jev-decisionops
Offline-by-default demo of six Jev decision workflows: model tiering, prompt screening, tool-call risk, reranking and more.
Overview
Jev DecisionOps v3 is an interview-oriented, runnable demonstration of six decision use cases with Jev as the engine. By default it runs an offline end-to-end simulation with deterministic decision rules, real local retrieval and extractive generation; live Jev and OpenAI adapters are opt-in and need credentials.
The workflows include LLM model selection (small, medium or large tier), prompt screening (allow, review or block), tool-call risk (allow if authorised, approve or deny, with a hard-coded deny-list that always wins), passage reranking with Score, and further steps. Version 2 adds a multi-step orchestration API and dashboard chaining retrieval, passage scoring, model-tier routing, optional generation, answer evaluation, confidence gating and sandbox remediation drafts. The README is explicit that vector databases, external tools and real human approval are future work.
What the decision model decides
| Type | Question | Answers |
|---|---|---|
| Choice | Which LLM tier should handle this request? | small · medium · large |
| Choice | Should this prompt be allowed, reviewed or blocked? | allow · review · block |
| Choice | What should happen to this tool call? | allow-if-authorized · approve · deny |
| Score | How relevant is this passage? | — |
How it works
- Start the app (offline simulation by default)
- Retrieve passages locally and score them
- Route to a model tier and optionally generate
- Evaluate the answer and gate on confidence; draft remediation in a sandbox
Strengths
- Zero pip dependencies; runs with no keys
- Shows how deterministic policy wraps each decision
- Per-stage traces
Caveats
- Built as an interview demo
- Default mode does not call Jev
- Several integrations are placeholders
Getting started
`python app.py` and open http://localhost:8765 (offline simulation, no keys needed).At a glance
- Type
- Project
- Status
- prototype
- Language
- Python
- Runtime
- Local web app
- Has code
- yes
Use cases and integrations
How curated lists describe it
Six Jev decision workflows for agentic AI: model routing, guardrails, tool-call…
wh000wh000/awesome-jev-live
Frequently asked questions
What does Jev decide?
Model tier, prompt screening, tool-call risk, passage relevance and related gates.
Does it call Jev by default?
No. It runs a deterministic offline simulation unless live mode and keys are configured.
Is it production software?
No, it is a demonstration built for interviews.
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