#Jig use cases

These examples show where Jig could help: running unfamiliar code more safely, giving AI agents narrower jobs, coordinating specialist workflows, and keeping people in control.

These are ideas we plan to test, not a promise that all of them work in Jig today. The getting-started guide shows what people can use now.

#Find an example

The examples grow with Jig, but no application has to use every capability.

The software factory is the north star: a complete application that may eventually combine several of these abilities.

#How to read the catalogue

Each entry starts with a plain-language description, followed by the smallest version worth trying, what Jig would add, and what would count as convincing evidence. Before an idea becomes a tutorial, it must beat the best simpler alternative; unsuccessful experiments will remain documented too.

Named methods such as Gauntlet and Independent Jury are explained in orchestration patterns.

#North star

#Software factory

A software factory turns an authorized issue into a tested patch through bounded planning, coding, checking, and human approval.

Research idea · Starts with one Agent · Complete application

• What the user gets: A maintainer turns an authorized issue into a tested, reviewable patch bundle while seeing its state and retaining merge and release authority.
• Why Jig: Independently admitted procedures, bounded workspaces, exact gates, and separated implementation and review authority remain inspectable across a long-running lifecycle.
• Simplest version: Start with one coding Agent and exact tests. Add planning, review, or security roles only when they receive different skills, workspaces, evidence, or approval authority. Semantic choice is optional; explicit routes should work first.
• What it needs: The earliest bounded slice is one root coding Agent plus exact in-package gates. The complete case is blocked on workspace authority, Agent-bearing component composition, durable issue facts, and explicit Git/CI adapters. Kanban, branch policy, repository credentials, and the interface are application responsibilities.
• Use something else when: A strong coding Agent plus CI wins for a small trusted team unless independent procedures and least-authority roles reduce escaped defects, unauthorized changes, or operator work.
• What would prove it: Compare equal-model, equal-tool, and equal-budget runs on frozen issues. Measure accepted-patch rate, escaped defects, unauthorized edits, operator time, latency, and cost; exercise duplicate issues, restart, Agent failure, cancellation, and stale work. Publish only the slice actually demonstrated—not a mock ticket classifier called a software factory.

#Run reviewed code safely

#Confidential counterparty evaluation

A team runs someone else's algorithm against its own private cases without sending the data back to the author.

Research idea · No Agent · Reviewed local run

• What the user gets: A buyer, laboratory, or compliance team evaluates counterparty-authored algorithms, formulas, or checks against operator-owned cases without sending those cases to the author.
• Why Jig: Exact reviewed bytes meet local data inside an independently enforced offline envelope. Edited source requires a new admission; the retained old admitted bytes remain runnable.
• Simplest version: One deterministic FLOW package and one Run. An Agent only adds another data recipient.
• What it needs: Exact admission, bounded input and result, no network or ambient host access, whole-tree limits, cancellation, and cleanup. The operator must treat output and diagnostics as possible disclosures; private input also needs a non-argument channel and an explicit retention policy, because the current host retains canonical root input in project history.
• Use something else when: A signed script, hardened CI worker, or disposable VM wins when the parties already share trust or an expert team operates the sandbox without meaningful integration cost.
• What would prove it: Test legitimate and hostile packages, mutation after admission, resource abuse, host reads, network access, deliberate output leakage, and residue. Compare setup time, review burden, repeat-run effort, and operator error with a competently configured VM or CI worker.

#Quarantined format decoder

A suspicious or obsolete file is opened by a disposable decoder instead of directly on the operator's machine.

Research idea · No Agent · Reviewed local run

• What the user gets: An archive or investigator extracts bounded metadata or a preview from a malformed legacy file while treating both file and parser as potentially hostile.
• Why Jig: A reusable parser package receives only the artifact and fixed resources; failures and descendants remain inside one reviewed Run.
• Simplest version: One decoder package per artifact, with an exact result schema and no Agent.
• What it needs: Read-only artifact projection or bounded streaming, file-type and output limits, containment, timeout, and cleanup. Jig does not prove that decoded content is semantically safe.
• Use something else when: Established content-disarm software or a disposable VM wins for standard formats and centralized operations.
• What would prove it: Use valid, malformed, decompression-bomb, parser-crash, fork, and exfiltration fixtures; compare supported-format coverage, operator effort, escape resistance, and cleanup with the best existing decoder path.

#Give one Agent a narrow job

#AI response release gate

A final checkpoint holds unsafe or unsupported AI responses before another system or person relies on them.

