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23 use casesAI Agent GuardrailsGate every agent action, memory write and retry with a cheap, typed allow / review / block decision.9 use casesLLM RoutingRoute each request to the right model, tool or intent with one fast typed decision.11 use casesLLM-as-a-Judge & EvalsScore agent runs, RAG answers and CI test evidence with low-variance typed verdicts.22 use casesClassification & TriageRoute tickets, issues, reports and applications by category, urgency and review need.11 use casesStructured Data ExtractionTurn free text into typed columns, matched entities, SQL filters and document splits.7 use casesSecurity & ComplianceGate invoice payments, validate pentest findings and lint for personal data before it ships.5 use casesGames & Adaptive UIFast typed decisions for game agents, RTS unit control and interfaces that adapt as users type.

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Jev decision flow: Use Jev as an agent action safety gateSafety

Gate every agent action with an independent allow / block / review call

Put an independent cheap Choice allow / block / review gate before every agent action—plus a Score for injection risk and a Noul for needs_human—so safety is not bound only inside the main model prompt.

See the workflow →Jev decision flow: Use Jev for adaptive UIUI

Morph a text box into the right UI

Morph one text box into the right UI card as the user types: Jev classifies intent and signals in one call, code parses every value.

See the workflow →Jev decision flow: Use Jev for agent decision gatesWorkflow

Gate agent-control steps with cheap System One decisions

A Choice, Score and Noul workflow for retry, escalate, tool pick, safety and continue gates in agent loops.

See the workflow →Jev decision flow: Use Jev as an agent-run judgeEval

Judge agent runs with low-variance typed scores

Score agent runs with bounded Choice, Score and Noul questions so your eval loop behaves like a measurement instrument—not another chatbot writing long chain-of-thought.

See the workflow →Jev decision flow: Use Jev to gate agent memoryAgents

Decide what an agent writes to memory

Decide what an AI agent writes to memory with typed questions (memory type, importance, sensitive data), and keep the expensive model for synthesis only.

See the workflow →Jev decision flow: Use Jev to verify agent outcomesAgent harness / Post-run check

Check after an agent run whether the real state change matches the business outcome, not just the agent's claims

An agent says the database job succeeded. Ask Jev whether the observed state change actually matches the intended outcome—verified, mismatch, or data loss—instead of trusting an LLM verifier that only checks the agent's own story.

See the workflow →Jev decision flow: Use Jev for AP invoice fraud gatesFinance / AP

Decide pay / verify vendor / human review / reject for each AP invoice before money moves

Gate every accounts payable invoice and payment with a typed Jev decision—pay / verify_vendor / human_review / reject—plus a fraud-risk Score and needs_human Noul, so duplicates, PO mismatches and vendor bank-detail changes never auto-pay.

See the workflow →Jev decision flow: Use Jev as a CI/CD AI quality gateCI / QA

Decide PASS / RETRY / REVIEW / BLOCK from AI test evidence in CI

Turn AI test evidence into a typed PASS / RETRY / REVIEW / BLOCK decision in CI—plus an evidence-strength Score and needs_human Noul—so the LLM produces evidence, Jev applies judgment, and the pipeline enforces policy.

See the workflow →Jev decision flow: Use Jev for Claude Code agent cascadesAgent cascade / Claude Code

Cascade Claude Code agents so Jev decides in-loop forks and Opus only takes the hard cases

Keep Opus on the plan. Let Jev answer every in-loop yes/no, tool pick, spam check and fork; hand only low-confidence or hard steps back to the frontier planner.

See the workflow →Jev decision flow: Use Jev to honor code-review rulingsCode review / Rulings

After a code-review check raises a finding on a diff hunk, decide whether a ruling a reviewer already made covers that exact hunk, so the finding is exempted, sent to a person, or kept, instead of reappearing on every pull request

A reviewer overruled a code-review finding once. Store that ruling outside the model and ask Jev whether it covers the next diff hunk, so the same finding stops coming back on every pull request.

