Build Ideas

These are hypotheses grounded in the documented typed-decision interface, collected X discussion, and located projects. Confidence reflects evidence and MVP tractability—not guaranteed demand.

1. Agent Tool Firewall

Problem: Autonomous agents can select destructive or irrelevant tools from large catalogs.
Product: A local gateway that scores every proposed tool call and requires confirmation only when risk is high.
Why Jev: Tool execution needs a typed allow/deny/escalate decision with confidence, not generated prose.
Architecture: Agent proposal → Jev risk and intent questions → deterministic policy thresholds → tool or approval UI.
Current alternative: Prompt-based guardrails, static allowlists, or an LLM judge on every call.
Jev advantage: A decision can run inside every agent step while preserving explicit policy code.
1–7 day MVP: Build an MCP proxy for five filesystem and shell tools with a macOS confirmation panel.
Validation experiment: Replay 200 benign and adversarial tool calls; measure unsafe allows, needless prompts, latency, and cost.
Evidence: source 1, source 2, source 3
Unknowns: Calibration under prompt injection and whether confidence remains stable across tool schemas.
Confidence: HIGH — The typed-decision interface directly matches tool gating, though safety still needs deterministic backstops.

2. Agent Model Router

Problem: Agents routinely send easy steps to expensive reasoning models and hard steps to models that fail.
Product: A drop-in router that selects model, reasoning effort, and fallback policy per step.
Why Jev: Routing is a repeated bounded choice plus confidence score.
Architecture: Request state → Choice(model) + Score(complexity) → route → outcome logging → offline evaluation.
Current alternative: Regex rules, hand-tuned heuristics, or another full LLM call.
Jev advantage: Routing overhead may remain small enough to apply before every inference.
1–7 day MVP: Proxy two inexpensive and two premium models for a coding-agent benchmark.
Validation experiment: Compare task success, total spend, and latency against cheapest-only and premium-only baselines.
Evidence: source 1, source 2, source 3
Unknowns: Whether the router generalizes to unseen tasks and remains cheaper after retries.
Confidence: HIGH — Routing is repeatedly cited in the source material and maps naturally to Choice.

3. CI Review Triage

Problem: Repositories waste reviewer attention on harmless diffs while risky changes can look deceptively small.
Product: A GitHub check that assigns review depth, owners, and required test classes to every pull request.
Why Jev: Each diff needs several parallel labels and risk scores rather than a review essay.
Architecture: Diff metadata → parallel risk/security/test questions → branch protection policy → check run.
Current alternative: CODEOWNERS, path rules, or expensive LLM review of every diff.
Jev advantage: Semantic triage can happen on every push before deeper reviewers are invoked.
1–7 day MVP: GitHub App for TypeScript projects that emits low/medium/high risk and recommended suites.
Validation experiment: Backtest on 100 merged PRs and compare labels with bugs, reverts, and reviewer choices.
Evidence: source 1, source 2, source 3
Unknowns: How much code context fits without losing calibration.
Confidence: MEDIUM — Strong primitive fit; independent accuracy evidence is not yet available.

4. Notification Interruptibility Engine

Problem: Notification systems use crude per-app settings and interrupt at the wrong moment.
Product: A macOS/iOS layer that decides deliver, batch, summarize-later, or suppress for every event.
Why Jev: Continuous, private context requires cheap decisions per notification.
Architecture: Notification + focus/calendar/device state → delivery Choice and urgency Score → OS action.
Current alternative: Focus modes, keyword filters, or cloud LLM summaries.
Jev advantage: Thousands of tiny judgments can shape attention without generating content.
1–7 day MVP: A macOS menu-bar app using calendar state and a local notification inbox simulation.
Validation experiment: Run a one-week shadow mode and ask users to approve or correct every proposed action.
Evidence: source 1, source 2, source 3
Unknowns: Platform interception limits and privacy expectations for notification content.
Confidence: MEDIUM — Compelling high-frequency use case, but OS APIs may constrain the product.

5. Semantic Feature-Flag Allocator

Problem: Feature flags target static cohorts but cannot react intelligently to the current session.
Product: A policy layer selecting safe UI variants based on session intent, friction, and risk.
Why Jev: Allocation needs a typed variant choice at request time with uncertainty-aware fallback.
Architecture: Approved session features → Jev variant Choice → deterministic eligibility checks → flag result.
Current alternative: Rule trees and broad A/B cohorts.
Jev advantage: Enables per-event adaptation while code retains the final eligibility boundary.
1–7 day MVP: SDK and dashboard for three variants in a sample SaaS onboarding flow.
Validation experiment: Shadow against existing rules and inspect disagreement quality before any live allocation.
Evidence: source 1, source 2, source 3
Unknowns: Fairness, explainability, and feedback loops from adaptive assignment.
Confidence: LOW — Architecture is novel but carries experimentation and fairness risks.

6. Support Action Gate

Problem: Support automation either stops at drafting text or grants an agent dangerously broad authority.
Product: A decision layer approving refunds, credits, escalations, and account changes within explicit limits.
Why Jev: The useful output is an action class and confidence, with deterministic dollar and permission caps.
Architecture: Ticket state → action Choice + fraud/urgency Scores → policy engine → execute or human queue.
Current alternative: Manual macros or an LLM agent with extensive prompt constraints.
Jev advantage: Separates judgment from execution and allows frequent re-evaluation as state changes.
1–7 day MVP: Sandbox against a CSV of historical tickets with simulated refund actions.
Validation experiment: Compare proposed actions with historical resolutions and specialist review.
Evidence: source 1, source 2, source 3
Unknowns: Domain-specific calibration and access to representative support data.
Confidence: MEDIUM — Explicitly aligned with workflow decisions, but production action requires conservative policy.

7. Inbox Micro-Router

Problem: Email rules fail on semantics, while generative inbox agents are costly and overpowered.
Product: An email client companion assigning folder, urgency, next-action, and safe automation eligibility.
Why Jev: Each message creates multiple small parallel decisions with no need for generated text.
Architecture: Message + relationship state → parallel Choice/Score questions → local rules → label or queue.
Current alternative: Filters, priority inbox classifiers, and LLM assistants.
Jev advantage: Can classify every inbound message and reclassify threads after replies.
1–7 day MVP: Local IMAP shadow classifier with an approval queue and accuracy dashboard.
Validation experiment: Measure corrections and time-to-important-message over two weeks.
Evidence: source 1, source 2, source 3
Unknowns: Provider access, sensitive content handling, and multilingual quality.
Confidence: MEDIUM — Well-bounded decisions and a feasible shadow-mode MVP.