Research idea · One Agent · One bounded Agent call

• What the user gets: A product safety or content-operations team receives an accept, hold, or human-review record before its AI application's response is published or passed to another system.
• Why Jig: The application can invoke an independently admitted reviewer with an explicit policy skill and closed result while retaining all release authority outside the model.
• Simplest version: Run deterministic schema and allow-list checks first; call one semantic reviewer only for injection, citation support, or policy questions that exact code cannot decide.
• What it needs: Per-call skill projection, bounded instructions, structured results, fail-closed validation, and an acceptable provider data posture. Citation checks also require an authoritative evidence packet.
• Use something else when: A guardrail library, schema validator, local classifier, or application-native review call wins whenever it supplies the same policy and authority boundary more directly.
• What would prove it: Evaluate false accepts, false holds, abstentions, latency, cost, and excluded-context leakage on adversarial and ordinary traffic. Compare with the best deterministic checks and the identical model call embedded directly in the application.

#Consent promise audit

A research team compares what participants were promised with what the study actually collects and exports.

Research idea · One Agent · One bounded Agent call

• What the user gets: A research team receives a source-linked matrix of mismatches among consent language, study protocol, collected fields, and export plans.
• Why Jig: A reusable review procedure can receive only the selected institutional policy skill and return findings without access to research systems or authority to approve the study.
• Simplest version: One privacy-review role followed by deterministic source and result validation.
• What it needs: Document input, source coordinates, collection-shaped results, and an acceptable provider. Policy correctness and ethics review remain external.
• Use something else when: A GRC product or local LLM review application wins unless independently maintained review packages and per-call context boundaries materially reduce integration or governance work.
• What would prove it: Domain reviewers label a blinded corpus; compare material-mismatch recall, unsupported findings, review time, and policy leakage with the incumbent process.

#Archive release screening

An archivist gets a focused list of passages that may conflict with the collection's release restrictions.

Research idea · One Agent · One bounded Agent call

• What the user gets: An archivist receives passages, names, and dates needing human review under one collection's donor and release restrictions.
• Why Jig: The same admitted screener can be reused while each call receives only the selected collection policy rather than a broad rights database.
• Simplest version: One screening role with source-linked findings and no publication authority.
• What it needs: Bounded document input, collection results, source spans, skill isolation, and a provider acceptable for the records.
• Use something else when: Archive-management software plus a local model wins when one institution owns the complete stack and policy set.
• What would prove it: Measure missed restrictions, unnecessary holds, source accuracy, review time, and leakage of sibling-collection policy on professionally labelled records.

#Near-miss normalization

Free-form safety reports are converted into the site's approved categories for a human to confirm.

Research idea · One Agent · One bounded Agent call

• What the user gets: A safety lead receives approved incident class, severity, and escalation fields from narrative near-miss reports for human confirmation.
• Why Jig: Sites can share an admitted extraction procedure while each projects only its own taxonomy and guidance.
• Simplest version: One Agent returns closed enums plus cited evidence; deterministic code rejects unknown values.
• What it needs: Per-call skills, closed structured output, explicit privacy posture, and site-owned categories. Jig supplies no safety judgment.
• Use something else when: A local classifier or incident-management product wins for high-volume, stable categories or one centrally managed site.
• What would prove it: Compare class and escalation errors, abstention, inter-reviewer disagreement, operator time, and cross-site taxonomy leakage with the strongest local classifier.

#Connect known steps

These examples follow a known route from start to finish. A step may use ordinary code, call an Agent, or run another reviewed Flow, but a model does not decide which procedure comes next.

#Grant proposal workshop

A proposal is drafted, checked against the evidence and budget, repaired, and handed back for human submission.

Research idea · One Agent · Fixed workflow

• What the user gets: A nonprofit receives a submission-ready proposal whose claims, budget, eligibility, and required sections survive explicit review gates.
• Why Jig: Evidence gathering, drafting, financial checking, and final acceptance can be exact admitted components with distinct skills and bounded repair loops rather than one context grading its own prose.
• Simplest version: One drafting Agent plus exact completeness and budget checks. Add a separate evidence or eligibility reviewer only when it has distinct sources or rejection authority.
• What it needs: Exact child calls, per-call skills, bounded repair, deterministic gates, and a human submission decision. Funding data and organizational evidence are application inputs.
• Use something else when: One strong writing Agent plus a checklist and spreadsheet wins unless separated gates reduce unsupported claims or review effort enough to justify added calls.
• What would prove it: On frozen grant briefs, compare eligibility failures, unsupported claims, budget inconsistencies, reviewer scores, operator time, cost, and latency with the one-Agent baseline.