See the workflow →Jev decision flow: Use Jev for coding-agent review gatesAgent harness / Review gate

Gate every coding-agent proposal with four narrow yes/no Jev checks and a deterministic permit / deny / caution / escalate verdict in code

Before a coding agent runs a proposed action, validate it in code, ask Jev four narrow yes/no review questions, and let a pure tested function turn those probabilities into permit, deny, caution or escalate—so the repository never executes anything on its own.

See the workflow →Jev decision flow: Use Jev to scan competitor adsAd intelligence

Classify every live ad in a competitor's ad library (creative angle, funnel stage, whether it carries an offer, how strong it is) cheaply and fast enough to scan thousands of ads across many brands, then let code count angles per brand and pull the standouts into a swipe file

Scan a competitor's whole ad library instead of scrolling it. Jev tags every live ad with its creative angle, funnel stage, offer and strength in one parallel call, so code can map what each brand is testing.

See the workflow →Jev decision flow: Use Jev as a confidence lampAgents / Confidence lamp

Route a typed intent Choice into auto-act, confirm-with-user, or escalate-to-human using Jev confidence and action severity

After a typed Choice (check balance, approve transfer, or support), read Jev confidence to auto-act, confirm with the user, or escalate to a human—thresholds track action severity, because confidence is not correctness.

See the workflow →Jev decision flow: Use Jev for crash report screeningData extraction

Screen crash narratives against a schema

Screen free-text crash and incident narratives against a fixed question schema, write the answers as columns and flag narratives that contradict the coded fields.

See the workflow →Jev decision flow: Use Jev for support-ticket routingWorkflow

Route support tickets by department and urgency

A Choice, Score and Noul workflow for ticket ownership, urgency and human review.

See the workflow →Jev decision flow: Use Jev to classify and split multi-doc PDFsDocuments

Classify inbox documents and decide multi-doc PDF split boundaries

After LiteParse (or similar) extracts text from an inbox PDF, let Jev classify the document and decide keep_whole / split_at_page / needs_review at candidate boundaries—plus a needs_human flag—so routing and multi-doc splits stay typed and gated in code.

See the workflow →Jev decision flow: Use Jev to fly a racing droneGames / Real-time control

On every camera frame, decide whether the next gate is visible and where it sits relative to the drone, so deterministic flight code can steer, climb or hold without waiting on a slow chat model

Fly a racing drone frame by frame: send each camera snapshot to Jev as a few narrow yes/no questions, and let code turn the probabilities into stick inputs. In one public test, Jev with yes/no answers out-flew multiple choice and CLM-8B.

See the workflow →Jev decision flow: Use Jev for entity resolutionEntity / Record matching

Match or reject candidate record pairs with typed same/different/review decisions

Decide whether two records or mentions refer to the same real-world entity—same_entity, different_entity, or review—with calibrated confidence, so dedupe and linking pipelines stop guessing from fuzzy string scores alone.

See the workflow →Jev decision flow: Use Jev to pre-filter agent failuresAgent observability / Failure triage

Tag every agent trace with a calibrated failure probability and failure mode, so a low threshold catches almost every failure and only the flagged slice goes to a slower high-precision reviewer

Screen millions of agent traces for reward hacking, hallucinated success and tool misuse. Jev scores every trace cheaply with calibrated probabilities; a low threshold keeps recall high and only flagged traces go to an expensive precision model.

See the workflow →Jev decision flow: Use Jev after a tool failureAgents / Failure recovery

Decide whether a failed agent step should be retried, switched to another tool, or stopped to ask the user

An agent step just failed. Ask Jev retry once, switch tools, or stop and ask—with a transient-failure score and a needs_user flag—so the harness recovers without a long chat-model post-mortem.

See the workflow →Jev decision flow: Use Jev for GitHub issue classificationDeveloper tools

Label and prioritize new GitHub issues

Label, prioritize and flag missing repros on new GitHub issues with typed Choice, Score and Noul questions, then auto-label only when confidence is high.

See the workflow →Jev decision flow: Use Jev as a grounding / hallucination judgeGrounding

Judge claim grounding against sources with pass / block / review

Check claims against source evidence with Choice pass / block / review, a Score for evidence strength, and a Noul for needs-human—so grounding gates stay typed, cheap, and calibratable.