8. Calendar Commitment Guard

Problem: Calendars accept meetings without judging context switching, preparation burden, or strategic value.
Product: A scheduling guard that classifies each invitation and proposes accept, decline, delegate, or request agenda.
Why Jev: The core operation is a repeated typed decision over calendar and relationship state.
Architecture: Invite + calendar + user policy → decision probabilities → deterministic availability checks → suggestion.
Current alternative: Static scheduling rules or manual review.
Jev advantage: Semantic policy can run for every invitation without composing messages unless requested.
1–7 day MVP: Google Calendar read-only companion with a daily review feed.
Validation experiment: Compare suggestions with the user's actual decisions for 50 invitations.
Evidence: source 1, source 2, source 3
Unknowns: Sparse personal training signals and sensitive relationship context.
Confidence: MEDIUM — Clear indie MVP, with value depending on personalized accuracy.

9. Agent Loop Stopper

Problem: Agents continue after diminishing returns, repeat failed actions, or stop before verifying outcomes.
Product: A loop controller deciding continue, retry differently, verify, ask, or stop after every step.
Why Jev: Agent loops require many low-latency state judgments where prose is overhead.
Architecture: Trace state → progress/failure Scores + next-control Choice → hard iteration budget → agent runtime.
Current alternative: Fixed step limits and LLM self-reflection prompts.
Jev advantage: Makes control a first-class typed layer and may reduce wasted premium-model calls.
1–7 day MVP: Middleware for one open-source agent with trace replay and stop-decision visualization.
Validation experiment: Run SWE-style tasks; compare completion, wasted calls, and premature stops.
Evidence: source 1, source 2, source 3
Unknowns: Reliable progress detection from partial traces.
Confidence: HIGH — Directly uses cheap decisions inside agent loops, a central Jev-native design space.

10. MCP Capability Router

Problem: Large MCP tool sets overload model context and increase incorrect tool selection.
Product: A proxy exposing only the small capability subset relevant to the current step.
Why Jev: Selecting capability groups is a bounded classification problem repeated at every turn.
Architecture: User intent + agent state → capability Choices → filtered MCP manifest → downstream model.
Current alternative: Expose all tools, manually configure servers, or ask an LLM to choose.
Jev advantage: Reduces schemas in context and can run before each agent turn.
1–7 day MVP: Proxy three MCP servers and visualize selected/hidden tools.
Validation experiment: Measure token use and tool-selection accuracy across 100 tasks.
Evidence: source 1, source 2, source 3
Unknowns: Cost of false exclusion and whether confidence is sufficient for fallback.
Confidence: HIGH — Concrete developer-tool problem with an observable, typed outcome.

11. Personal File Attention Index

Problem: Desktop search finds keyword matches but does not continuously infer what is currently important.
Product: A local index that scores files, downloads, screenshots, and documents for urgency and project relevance.
Why Jev: The index becomes useful through thousands of cheap decisions per filesystem event.
Architecture: File metadata/extract → parallel relevance Scores → local SQLite index → Spotlight-style UI.
Current alternative: Recency sorting, embeddings, or manual folders.
Jev advantage: Adds active judgment to every file event without generating summaries.
1–7 day MVP: macOS watcher for Downloads and Desktop with a ranked menu-bar view.
Validation experiment: Track opened files and explicit corrections for one week.
Evidence: source 1, source 2, source 3
Unknowns: Private-data controls and whether lightweight features provide enough context.
Confidence: MEDIUM — Visually compelling and high frequency, with unproven personalization quality.

12. Webhook Decision Bus

Problem: SaaS webhooks trigger brittle rule chains that cannot interpret nuanced event context.
Product: A developer service mapping each event to typed route, priority, and action decisions.
Why Jev: Event streams need low-cost decisions per event rather than text generation.
Architecture: Webhook → normalized state → Jev questions → signed typed result → queues/functions.
Current alternative: Zapier filters, JSONPath rules, or per-event LLM calls.
Jev advantage: Semantic branching becomes an infrastructure primitive across large event volumes.
1–7 day MVP: Open-source gateway for GitHub and Stripe-like test events with replay tooling.
Validation experiment: Build three real automations and compare rule complexity and misroutes.
Evidence: source 1, source 2, source 3
Unknowns: Tenant-specific calibration and operational guarantees.
Confidence: HIGH — A natural typed-decision API and feasible open-source wedge.

13. Privacy-Preserving Screen Event Filter

Problem: Screen-aware assistants send too much irrelevant or sensitive context upstream.
Product: A local preflight layer deciding whether a screen change is useful, sensitive, or ignorable.
Why Jev: Every screen event needs fast Boolean and category decisions before expensive processing.
Architecture: On-device OCR/accessibility snapshot → sensitivity/relevance decisions → redact/drop/forward.
Current alternative: Send all frames, sample periodically, or use fragile app allowlists.
Jev advantage: Can reduce both privacy exposure and downstream multimodal cost.
1–7 day MVP: macOS recorder in shadow mode with a timeline showing kept and rejected events.
Validation experiment: Label 500 screen transitions and measure sensitive false negatives.
Evidence: source 1, source 2, source 3
Unknowns: Whether Jev accepts sufficient multimodal-derived state and local-processing requirements.
Confidence: LOW — High upside but privacy and input-modality assumptions need testing.

14. Adaptive Observability Sampler

Problem: Teams either retain too many traces or miss the rare traces that explain incidents.
Product: A collector making keep/drop/escalate decisions from trace metadata and partial spans.
Why Jev: Sampling happens at high volume and requires a tiny typed decision per trace.
Architecture: Span features → anomaly/value Scores → deterministic quotas → storage tier.
Current alternative: Random, head-based, or static rule sampling.
Jev advantage: Semantically interesting traces can survive without LLM-scale cost per request.
1–7 day MVP: OpenTelemetry collector processor with a replay benchmark.
Validation experiment: Replay public traces with injected incidents; compare detection under equal storage budgets.
Evidence: source 1, source 2, source 3
Unknowns: Latency at collector scale and bias introduced by learned sampling.
Confidence: MEDIUM — Excellent economics fit, but demanding throughput requirements.

15. Smart Retry Policy

Problem: Distributed systems retry uniformly even when errors indicate permanent failure or overload.
Product: A library choosing retry, backoff class, fallback, or dead-letter from structured failure context.
Why Jev: Each failure needs a bounded control decision, often many times per request chain.
Architecture: Error + operation metadata → control Choice and recoverability Score → capped policy executor.
Current alternative: Status-code tables and exponential backoff everywhere.
Jev advantage: Semantic retry behavior can adapt while hard caps guarantee safety.
1–7 day MVP: TypeScript fetch wrapper with offline replay against recorded failures.
Validation experiment: Measure successful recoveries, added load, and bad retries against standard policies.
Evidence: source 1, source 2, source 3
Unknowns: Novel failure generalization and added latency on already-failing paths.
Confidence: MEDIUM — Strong primitive fit but system-level failure behavior needs rigorous testing.