#Procurement evidence brief

Vendor claims are gathered and challenged before they become a recommendation.

Research idea · One Agent · Fixed workflow

• What the user gets: A procurement team receives a source-grounded comparison whose claims and recommendation can be traced to current vendor evidence.
• Why Jig: A research component gathers evidence while a separately scoped reviewer can reject unsupported or unsuitable claims before composition.
• Simplest version: Research, evidence review, then deterministic assembly; omit the second Agent if mechanical citation checks perform as well.
• What it needs: Source access supplied by the application, exact child inputs, citation-bearing results, distinct review authority, and a human purchasing decision.
• Use something else when: A research assistant, procurement platform, or one web-capable Agent wins unless separation improves source fitness and reduces unsupported conclusions.
• What would prove it: Compare factual support, omitted material risks, source freshness, decision-maker effort, calls, and cost on completed procurement decisions with known evidence.

#Underpayment reconstruction

Messy work records become an auditable calculation of wages that may be missing.

Research idea · One Agent · Fixed workflow

• What the user gets: A worker, union, or clinic receives a source-linked ledger of possible missing wages computed under exact reviewed rules.
• Why Jig: Independently maintained extraction and jurisdictional rule packages can be distributed without allowing the Agent to decide entitlement or perform monetary arithmetic.
• Simplest version: One logical extraction role over bounded records, deterministic normalization, exact decimal calculation, and professional review.
• What it needs: Document input, source coordinates, collection results, selected skills, and exact rule code. OCR, legal rules, acceptable data processing, and professional judgment are external prerequisites.
• Use something else when: Payroll-audit software or a local extraction application feeding the same calculator wins unless cross-party package maintenance is measurably easier or more trustworthy with FLOW.
• What would prove it: Measure final-ledger precision and recall, usable case coverage, unresolved rate, arithmetic errors, review time, and false negatives on stratified professional fixtures; separately test rule updates across more than one clinic or jurisdiction maintainer.

#Disaster claim binder

Photos, receipts, policies, and inventories are assembled into a reviewable claim package without deciding coverage.

Research idea · One Agent · Fixed workflow

• What the user gets: A household receives a room-by-room evidence matrix linking damage, inventory, receipts, policy clauses, and missing evidence without an automatic coverage decision.
• Why Jig: An independent aid organization can distribute an inspectable local method whose extraction roles cannot submit claims or alter originals.
• Simplest version: Bounded artifact extraction followed by deterministic hashing, deduplication, ordering, and joining.
• What it needs: Multimodal artifacts, source coordinates, typed collections, and controlled local storage. Coverage interpretation and claimant approval remain outside Jig.
• Use something else when: Claims-management software or a local multimodal application wins when neutrality, inspectability, and independent package maintenance do not matter.
• What would prove it: Compare evidence recall, false associations, missing-item usefulness, preparation time, and user comprehension on professionally reviewed claim sets.

#Food recall trace

Inconsistent supplier and production records become a traceable map from suspect lots to finished goods.

Research idea · One Agent · Fixed workflow

• What the user gets: A small producer traces suspect supplier lots to finished batches from inconsistent invoices, certificates, and production logs.
• Why Jig: Semantic extraction cannot alter the exact lot-graph algorithm, and an independently maintained procedure can run without receiving the producer's operational credentials.
• Simplest version: Bounded extraction by record type followed by an exact graph with source-linked uncertain edges.
• What it needs: Document projection, typed collections, provenance, exact graph code, and human recall authority. Jig is not a traceability database.
• Use something else when: An integrated ERP or traceability product wins whenever the producer already has clean operational data.
• What would prove it: On known lot histories, measure missed and false edges, unresolved records, time to isolate affected batches, and operator effort against the existing process.

#Protocol deviation reconstruction

Logs, notes, and the approved procedure are combined into a timeline that keeps contradictions visible.

Research idea · One Agent · Fixed workflow

• What the user gets: A research or manufacturing team receives an evidence-linked timeline of departures from an approved protocol without erasing contradictions.
• Why Jig: Agents extract bounded claims; exact code normalizes chronology and preserves competing source statements for review.
• Simplest version: Extract obligations and observations separately, then join by exact time and identifier rules.
• What it needs: Document input, source spans, typed collections, exact time handling, and domain review. Jig does not determine regulatory impact.
• Use something else when: QMS or LIMS software wins for already integrated operations with reliable structured records.
• What would prove it: Compare event and deviation recall, false joins, contradiction retention, investigation time, and reviewer agreement on known incidents.

