Build blueprint
Probabilistic Rules Engine
An open-source engine combining Jev judgments with auditable deterministic conditions.
- Problem
- Business rule systems are deterministic even when concepts such as urgency, fit, or risk are fuzzy.
- Why Jev
- Jev supplies probabilistic predicates; ordinary code composes and enforces them.
- Architecture
- Typed state → named Jev predicates → threshold/versioned rules → action and audit log.
- Current alternative
- Huge decision tables or LLM prompts returning JSON.
- Jev advantage
- Treats intelligent judgment as a testable rule primitive rather than an autonomous agent.
- Unknowns
- Threshold governance, drift monitoring, and debugging expectations.
Why this confidence: This directly exposes Jev's typed probabilities as a software primitive.
This is an authored hypothesis derived from the research corpus. Nothing here demonstrates product demand, or that Jev performs well on this particular workload. Run the validation experiment before building past the MVP.
Sources
Limitations
- Threshold governance, drift monitoring, and debugging expectations.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Probabilistic predicate + deterministic action PlausibleJev supplies fuzzy predicates while TypeScript, policies, and workflows execute constrained actions.
- MAGI System on Jev ObservedAn open-source three-sage voting experiment inspired by Neon Genesis Evangelion.
- Cheap decision calls could make tens or hundreds of semantic judgments per event economical. SpeculativeThe product design follows from published pricing and parallel question primitives, not independent production evidence.
- Cascade router PlausibleA cheap decision chooses whether to use rules, a small model, a premium model, a specialist, or a human.