16. Dependency Update Gate

Problem: Automated dependency PRs create noise and hide the few updates requiring immediate action.
Product: A bot deciding auto-merge, test-more, human-review, defer, or block for every update.
Why Jev: Update disposition is a repeated typed choice over changelog, diff, usage, and risk state.
Architecture: Update metadata + repository usage → risk Scores/Choice → CI policy → action.
Current alternative: Version-range rules and manual Dependabot triage.
Jev advantage: Semantic triage can cover every package without a generative review call.
1–7 day MVP: GitHub Action for npm repositories with shadow recommendations.
Validation experiment: Backtest six months of updates and security advisories.
Evidence: source 1, source 2, source 3
Unknowns: Changelog quality and ecosystem-specific risk features.
Confidence: MEDIUM — Useful solo-developer wedge with measurable historical outcomes.

17. Continuous Form Friction Controller

Problem: Forms ask every user the same questions even when many fields are irrelevant or risky.
Product: A form engine deciding which question to show next, skip, verify, or escalate.
Why Jev: Each field transition is a small decision based on accumulated typed state.
Architecture: Form state → next-question Choice + fraud/completeness Scores → schema-constrained UI.
Current alternative: Branching form logic or conversational forms.
Jev advantage: Supports many micro-decisions without turning the flow into a chatbot.
1–7 day MVP: React form builder for insurance-style intake with a visible decision trace.
Validation experiment: Compare completion time and incorrect skips with a fixed form.
Evidence: source 1, source 2, source 3
Unknowns: Compliance requirements and reliable handling of rare cases.
Confidence: MEDIUM — Typed output fits dynamic forms, though regulated uses need strict constraints.

18. Autonomous QA Scenario Selector

Problem: UI test suites run too many redundant paths while missing state-dependent risky scenarios.
Product: A test controller choosing the next action and deciding when a distinct failure has been found.
Why Jev: Exploration requires hundreds of small action and novelty decisions per session.
Architecture: DOM/screenshot-derived state → next-action Choice + novelty/failure Scores → Playwright.
Current alternative: Scripted tests, random fuzzing, or full multimodal agents.
Jev advantage: Decision calls can sit inside a dense exploration loop.
1–7 day MVP: Playwright plugin for one demo app with a live exploration graph.
Validation experiment: Seed known bugs and compare discovery time against random and scripted baselines.
Evidence: source 1, source 2, source 3
Unknowns: Rich UI state representation and avoiding exploration loops.
Confidence: MEDIUM — High-frequency decisions are central, but state encoding is challenging.

19. Meeting Participation Router

Problem: Teams invite people broadly because deciding who truly needs each agenda item is tedious.
Product: A planner assigning attend, async input, optional, or no-action per person and agenda item.
Why Jev: The product makes a matrix of small choices—many decisions per meeting.
Architecture: Agenda + roles + projects → per-person Choices → organizer review → calendar updates.
Current alternative: Organizer intuition and blanket invitations.
Jev advantage: Parallel typed questions can evaluate the entire participation matrix.
1–7 day MVP: Calendar add-on generating a reviewable attendance matrix.
Validation experiment: Shadow 30 meetings and survey organizers and suggested non-attendees.
Evidence: source 1, source 2, source 3
Unknowns: Organizational politics and incomplete role context.
Confidence: LOW — Technically suitable but behavior change and social acceptance are uncertain.

20. Home Automation Intent Layer

Problem: Smart-home rules become unmanageable when context spans occupants, weather, devices, and routines.
Product: A Home Assistant layer turning state changes into typed comfort, safety, and automation decisions.
Why Jev: Home state changes continuously and needs bounded decisions with local budget controls.
Architecture: Home Assistant state → parallel decisions → safety rules → service calls.
Current alternative: Large YAML rule trees or cloud LLM automations.
Jev advantage: Frequent evaluation can simplify rules while deterministic safety constraints remain authoritative.
1–7 day MVP: Extend the existing HA-Jev integration with shadow-mode recommendations and evaluation logs.
Validation experiment: Run for one week without actions, measuring corrections and token spend.
Evidence: source 1, source 2, source 3
Unknowns: Cloud dependence, privacy, and reliability during outages.
Confidence: HIGH — An independent open-source integration already demonstrates feasibility.

21. Probabilistic Rules Engine

Problem: Business rule systems are deterministic even when concepts such as urgency, fit, or risk are fuzzy.
Product: An open-source engine combining Jev judgments with auditable deterministic conditions.
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.
1–7 day MVP: TypeScript DSL, local replay UI, and three example policies.
Validation experiment: Ask developers to replace one brittle rule tree and measure complexity and accuracy.
Evidence: source 1, source 2, source 3
Unknowns: Threshold governance, drift monitoring, and debugging expectations.
Confidence: HIGH — This directly exposes Jev's typed probabilities as a software primitive.

22. LLM Output Escalation Mesh

Problem: Systems apply one verifier to all generated outputs or trust them uniformly.
Product: A mesh of cheap per-claim decisions selecting accept, recheck, regenerate, retrieve, or ask a human.
Why Jev: A generated response can require dozens of independent confidence and policy decisions.
Architecture: Parsed output units → parallel verifier/router questions → selective expensive checks → response.
Current alternative: One LLM-as-judge pass or universal retrieval.
Jev advantage: Spends expensive verification only where cheap decisions indicate risk.
1–7 day MVP: Middleware for structured extraction outputs with a claim-level audit view.
Validation experiment: Use a labeled extraction dataset and compare total cost at equal error rate.
Evidence: source 1, source 2, source 3
Unknowns: Verifier correlation with the generator and claim segmentation quality.
Confidence: MEDIUM — Promising cascade architecture; independent verifier performance is unknown.

23. Context Window Admission Controller

Problem: Agents overfill context with available material instead of admitting only task-relevant evidence.
Product: A component scoring every candidate memory, file, or message before context assembly.
Why Jev: Hundreds of inclusion decisions may be needed for a single model request.
Architecture: Task + candidate metadata/snippets → relevance Scores → diversity and token-budget algorithm → context.
Current alternative: Top-k embeddings and recency heuristics.
Jev advantage: Allows semantic admission at a granularity that full LLM ranking makes expensive.
1–7 day MVP: Plugin for a coding agent that ranks repository files and explains admitted context.
Validation experiment: Compare task success and input tokens against embedding-only retrieval.
Evidence: source 1, source 2, source 3
Unknowns: Whether independent scoring preserves cross-item diversity.
Confidence: HIGH — Clear economic leverage from many decisions per expensive request.