#Compartmentalized accession

A museum produces internal rights notes and a public label without showing private donor terms to the writing step.

Research idea · Multiple Agents · Fixed workflow

• What the user gets: A museum receives private rights flags, handling notes, and a public label while the public-writing role never receives donor terms.
• Why Jig: Information separation can be an exact reviewed dataflow with different skills and inputs rather than an instruction to one all-seeing model.
• Simplest version: A private rights role, deterministic cleared-fact projection, and a separately scoped public-writing role.
• What it needs: Narrow child inputs, rich intermediate contracts, source documents, diagnostics review, and per-call skills. Rights clearance and reidentification analysis remain institutional responsibilities.
• Use something else when: A bespoke local model pipeline or one trusted collections editor wins unless independently maintained components and inspectable context exclusion reduce real risk.
• What would prove it: Seed direct and indirect private facts and factual invariants; inspect every output channel, label usefulness, rights-review time, and leakage against the strongest local pipeline.

#Private feedback analysis

Sensitive feedback is summarized without giving the analysis step the identities behind it.

Research idea · One Agent · Fixed workflow

• What the user gets: An organization receives themes and actionable concerns while the analysis role never receives respondent identities.
• Why Jig: A trusted projection role and restricted analysis package can have different visible inputs and authority under one inspectable run tree.
• Simplest version: Deterministic or trusted de-identification, restricted analysis, then permitted reintegration; no voting or extra personas.
• What it needs: Explicit information-flow contracts, narrow child input, bounded results, diagnostic controls, and a realistic reidentification threat model.
• Use something else when: A data clean room, local redaction pipeline, or one authorized analyst wins when package reuse and host-enforced separation provide no additional assurance.
• What would prove it: Seed direct and quasi-identifiers; measure leakage, theme utility, false grouping, and operator effort across all observable channels.

#Public notice adaptation

A public notice is made clearer, more accessible, and easier to translate without changing its protected facts.

Research idea · One Agent · Fixed workflow

• What the user gets: A public body receives clear, accessible, and localized notice variants without changes to protected dates, obligations, contacts, or legal facts.
• Why Jig: Independently maintained transformations can receive disjoint edit scopes while deterministic checks protect exact fields between stages.
• Simplest version: One logical editing role may run several lenses; multiple Agents are justified only by different skills or language authority.
• What it needs: Structured source content, exact invariant checks, bounded child transformations, and qualified accessibility and translation review.
• Use something else when: A template system or one constrained editor wins when the transformations and languages are centrally managed.
• What would prove it: Inject tempting factual changes and compare invariant violations, readability, accessibility, translation quality, review time, and cost with one all-purpose editor.

#Futureproof event plan

An organizer sees whether the same plan still makes sense under low, expected, and high attendance.

Research idea · One Agent · Fixed workflow

• What the user gets: An organizer sees how a choice among authorized plans changes across explicit attendance scenarios instead of receiving one blended recommendation.
• Why Jig: Scenario-isolated Agent calls can be bounded and joined by exact code that cannot invent choices or smooth disagreement into a false robust answer.
• Simplest version: One logical role invoked separately per scenario; a deterministic join compares closed choice IDs. This does not require child Flows.
• What it needs: Closed Agent results, isolated instructions, fixed scenarios, bounded explanations, and a human decision. Jig supplies neither scenarios nor probabilities.
• Use something else when: A comparison table, deterministic regret model, or the identical multi-call protocol in a small Agent script wins unless FLOW admission and reuse create an additional measured benefit.
• What would prove it: Compare isolated and all-context treatments at equal calls and token budgets, plus the identical non-Jig protocol. Use blinded invariant, reversal, and defer cases; measure false robustness, decision quality, user effort, variance, latency, and cost.

#Career transition bridge

A career changer searches forward from current skills and backward from a target role to find a realistic bridge.

Research idea · One Agent · Fixed workflow

• What the user gets: A career changer receives feasible bridge states connecting current evidence and constraints to the prerequisites of a target role.
• Why Jig: Forward feasibility and backward prerequisite search can commit independently before exact compatibility checks expose the smallest missing bridge.
• Simplest version: Two bounded searches plus a typed join; use one planner if separation does not alter results.
• What it needs: Explicit current evidence, target criteria, bounded search, no fabricated qualifications, and human ownership of commitments.
• Use something else when: A career adviser or one planning Agent wins unless independent frontiers reveal materially more valid and actionable bridges.
• What would prove it: On longitudinal cases, compare valid bridge discovery, missing prerequisites, unsupported claims, user follow-through, cost, and time with one strong planner.