See the workflow →Jev decision flow: Use Jev for hybrid game agentsGames

Split game agents into System 1 and System 2

Split a game agent into a fast System 1 (Jev picks the action every tick) and a slow System 2 (an LLM plans), and call the planner only when the state demands it.

See the workflow →Jev decision flow: Use Jev for edge intent extractionRouting

Extract typed intent fields at the edge

Turn a user request into four bounded intent fields in one Jev call, then validate, admit and schedule it in ordinary code instead of waiting on an LLM parser.

See the workflow →Jev decision flow: Use Jev for LLM tool routingAgents

Route requests to the right tool or skill

Pick which tool or skill an agent should load, including "none", with one typed Choice over your catalog, then auto-load, shortlist or skip based on confidence.

See the workflow →Jev decision flow: Use Jev to check memory writesAgent memory / Write check

Before an agent writes a new long-term memory, decide whether the source conversation actually supports it, whether it overstates what the user said, and whether it duplicates an existing memory, so code can save, merge, narrow or drop it instead of storing every guess

Before an agent saves a memory, ask Jev whether the source conversation supports it, whether it reads too much into what was said, and whether it repeats a memory you already have. Code then saves, merges, narrows or drops it.

See the workflow →Jev decision flow: Use Jev for criteria-based model routingRouting

Route requests to the right model tier by clear criteria

Ultra-fast, criteria-based model routing with Jev: score complexity, reasoning need and risk, then send each request to flash, balanced or frontier before the expensive model runs.

See the workflow →Jev decision flow: Use Jev for pentest findingsSecurity

Validate pentest agent findings

Pass every finding from a pentest agent through four typed gates (adjudicate, re-grade, prune, confirm) before it reaches the report, so the finder never grades its own work.

See the workflow →Jev decision flow: Use Jev to rank product needsProduct discovery / Needs ranking

Classify every product need from complaint data so a weighted ranking shows which problems are worth customer interviews

Turn a pile of user complaints into a ranked list of product needs. Jev labels each need's type, severity and whether recent launches already solve it; your code applies user-tunable weights and sorts.

See the workflow →Jev decision flow: Use Jev as RAG gates after retrievalRAG / Evidence gates

Gate retrieved evidence before the generator runs

After retrieval and before generation, ask Jev whether each chunk is relevant, whether evidence is enough to answer, and whether to generate, retrieve more, discard, or review—so RAG pipelines stop feeding noise into the LLM.

See the workflow →Jev decision flow: Use Jev to check refund tool callsTool calls / Java

Decide execute / fix args / ask customer / block for each proposed refund tool call before it runs

Before your Java or LangChain4j assistant executes a refund tool call, run a typed Jev check—execute / fix_args / ask_customer / block—plus an argument-mismatch Score and needs_human Noul, so wrong amounts and wrong orders never reach the payment API.

See the workflow →Jev decision flow: Use Jev for research paper triageResearch

Triage research papers by topic and relevance

Sort a flood of research papers by topic and relevance to your brief with typed questions, so people only read what matters.

See the workflow →Jev decision flow: Use Jev for resume screeningRecruiting

Screen resumes against a role

Screen resumes against a role with typed Choice, Score and Noul questions, shortlist only confident matches and keep a human on every rejection.

See the workflow →Jev decision flow: Use Jev for risk-agent triageRisk agent / Typed triage

Gate a risk sweep with typed threat, relevance, and severity judgments before any deep dive

A background risk agent searches the web, then asks Jev bounded threat / relevance / severity questions—so a cheap typed judge decides escalate-or-skip while a smaller generative model only writes the briefing.

See the workflow →Jev decision flow: Use Jev for RTS micro controlGames

Control units in real time while an LLM plans

Use Jev for per-unit, real-time micro decisions (targeting, spell use, retreat) under an LLM macro planner, with honest limits on what a text-only System 1 can do.