24. Local Activity Auto-Timeline

Problem: People cannot reconstruct what they worked on across apps without manual time tracking.
Product: A private desktop timeline deciding whether each app/window event starts, continues, or ends an activity.
Why Jev: Continuous desktop events demand cheap classification rather than narrative generation.
Architecture: Accessibility events → activity Choice and boundary Boolean → local timeline → editable UI.
Current alternative: App-name timers and periodic screenshots.
Jev advantage: Semantic events can produce a useful timeline without recording every frame.
1–7 day MVP: macOS menu-bar app for browser, editor, and terminal activity.
Validation experiment: Compare inferred blocks with a user's end-of-day corrections.
Evidence: source 1, source 2, source 3
Unknowns: Privacy, app permissions, and ambiguous multitasking.
Confidence: MEDIUM — Strong indie fit and event-level economics; personalization remains untested.

25. API Abuse Shape Detector

Problem: Rate limits catch volume but miss low-and-slow misuse that is semantically suspicious.
Product: An edge middleware assigning abuse type, severity, and response tier to suspicious request sequences.
Why Jev: Risk decisions must be cheap enough for frequent invocation and return constrained actions.
Architecture: Aggregated request features → abuse Choice/Score → hard security rules → allow/challenge/block.
Current alternative: WAF signatures and expensive anomaly pipelines.
Jev advantage: Could add semantic judgment between static rules and heavyweight investigation.
1–7 day MVP: Replay proxy over synthetic API traffic; never block live requests initially.
Validation experiment: Measure false positives across scripted attacks and normal burst patterns.
Evidence: source 1, source 2, source 3
Unknowns: Adversarial robustness and safe feature representation.
Confidence: LOW — Security value is high but unverified calibration makes autonomous blocking premature.

26. Data Pipeline Quarantine Router

Problem: Malformed or semantically odd records either fail whole jobs or silently contaminate downstream data.
Product: A stream processor choosing accept, normalize, quarantine, retry, or human review per record.
Why Jev: Large pipelines need a constrained decision on many ambiguous records.
Architecture: Schema result + record sample + lineage → action Choice/confidence → deterministic transform queues.
Current alternative: Validation rules and catch-all dead-letter queues.
Jev advantage: Adds semantic triage without generating transformed data.
1–7 day MVP: CLI for CSV/JSON imports with a reviewable quarantine folder.
Validation experiment: Inject labeled anomalies into three public datasets and compare rule-only routing.
Evidence: source 1, source 2, source 3
Unknowns: Throughput and handling sensitive data in model inputs.
Confidence: MEDIUM — Bounded action space and measurable quality, with scale still uncertain.

27. Adaptive Game NPC Director

Problem: Game encounters rely on authored condition trees or expensive generative agents.
Product: A runtime director selecting tactics, attention, difficulty response, and group coordination every tick interval.
Why Jev: Games need many typed decisions, not dialogue, under tight latency and cost constraints.
Architecture: World state → parallel NPC Choices/Scores → deterministic behavior trees → animation/action.
Current alternative: Behavior trees, utility AI, or LLM-driven NPCs.
Jev advantage: Probabilistic judgment could complement authored action execution at much higher frequency.
1–7 day MVP: Top-down arena demo with Jev director versus a fixed utility-AI baseline.
Validation experiment: Measure action diversity, player-rated coherence, latency, and cost over 1,000 encounters.
Evidence: source 1, source 2, source 3
Unknowns: Network dependence and determinism requirements for games.
Confidence: MEDIUM — Doom and MAGI demonstrations suggest interest, not production readiness.

28. Multi-Agent Vote Calibrator

Problem: Agent ensembles use crude majority voting and ignore uncertainty or correlated errors.
Product: A coordinator deciding when votes agree enough, need another specialist, or require human escalation.
Why Jev: Coordination consists of repeated confidence and next-participant decisions.
Architecture: Agent outputs/votes → consensus Scores and next-step Choice → bounded orchestration loop.
Current alternative: Simple majority, fixed debate rounds, or a final LLM judge.
Jev advantage: Can allocate extra agents only to ambiguous cases.
1–7 day MVP: Reproduce the open MAGI concept and add confidence-aware stopping.
Validation experiment: Compare accuracy and calls on a labeled decision dataset.
Evidence: source 1, source 2, source 3
Unknowns: Whether Jev reduces rather than inherits correlated ensemble errors.
Confidence: MEDIUM — There is a concrete open-source inspiration, but ensemble gains need measurement.

29. Micro-Approval SDK

Problem: Product teams repeatedly rebuild confidence thresholds, fallback queues, and audit logs around AI decisions.
Product: An SDK and embeddable UI for approve/deny/escalate flows backed by typed probabilistic decisions.
Why Jev: The API can expose Jev probabilities directly while code owns threshold and action semantics.
Architecture: Domain adapter → Jev questions → versioned threshold policy → approval widget/audit store.
Current alternative: Custom prompt-and-JSON glue in every product.
Jev advantage: Turns safe partial automation into a reusable software pattern.
1–7 day MVP: TypeScript package, SQLite audit store, and React review queue.
Validation experiment: Integrate into two different demos and measure integration effort and override rates.
Evidence: source 1, source 2, source 3
Unknowns: How generic the state adapters and calibration UI can be.
Confidence: HIGH — A small, open-source developer tool that makes the core architecture tangible.

30. Semantic Background-Task Scheduler

Problem: Apps schedule maintenance by clock time instead of current user intent, device state, and task urgency.
Product: A scheduler choosing run-now, defer, batch, cancel, or request power/network for each task.
Why Jev: Scheduling creates continuous, bounded decisions across many tasks and state changes.
Architecture: Task/device/user state → action Choice + urgency Score → OS constraints → executor.
Current alternative: Fixed intervals and hand-authored priority rules.
Jev advantage: Semantic batching could improve responsiveness and resource use without generative output.
1–7 day MVP: macOS daemon simulating decisions for backup, indexing, and sync jobs.
Validation experiment: Replay a week of device-state traces and compare delays and contention.
Evidence: source 1, source 2, source 3
Unknowns: OS scheduling permissions and benefits over well-designed heuristics.
Confidence: LOW — Architecturally distinctive, but conventional algorithms may already suffice.

31. Decision Regression Harness

Problem: Teams cannot safely change prompts, thresholds, or Jev model versions without seeing behavior drift.
Product: A test runner recording typed decisions and calibration metrics over versioned scenario suites.
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.
1–7 day MVP: CLI with YAML fixtures, golden distributions, tolerance rules, and GitHub annotations.
Validation experiment: Use it on three sample decision apps and intentionally introduce prompt regressions.
Evidence: source 1, source 2, source 3
Unknowns: API support for deterministic comparison and expected probability variance.
Confidence: HIGH — Typed decisions create a clear developer-tool opportunity independent of broad model adoption.

