Patterns
TypeSafe docs page on decision patterns: speculative fan-out, confidence-gated routing, composite scoring and intent routing.
This summary is based on how curated lists describe the project; its README was not available to review.
Overview
This TypeSafe documentation page describes common patterns for using Jev, according to the list summaries: speculative fan-out, confidence-gated routing, composite scoring and intent routing. These patterns show how to combine typed decisions with thresholds and multiple questions in application code.
The page itself was not fetched, so details of each pattern are not summarised here. It is a useful starting point for developers designing how their code should act on Jev's probabilities.
What the decision model decides
| Type | Question | Answers |
|---|---|---|
| Choice | Intent routing: which handler should take this request?(inferred) | — |
| Yes / no | Confidence gate: act automatically or escalate?(inferred) | act · escalate |
How it works
- Choose a pattern that fits your workflow.
- Ask Jev the relevant typed questions.
- Apply thresholds or combine scores in code.
Strengths
- Official guidance on structuring decision logic.
Caveats
- Content not reviewed directly.
Getting started
Read https://docs.typesafe.ai/patterns.At a glance
- Type
- Article
- Status
- production
- Runtime
- hosted API docs
- Has code
- no
- Has tests
- no
Use cases and integrations
How curated lists describe it
Speculative fan-out, confidence-gated routing, composite scoring, and intent routing.
hellogumbo/awesome-jevConfidence-gated routing, speculative fan-out, composite scoring, and intent routing.
MrJev/awesome-jev
Frequently asked questions
Which patterns are covered?
Speculative fan-out, confidence-gated routing, composite scoring and intent routing.
Is this official?
Yes, it is TypeSafe documentation.
Does it include code?
Likely examples, but the page was not reviewed here.
Related projects
Agent skill and CLI that chains Jev question design: find decision calls, check fit, draft, lint, calibrate thresholds and plan a cascade.
Agent skillprototypePythonRunnable Jev cookbooks, a plain-English guide and sixteen interactive simulations of how a typed decision model changes architecture.
AppbetaPythonChinese-language tutorial repo with six runnable Python demos and four notes on Jev primitives, with offline mock mode and live samples.
ServiceprototypePythonEducational site with six bounded recipes and a custom builder for AI SDK Choice, Score and Boolean evaluations, exportable as TypeScript.
ServiceTypeScriptPython sandbox turning Jev issue-category and reproduction judgments into testable routing proposals, with offline replay.
ProjectprototypePythonPlaybook for using Jev as a cheap gate before expensive agent steps: confidence policy, question recipes, a skill and a router.
Projectprototype