See the workflow →Jev decision flow: Use Jev as a semantic PII linterCI / Semantic lint

Decide pass / warn / fail / needs review for each changed line against meaning-level lint rules like "never log PII"

ESLint catches syntax, not meaning. Write lint rules as English yes/no questions—"does this change log PII?"—and let Jev return calibrated probabilities per file, line and rule: pass / warn / fail / needs_review, plus a PII severity Score, so CI blocks real leaks and stays quiet on safe logging.

See the workflow →Jev decision flow: Use Jev as semantic SQL filtersData / Semantic SQL

Turn free-text rows into typed SQL columns: a boolean filter, an ENUM routing value and a sortable score

WHERE clauses can't read meaning. Ask Jev plain-English questions per row—"is this customer angry?", "which team owns this ticket?", "how urgent is it?"—and get typed Noul, Choice and Score answers you can store as a boolean predicate, an ENUM column and a sortable value in DuckDB or any SQL engine.

See the workflow →Jev decision flow: Use Jev to audit every SEO pageSEO / GEO audits

Judge every page and query pair on a site (buyer intent, intent match, chance of being cited by AI assistants, first fix) cheaply enough to run on all URLs every month, then hand only the pages that need work to a writing model

Run an SEO/GEO audit on every page instead of a sample. Jev judges query intent, page match, AI citation chance and the first fix for each URL in one parallel call; an LLM only writes the fixes.

See the workflow →Jev decision flow: Use Jev to keep or drop leads before enrichmentLead gate / People search

Gate paid people-search enrichment so agents only buy contact data for leads worth keeping

Run ~1,000 leads through Jev before a waterfall across Apollo, Clay, People Data Labs, Hunter, ContactOut and Ploid. Pay enrichment only on leads Jev kept—about $0.0024 per lead instead of $2,000 subscriptions.

See the workflow →Jev decision flow: Use Jev to steer or queueAgents / Steer or queue

Decide whether a new message should steer the running tool call or wait in the queue

A user types while an agent tool call is running. Ask Jev steer, queue, or interrupt—with a relevance score and a targets_running_call flag—so the harness routes the message without a chat-model round trip.

See the workflow →Jev decision flow: Use Jev for test selectionCI / Test selection

Pick which tests to run first for a given diff

Given a diff and a candidate test, decide run_first, run_later, or skip—with a relevance score and a covers_diff flag—so CI spends the budget on tests that actually protect the change.

See the workflow →Jev decision flow: Use Jev to turn text into columnsData extraction

Turn text into typed columns

Ask many bounded questions about the same text in one Jev call and store the answers as typed columns that SQL, dashboards and rules can use.

See the workflow →Jev decision flow: Use Jev for a trading-desk verdictMulti-agent desk / Bounded call

Turn a synthesized multi-agent market state into a bounded BUY / SELL / HOLD call

After dozens of specialist agents scan a market, compress their signals into one structured state and ask Jev a bounded BUY / SELL / HOLD call—so the expensive model never drowns in raw noise.

See the workflow →Jev decision flow: Use Jev for inspectable computer-use next actionsComputer use

Pick the next computer-use action from OCR + a11y state

After local OCR and an accessibility tree snapshot, let Jev pick the next click, scroll, type, wait, or done—with a typed confidence score and a needs_human flag so every computer-use step is inspectable and gated.

See the workflow →Jev decision flow: Use Jev to ignore side talk in voice appsVoice apps / Relevance

Decide, on every end-of-turn event, whether the speech was addressed to the voice app at all and, if so, which command it maps to, so side conversations and filler are ignored instead of turned into actions

A voice-steered app hears everything in the room. Ask Jev whether each finished turn was meant for the app and which command it is, so a phone call at the next desk never moves the game.

See the workflow →Jev decision flow: Use Jev for voice turn-takingVoice agents / Turn-taking

Decide whether a voice agent should reply now or keep listening when the caller goes quiet

A caller pauses mid-sentence. Ask Jev whether the turn is done—respond now, keep listening, or backchannel—so a voice agent stops waiting on a fixed silence timer or a chat-model round trip.

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