Build Ideas

These are hypotheses grounded in the documented typed-decision interface, collected X discussion, and located projects. Confidence reflects evidence and MVP tractability—not guaranteed demand.

1. Agent Tool Firewall

Problem: Autonomous agents can select destructive or irrelevant tools from large catalogs.
Product: A local gateway that scores every proposed tool call and requires confirmation only when risk is high.
Why Jev: Tool execution needs a typed allow/deny/escalate decision with confidence, not generated prose.
Architecture: Agent proposal → Jev risk and intent questions → deterministic policy thresholds → tool or approval UI.
Current alternative: Prompt-based guardrails, static allowlists, or an LLM judge on every call.
Jev advantage: A decision can run inside every agent step while preserving explicit policy code.
1–7 day MVP: Build an MCP proxy for five filesystem and shell tools with a macOS confirmation panel.
Validation experiment: Replay 200 benign and adversarial tool calls; measure unsafe allows, needless prompts, latency, and cost.
Evidence: source 1, source 2, source 3
Unknowns: Calibration under prompt injection and whether confidence remains stable across tool schemas.
Confidence: HIGH — The typed-decision interface directly matches tool gating, though safety still needs deterministic backstops.

2. Agent Model Router

Problem: Agents routinely send easy steps to expensive reasoning models and hard steps to models that fail.
Product: A drop-in router that selects model, reasoning effort, and fallback policy per step.
Why Jev: Routing is a repeated bounded choice plus confidence score.
Architecture: Request state → Choice(model) + Score(complexity) → route → outcome logging → offline evaluation.
Current alternative: Regex rules, hand-tuned heuristics, or another full LLM call.
Jev advantage: Routing overhead may remain small enough to apply before every inference.
1–7 day MVP: Proxy two inexpensive and two premium models for a coding-agent benchmark.
Validation experiment: Compare task success, total spend, and latency against cheapest-only and premium-only baselines.
Evidence: source 1, source 2, source 3
Unknowns: Whether the router generalizes to unseen tasks and remains cheaper after retries.
Confidence: HIGH — Routing is repeatedly cited in the source material and maps naturally to Choice.

3. CI Review Triage

Problem: Repositories waste reviewer attention on harmless diffs while risky changes can look deceptively small.
Product: A GitHub check that assigns review depth, owners, and required test classes to every pull request.
Why Jev: Each diff needs several parallel labels and risk scores rather than a review essay.
Architecture: Diff metadata → parallel risk/security/test questions → branch protection policy → check run.
Current alternative: CODEOWNERS, path rules, or expensive LLM review of every diff.
Jev advantage: Semantic triage can happen on every push before deeper reviewers are invoked.
1–7 day MVP: GitHub App for TypeScript projects that emits low/medium/high risk and recommended suites.
Validation experiment: Backtest on 100 merged PRs and compare labels with bugs, reverts, and reviewer choices.
Evidence: source 1, source 2, source 3
Unknowns: How much code context fits without losing calibration.
Confidence: MEDIUM — Strong primitive fit; independent accuracy evidence is not yet available.

4. Notification Interruptibility Engine

Problem: Notification systems use crude per-app settings and interrupt at the wrong moment.
Product: A macOS/iOS layer that decides deliver, batch, summarize-later, or suppress for every event.
Why Jev: Continuous, private context requires cheap decisions per notification.
Architecture: Notification + focus/calendar/device state → delivery Choice and urgency Score → OS action.
Current alternative: Focus modes, keyword filters, or cloud LLM summaries.
Jev advantage: Thousands of tiny judgments can shape attention without generating content.
1–7 day MVP: A macOS menu-bar app using calendar state and a local notification inbox simulation.
Validation experiment: Run a one-week shadow mode and ask users to approve or correct every proposed action.
Evidence: source 1, source 2, source 3
Unknowns: Platform interception limits and privacy expectations for notification content.
Confidence: MEDIUM — Compelling high-frequency use case, but OS APIs may constrain the product.

5. Semantic Feature-Flag Allocator

Problem: Feature flags target static cohorts but cannot react intelligently to the current session.
Product: A policy layer selecting safe UI variants based on session intent, friction, and risk.
Why Jev: Allocation needs a typed variant choice at request time with uncertainty-aware fallback.
Architecture: Approved session features → Jev variant Choice → deterministic eligibility checks → flag result.
Current alternative: Rule trees and broad A/B cohorts.
Jev advantage: Enables per-event adaptation while code retains the final eligibility boundary.
1–7 day MVP: SDK and dashboard for three variants in a sample SaaS onboarding flow.
Validation experiment: Shadow against existing rules and inspect disagreement quality before any live allocation.
Evidence: source 1, source 2, source 3
Unknowns: Fairness, explainability, and feedback loops from adaptive assignment.
Confidence: LOW — Architecture is novel but carries experimentation and fairness risks.

6. Support Action Gate

Problem: Support automation either stops at drafting text or grants an agent dangerously broad authority.
Product: A decision layer approving refunds, credits, escalations, and account changes within explicit limits.
Why Jev: The useful output is an action class and confidence, with deterministic dollar and permission caps.
Architecture: Ticket state → action Choice + fraud/urgency Scores → policy engine → execute or human queue.
Current alternative: Manual macros or an LLM agent with extensive prompt constraints.
Jev advantage: Separates judgment from execution and allows frequent re-evaluation as state changes.
1–7 day MVP: Sandbox against a CSV of historical tickets with simulated refund actions.
Validation experiment: Compare proposed actions with historical resolutions and specialist review.
Evidence: source 1, source 2, source 3
Unknowns: Domain-specific calibration and access to representative support data.
Confidence: MEDIUM — Explicitly aligned with workflow decisions, but production action requires conservative policy.

7. Inbox Micro-Router

Problem: Email rules fail on semantics, while generative inbox agents are costly and overpowered.
Product: An email client companion assigning folder, urgency, next-action, and safe automation eligibility.
Why Jev: Each message creates multiple small parallel decisions with no need for generated text.
Architecture: Message + relationship state → parallel Choice/Score questions → local rules → label or queue.
Current alternative: Filters, priority inbox classifiers, and LLM assistants.
Jev advantage: Can classify every inbound message and reclassify threads after replies.
1–7 day MVP: Local IMAP shadow classifier with an approval queue and accuracy dashboard.
Validation experiment: Measure corrections and time-to-important-message over two weeks.
Evidence: source 1, source 2, source 3
Unknowns: Provider access, sensitive content handling, and multilingual quality.
Confidence: MEDIUM — Well-bounded decisions and a feasible shadow-mode MVP.

