Score is the fitting primitive. TypeSafe recommends focused judgments and composing answers in application code; questions sharing the same state can be sent together and are evaluated independently. Read the primitives guidance.
Halay’s example asks, “How relevant is this skill to answering the user’s latest request?” Its two ordered criteria are “Not needed; the request can be answered fully without this skill.” and “Needed; the request (or part of it) requires this skill.” The sample sets RelevanceThreshold to 0.6. That is the tutorial’s choice for this two-level rubric, not a universal recommendation or an API default. Adding another criterion changes the score scale, so the same threshold no longer has the same interpretation.
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Understand what the example scores show
For the request “How much stock is left for the winter coat?”, the tutorial illustrates these outputs:
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| Skill | Illustrative score | Result at the example 0.6 threshold |
|---|---|---|
| GetProduct | 0.21 | Not selected |
| GetActiveCatalog | 0.06 | Not selected |
| GetStock | 0.96 | Selected |
| GetShipments | 0.44 | Not selected |
These are illustrative response values, not results from a validation set or evidence of general routing accuracy. The tutorial also shows a combined follow-up request that allows GetProduct through. Its displayed example usage is 512 input tokens and 24 output tokens; those figures describe that example only, not typical usage or a cost benchmark.
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The tutorial’s request uses model: "jev-latest", and its sample response reports jev-1.13.0. The API reference describes jev-latest as the flagship model alias, but the sample’s resolved version is not a guarantee of current alias behavior.
Set the threshold against the cost of mistakes
A threshold trades off two failures: including irrelevant tools, which weakens the point of narrowing the tool loop, and excluding a skill the request actually needs, which can prevent the task from being completed. Because a gate can hide required tools, decide which error is more costly for your application before settling on a cutoff.
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- Create labeled examples pairing realistic requests and conversation context with the skills each request actually needs.
- Evaluate whether the gate retains needed skills (recall) and excludes unneeded ones (precision) at candidate thresholds.
- Include ambiguous follow-ups and requests that require more than one capability.
- Revisit the evaluation whenever you change the rubric, since changing its ordered levels changes the scale that the threshold acts on.
This is evaluation advice for a thresholded selector, not a reported result from the tutorial. The reviewed sources do not establish independent production measurements for routing accuracy, recall, latency, token reduction, or total cost.
Account for the extra API hop
Halay identifies one additional classifier call and one blocking hop before the first token. He also notes that, in an otherwise local-inference stack, the hosted System One call is the only external dependency on the chat path. The intended architectural effect is that fewer irrelevant tools and descriptions reach the LLM; the sources do not independently measure whether that improves speed, quality, or cost.
The tutorial describes a pass-through selector when a TypeSafe API key is not configured, allowing selection to continue without the scoring gate. The API reference lists 401 for a missing or invalid key, 422 for an invalid request body, 429 for a rate-limit breach, and 529 for temporary overload. A production implementation should decide how to handle those failures—including whether to retry or fall back—but the tutorial does not specify a complete error-resilience policy.
When this pattern fits
Skill-level scoring is most relevant when discovery yields many agent capabilities and exposing every one would burden the downstream tool loop. Compared with offering all agents, it adds an external classification step in exchange for the option to narrow what the LLM sees. Whether that trade is worthwhile depends on measured recall, precision, and operational constraints in the application—not on the illustrative scores alone.
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