Build blueprint
Decision Regression Harness
A test runner recording typed decisions and calibration metrics over versioned scenario suites.
- Problem
- Teams cannot safely change prompts, thresholds, or Jev model versions without seeing behavior drift.
- Why Jev
- Typed outputs and probabilities make decisions unusually amenable to regression testing.
- Architecture
- Fixture states → batch Jev evaluation → schema/calibration/diff checks → CI report.
- Current alternative
- Ad hoc prompt snapshots and manual playground testing.
- Jev advantage
- Tests the machine-facing contract rather than brittle generated wording.
- Unknowns
- API support for deterministic comparison and expected probability variance.
Why this confidence: Typed decisions create a clear developer-tool opportunity independent of broad model adoption.
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
- API support for deterministic comparison and expected probability variance.
- 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.
- Axon Work integration ObservedThe author states that Axon Work uses Jev-style model decisions with a skill/action harness.
- Routing, classification, verification, and workflow control are the dominant early mental models. PlausibleThose categories recur in the collected launch discussion and align with the documented output primitives.
- Confidence gate PlausibleAutomation proceeds above a threshold; ambiguous cases go to another check or a person.