8. Calendar Commitment Guard

Problem: Calendars accept meetings without judging context switching, preparation burden, or strategic value.
Product: A scheduling guard that classifies each invitation and proposes accept, decline, delegate, or request agenda.
Why Jev: The core operation is a repeated typed decision over calendar and relationship state.
Architecture: Invite + calendar + user policy → decision probabilities → deterministic availability checks → suggestion.
Current alternative: Static scheduling rules or manual review.
Jev advantage: Semantic policy can run for every invitation without composing messages unless requested.
1–7 day MVP: Google Calendar read-only companion with a daily review feed.
Validation experiment: Compare suggestions with the user's actual decisions for 50 invitations.
Evidence: source 1, source 2, source 3
Unknowns: Sparse personal training signals and sensitive relationship context.
Confidence: MEDIUM — Clear indie MVP, with value depending on personalized accuracy.

9. Agent Loop Stopper

Problem: Agents continue after diminishing returns, repeat failed actions, or stop before verifying outcomes.
Product: A loop controller deciding continue, retry differently, verify, ask, or stop after every step.
Why Jev: Agent loops require many low-latency state judgments where prose is overhead.
Architecture: Trace state → progress/failure Scores + next-control Choice → hard iteration budget → agent runtime.
Current alternative: Fixed step limits and LLM self-reflection prompts.
Jev advantage: Makes control a first-class typed layer and may reduce wasted premium-model calls.
1–7 day MVP: Middleware for one open-source agent with trace replay and stop-decision visualization.
Validation experiment: Run SWE-style tasks; compare completion, wasted calls, and premature stops.
Evidence: source 1, source 2, source 3
Unknowns: Reliable progress detection from partial traces.
Confidence: HIGH — Directly uses cheap decisions inside agent loops, a central Jev-native design space.

10. MCP Capability Router

Problem: Large MCP tool sets overload model context and increase incorrect tool selection.
Product: A proxy exposing only the small capability subset relevant to the current step.
Why Jev: Selecting capability groups is a bounded classification problem repeated at every turn.
Architecture: User intent + agent state → capability Choices → filtered MCP manifest → downstream model.
Current alternative: Expose all tools, manually configure servers, or ask an LLM to choose.
Jev advantage: Reduces schemas in context and can run before each agent turn.
1–7 day MVP: Proxy three MCP servers and visualize selected/hidden tools.
Validation experiment: Measure token use and tool-selection accuracy across 100 tasks.
Evidence: source 1, source 2, source 3
Unknowns: Cost of false exclusion and whether confidence is sufficient for fallback.
Confidence: HIGH — Concrete developer-tool problem with an observable, typed outcome.

11. Personal File Attention Index

Problem: Desktop search finds keyword matches but does not continuously infer what is currently important.
Product: A local index that scores files, downloads, screenshots, and documents for urgency and project relevance.
Why Jev: The index becomes useful through thousands of cheap decisions per filesystem event.
Architecture: File metadata/extract → parallel relevance Scores → local SQLite index → Spotlight-style UI.
Current alternative: Recency sorting, embeddings, or manual folders.
Jev advantage: Adds active judgment to every file event without generating summaries.
1–7 day MVP: macOS watcher for Downloads and Desktop with a ranked menu-bar view.
Validation experiment: Track opened files and explicit corrections for one week.
Evidence: source 1, source 2, source 3
Unknowns: Private-data controls and whether lightweight features provide enough context.
Confidence: MEDIUM — Visually compelling and high frequency, with unproven personalization quality.

12. Webhook Decision Bus

Problem: SaaS webhooks trigger brittle rule chains that cannot interpret nuanced event context.
Product: A developer service mapping each event to typed route, priority, and action decisions.
Why Jev: Event streams need low-cost decisions per event rather than text generation.
Architecture: Webhook → normalized state → Jev questions → signed typed result → queues/functions.
Current alternative: Zapier filters, JSONPath rules, or per-event LLM calls.
Jev advantage: Semantic branching becomes an infrastructure primitive across large event volumes.
1–7 day MVP: Open-source gateway for GitHub and Stripe-like test events with replay tooling.
Validation experiment: Build three real automations and compare rule complexity and misroutes.
Evidence: source 1, source 2, source 3
Unknowns: Tenant-specific calibration and operational guarantees.
Confidence: HIGH — A natural typed-decision API and feasible open-source wedge.

13. Privacy-Preserving Screen Event Filter

Problem: Screen-aware assistants send too much irrelevant or sensitive context upstream.
Product: A local preflight layer deciding whether a screen change is useful, sensitive, or ignorable.
Why Jev: Every screen event needs fast Boolean and category decisions before expensive processing.
Architecture: On-device OCR/accessibility snapshot → sensitivity/relevance decisions → redact/drop/forward.
Current alternative: Send all frames, sample periodically, or use fragile app allowlists.
Jev advantage: Can reduce both privacy exposure and downstream multimodal cost.
1–7 day MVP: macOS recorder in shadow mode with a timeline showing kept and rejected events.
Validation experiment: Label 500 screen transitions and measure sensitive false negatives.
Evidence: source 1, source 2, source 3
Unknowns: Whether Jev accepts sufficient multimodal-derived state and local-processing requirements.
Confidence: LOW — High upside but privacy and input-modality assumptions need testing.

14. Adaptive Observability Sampler

Problem: Teams either retain too many traces or miss the rare traces that explain incidents.
Product: A collector making keep/drop/escalate decisions from trace metadata and partial spans.
Why Jev: Sampling happens at high volume and requires a tiny typed decision per trace.
Architecture: Span features → anomaly/value Scores → deterministic quotas → storage tier.
Current alternative: Random, head-based, or static rule sampling.
Jev advantage: Semantically interesting traces can survive without LLM-scale cost per request.
1–7 day MVP: OpenTelemetry collector processor with a replay benchmark.
Validation experiment: Replay public traces with injected incidents; compare detection under equal storage budgets.
Evidence: source 1, source 2, source 3
Unknowns: Latency at collector scale and bias introduced by learned sampling.
Confidence: MEDIUM — Excellent economics fit, but demanding throughput requirements.

15. Smart Retry Policy

Problem: Distributed systems retry uniformly even when errors indicate permanent failure or overload.
Product: A library choosing retry, backoff class, fallback, or dead-letter from structured failure context.
Why Jev: Each failure needs a bounded control decision, often many times per request chain.
Architecture: Error + operation metadata → control Choice and recoverability Score → capped policy executor.
Current alternative: Status-code tables and exponential backoff everywhere.
Jev advantage: Semantic retry behavior can adapt while hard caps guarantee safety.
1–7 day MVP: TypeScript fetch wrapper with offline replay against recorded failures.
Validation experiment: Measure successful recoveries, added load, and bad retries against standard policies.
Evidence: source 1, source 2, source 3
Unknowns: Novel failure generalization and added latency on already-failing paths.
Confidence: MEDIUM — Strong primitive fit but system-level failure behavior needs rigorous testing.

16. Dependency Update Gate

Problem: Automated dependency PRs create noise and hide the few updates requiring immediate action.
Product: A bot deciding auto-merge, test-more, human-review, defer, or block for every update.
Why Jev: Update disposition is a repeated typed choice over changelog, diff, usage, and risk state.
Architecture: Update metadata + repository usage → risk Scores/Choice → CI policy → action.
Current alternative: Version-range rules and manual Dependabot triage.
Jev advantage: Semantic triage can cover every package without a generative review call.
1–7 day MVP: GitHub Action for npm repositories with shadow recommendations.
Validation experiment: Backtest six months of updates and security advisories.
Evidence: source 1, source 2, source 3
Unknowns: Changelog quality and ecosystem-specific risk features.
Confidence: MEDIUM — Useful solo-developer wedge with measurable historical outcomes.

17. Continuous Form Friction Controller

Problem: Forms ask every user the same questions even when many fields are irrelevant or risky.
Product: A form engine deciding which question to show next, skip, verify, or escalate.
Why Jev: Each field transition is a small decision based on accumulated typed state.
Architecture: Form state → next-question Choice + fraud/completeness Scores → schema-constrained UI.
Current alternative: Branching form logic or conversational forms.
Jev advantage: Supports many micro-decisions without turning the flow into a chatbot.
1–7 day MVP: React form builder for insurance-style intake with a visible decision trace.
Validation experiment: Compare completion time and incorrect skips with a fixed form.
Evidence: source 1, source 2, source 3
Unknowns: Compliance requirements and reliable handling of rare cases.
Confidence: MEDIUM — Typed output fits dynamic forms, though regulated uses need strict constraints.

18. Autonomous QA Scenario Selector

Problem: UI test suites run too many redundant paths while missing state-dependent risky scenarios.
Product: A test controller choosing the next action and deciding when a distinct failure has been found.
Why Jev: Exploration requires hundreds of small action and novelty decisions per session.
Architecture: DOM/screenshot-derived state → next-action Choice + novelty/failure Scores → Playwright.
Current alternative: Scripted tests, random fuzzing, or full multimodal agents.
Jev advantage: Decision calls can sit inside a dense exploration loop.
1–7 day MVP: Playwright plugin for one demo app with a live exploration graph.
Validation experiment: Seed known bugs and compare discovery time against random and scripted baselines.
Evidence: source 1, source 2, source 3
Unknowns: Rich UI state representation and avoiding exploration loops.
Confidence: MEDIUM — High-frequency decisions are central, but state encoding is challenging.

19. Meeting Participation Router

Problem: Teams invite people broadly because deciding who truly needs each agenda item is tedious.
Product: A planner assigning attend, async input, optional, or no-action per person and agenda item.
Why Jev: The product makes a matrix of small choices—many decisions per meeting.
Architecture: Agenda + roles + projects → per-person Choices → organizer review → calendar updates.
Current alternative: Organizer intuition and blanket invitations.
Jev advantage: Parallel typed questions can evaluate the entire participation matrix.
1–7 day MVP: Calendar add-on generating a reviewable attendance matrix.
Validation experiment: Shadow 30 meetings and survey organizers and suggested non-attendees.
Evidence: source 1, source 2, source 3
Unknowns: Organizational politics and incomplete role context.
Confidence: LOW — Technically suitable but behavior change and social acceptance are uncertain.

20. Home Automation Intent Layer

Problem: Smart-home rules become unmanageable when context spans occupants, weather, devices, and routines.
Product: A Home Assistant layer turning state changes into typed comfort, safety, and automation decisions.
Why Jev: Home state changes continuously and needs bounded decisions with local budget controls.
Architecture: Home Assistant state → parallel decisions → safety rules → service calls.
Current alternative: Large YAML rule trees or cloud LLM automations.
Jev advantage: Frequent evaluation can simplify rules while deterministic safety constraints remain authoritative.
1–7 day MVP: Extend the existing HA-Jev integration with shadow-mode recommendations and evaluation logs.
Validation experiment: Run for one week without actions, measuring corrections and token spend.
Evidence: source 1, source 2, source 3
Unknowns: Cloud dependence, privacy, and reliability during outages.
Confidence: HIGH — An independent open-source integration already demonstrates feasibility.

21. Probabilistic Rules Engine

Problem: Business rule systems are deterministic even when concepts such as urgency, fit, or risk are fuzzy.
Product: An open-source engine combining Jev judgments with auditable deterministic conditions.
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.
1–7 day MVP: TypeScript DSL, local replay UI, and three example policies.
Validation experiment: Ask developers to replace one brittle rule tree and measure complexity and accuracy.
Evidence: source 1, source 2, source 3
Unknowns: Threshold governance, drift monitoring, and debugging expectations.
Confidence: HIGH — This directly exposes Jev's typed probabilities as a software primitive.

22. LLM Output Escalation Mesh

Problem: Systems apply one verifier to all generated outputs or trust them uniformly.
Product: A mesh of cheap per-claim decisions selecting accept, recheck, regenerate, retrieve, or ask a human.
Why Jev: A generated response can require dozens of independent confidence and policy decisions.
Architecture: Parsed output units → parallel verifier/router questions → selective expensive checks → response.
Current alternative: One LLM-as-judge pass or universal retrieval.
Jev advantage: Spends expensive verification only where cheap decisions indicate risk.
1–7 day MVP: Middleware for structured extraction outputs with a claim-level audit view.
Validation experiment: Use a labeled extraction dataset and compare total cost at equal error rate.
Evidence: source 1, source 2, source 3
Unknowns: Verifier correlation with the generator and claim segmentation quality.
Confidence: MEDIUM — Promising cascade architecture; independent verifier performance is unknown.

23. Context Window Admission Controller

Problem: Agents overfill context with available material instead of admitting only task-relevant evidence.
Product: A component scoring every candidate memory, file, or message before context assembly.
Why Jev: Hundreds of inclusion decisions may be needed for a single model request.
Architecture: Task + candidate metadata/snippets → relevance Scores → diversity and token-budget algorithm → context.
Current alternative: Top-k embeddings and recency heuristics.
Jev advantage: Allows semantic admission at a granularity that full LLM ranking makes expensive.
1–7 day MVP: Plugin for a coding agent that ranks repository files and explains admitted context.
Validation experiment: Compare task success and input tokens against embedding-only retrieval.
Evidence: source 1, source 2, source 3
Unknowns: Whether independent scoring preserves cross-item diversity.
Confidence: HIGH — Clear economic leverage from many decisions per expensive request.

24. Local Activity Auto-Timeline

Problem: People cannot reconstruct what they worked on across apps without manual time tracking.
Product: A private desktop timeline deciding whether each app/window event starts, continues, or ends an activity.
Why Jev: Continuous desktop events demand cheap classification rather than narrative generation.
Architecture: Accessibility events → activity Choice and boundary Boolean → local timeline → editable UI.
Current alternative: App-name timers and periodic screenshots.
Jev advantage: Semantic events can produce a useful timeline without recording every frame.
1–7 day MVP: macOS menu-bar app for browser, editor, and terminal activity.
Validation experiment: Compare inferred blocks with a user's end-of-day corrections.
Evidence: source 1, source 2, source 3
Unknowns: Privacy, app permissions, and ambiguous multitasking.
Confidence: MEDIUM — Strong indie fit and event-level economics; personalization remains untested.

25. API Abuse Shape Detector

Problem: Rate limits catch volume but miss low-and-slow misuse that is semantically suspicious.
Product: An edge middleware assigning abuse type, severity, and response tier to suspicious request sequences.
Why Jev: Risk decisions must be cheap enough for frequent invocation and return constrained actions.
Architecture: Aggregated request features → abuse Choice/Score → hard security rules → allow/challenge/block.
Current alternative: WAF signatures and expensive anomaly pipelines.
Jev advantage: Could add semantic judgment between static rules and heavyweight investigation.
1–7 day MVP: Replay proxy over synthetic API traffic; never block live requests initially.
Validation experiment: Measure false positives across scripted attacks and normal burst patterns.
Evidence: source 1, source 2, source 3
Unknowns: Adversarial robustness and safe feature representation.
Confidence: LOW — Security value is high but unverified calibration makes autonomous blocking premature.

26. Data Pipeline Quarantine Router

Problem: Malformed or semantically odd records either fail whole jobs or silently contaminate downstream data.
Product: A stream processor choosing accept, normalize, quarantine, retry, or human review per record.
Why Jev: Large pipelines need a constrained decision on many ambiguous records.
Architecture: Schema result + record sample + lineage → action Choice/confidence → deterministic transform queues.
Current alternative: Validation rules and catch-all dead-letter queues.
Jev advantage: Adds semantic triage without generating transformed data.
1–7 day MVP: CLI for CSV/JSON imports with a reviewable quarantine folder.
Validation experiment: Inject labeled anomalies into three public datasets and compare rule-only routing.
Evidence: source 1, source 2, source 3
Unknowns: Throughput and handling sensitive data in model inputs.
Confidence: MEDIUM — Bounded action space and measurable quality, with scale still uncertain.

27. Adaptive Game NPC Director

Problem: Game encounters rely on authored condition trees or expensive generative agents.
Product: A runtime director selecting tactics, attention, difficulty response, and group coordination every tick interval.
Why Jev: Games need many typed decisions, not dialogue, under tight latency and cost constraints.
Architecture: World state → parallel NPC Choices/Scores → deterministic behavior trees → animation/action.
Current alternative: Behavior trees, utility AI, or LLM-driven NPCs.
Jev advantage: Probabilistic judgment could complement authored action execution at much higher frequency.
1–7 day MVP: Top-down arena demo with Jev director versus a fixed utility-AI baseline.
Validation experiment: Measure action diversity, player-rated coherence, latency, and cost over 1,000 encounters.
Evidence: source 1, source 2, source 3
Unknowns: Network dependence and determinism requirements for games.
Confidence: MEDIUM — Doom and MAGI demonstrations suggest interest, not production readiness.

28. Multi-Agent Vote Calibrator

Problem: Agent ensembles use crude majority voting and ignore uncertainty or correlated errors.
Product: A coordinator deciding when votes agree enough, need another specialist, or require human escalation.
Why Jev: Coordination consists of repeated confidence and next-participant decisions.
Architecture: Agent outputs/votes → consensus Scores and next-step Choice → bounded orchestration loop.
Current alternative: Simple majority, fixed debate rounds, or a final LLM judge.
Jev advantage: Can allocate extra agents only to ambiguous cases.
1–7 day MVP: Reproduce the open MAGI concept and add confidence-aware stopping.
Validation experiment: Compare accuracy and calls on a labeled decision dataset.
Evidence: source 1, source 2, source 3
Unknowns: Whether Jev reduces rather than inherits correlated ensemble errors.
Confidence: MEDIUM — There is a concrete open-source inspiration, but ensemble gains need measurement.

29. Micro-Approval SDK

Problem: Product teams repeatedly rebuild confidence thresholds, fallback queues, and audit logs around AI decisions.
Product: An SDK and embeddable UI for approve/deny/escalate flows backed by typed probabilistic decisions.
Why Jev: The API can expose Jev probabilities directly while code owns threshold and action semantics.
Architecture: Domain adapter → Jev questions → versioned threshold policy → approval widget/audit store.
Current alternative: Custom prompt-and-JSON glue in every product.
Jev advantage: Turns safe partial automation into a reusable software pattern.
1–7 day MVP: TypeScript package, SQLite audit store, and React review queue.
Validation experiment: Integrate into two different demos and measure integration effort and override rates.
Evidence: source 1, source 2, source 3
Unknowns: How generic the state adapters and calibration UI can be.
Confidence: HIGH — A small, open-source developer tool that makes the core architecture tangible.

30. Semantic Background-Task Scheduler

Problem: Apps schedule maintenance by clock time instead of current user intent, device state, and task urgency.
Product: A scheduler choosing run-now, defer, batch, cancel, or request power/network for each task.
Why Jev: Scheduling creates continuous, bounded decisions across many tasks and state changes.
Architecture: Task/device/user state → action Choice + urgency Score → OS constraints → executor.
Current alternative: Fixed intervals and hand-authored priority rules.
Jev advantage: Semantic batching could improve responsiveness and resource use without generative output.
1–7 day MVP: macOS daemon simulating decisions for backup, indexing, and sync jobs.
Validation experiment: Replay a week of device-state traces and compare delays and contention.
Evidence: source 1, source 2, source 3
Unknowns: OS scheduling permissions and benefits over well-designed heuristics.
Confidence: LOW — Architecturally distinctive, but conventional algorithms may already suffice.

31. Decision Regression Harness

Problem: Teams cannot safely change prompts, thresholds, or Jev model versions without seeing behavior drift.
Product: A test runner recording typed decisions and calibration metrics over versioned scenario suites.
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.
1–7 day MVP: CLI with YAML fixtures, golden distributions, tolerance rules, and GitHub annotations.
Validation experiment: Use it on three sample decision apps and intentionally introduce prompt regressions.
Evidence: source 1, source 2, source 3
Unknowns: API support for deterministic comparison and expected probability variance.
Confidence: HIGH — Typed decisions create a clear developer-tool opportunity independent of broad model adoption.