#Curriculum blind-spot audit

A curriculum is examined across two different dimensions to reveal important combinations it never teaches.

Research idea · One Agent · Fixed workflow

• What the user gets: An educator receives evidence-backed gaps at the intersections of subject concepts and cognitive skills.
• Why Jig: Separate bounded analyses can derive genuinely different axes before exact crossing, reducing the chance that one framing hides omissions.
• Simplest version: Derive or supply two axes, cross them deterministically, and investigate high-risk empty cells.
• What it needs: Curriculum artifacts, stable coverage evidence, explicit axis definitions, and educator review. A filled grid is not proof of completeness.
• Use something else when: A conventional curriculum rubric wins when both axes are already known or one analyst can apply them reliably.
• What would prove it: Seed intersection-only omissions; compare recall, false gaps, axis correlation, teacher usefulness, effort, and cost with the best existing rubric.

#Household energy investigation

Competing explanations for a surprising energy bill are narrowed through safe, inexpensive observations.

Research idea · One Agent · Fixed workflow

• What the user gets: A household receives safe, low-cost observations that distinguish plausible causes of an unexplained energy-cost spike.
• Why Jig: Rival causal models can remain separate until evidence arrives, while exact policy prevents either role from authorizing unsafe tests.
• Simplest version: Commit predicted observations for bounded hypotheses, choose one safe discriminator, update, and stop at a fixed budget.
• What it needs: User-supplied bills and observations, a safe-test allow-list, explicit uncertainty, and referral to qualified professionals.
• Use something else when: Utility diagnostics, an electrician, or a professional troubleshooting guide wins whenever it already determines the safe next test.
• What would prove it: Use known-cause fixtures and supervised field cases; compare unsafe advice, premature closure, tests requested, diagnostic accuracy, cost, and time with one diagnostic Agent.

#Truthful job application

A job application is tailored to an employer while every claim stays tied to the applicant's real experience.

Research idea · One Agent · Fixed workflow

• What the user gets: A job seeker receives a tailored application whose claims are traceable to supplied experience and whose gaps remain explicit.
• Why Jig: Employer research, evidence mapping, drafting, and unsupported- claim review can have separate inputs and stop rules while the user retains submission authority.
• Simplest version: One drafting Agent plus deterministic evidence links; add research or review roles only when their separation changes errors.
• What it needs: Selected skills, exact child calls where roles are separate, bounded personal data, source-linked claims, and human submission.
• Use something else when: A strong career assistant wins unless separated evidence review reduces fabricated or weakly supported claims enough to justify added work.
• What would prove it: Compare unsupported claims, interview relevance, user editing time, response rate where observable, privacy exposure, cost, and latency with one strong assistant.

#Choose among approved routes

#Repair diagnostic

A technician describes a problem in ordinary language and receives one approved next diagnostic step—or an abstention.

Research idea · One Agent · Approved route selection

• What the user gets: A technician receives the next test from one admitted, model-compatible procedure—or an explicit abstention—from free-text symptoms.
• Why Jig: The model selects an authorized identity; deterministic code owns eligibility and exact invocation, so a nonexistent reset or unsafe command cannot enter the candidate universe.
• Simplest version: Filter a finite catalogue, request one closed ID or abstention, validate it, then call the exact procedure.
• What it needs: Host-owned candidate construction, compatibility metadata, complete candidate disclosure, abstention, and choice evidence. Device authority is a separate capability, not part of selection.
• Use something else when: OEM diagnostics, a decision tree, or a structured form wins for known fault codes and machine-readable observations.
• What would prove it: Freeze the candidate set and test ambiguous, unauthorized, incompatible, and nonexistent requests. Measure selection, abstention, unsafe routing, task completion, and operator effort against the best deterministic router; any out-of-set invocation is a hard failure.

#Vetted rule front desk

A messy benefits or legal account is directed to the correct maintained rule calculator instead of answered from model memory.

Research idea · One Agent · Approved route selection

• What the user gets: A legal-aid or benefits worker receives a result from one admitted jurisdiction-specific calculator, or an abstention, after entering an unstructured account.
• Why Jig: Semantic interpretation may identify the applicable exact rule package without allowing the model to invent law, arithmetic, or executable procedure.
• Simplest version: Deterministic jurisdiction and version filtering, one closed semantic choice, exact calculation, and professional review.
• What it needs: Maintained rule packages, compatibility, clarification and abstention, source-linked facts, and human legal authority.
• Use something else when: A structured expert system wins when a form can collect the legally relevant facts reliably.
• What would prove it: Domain experts label cases and exclusions; compare applicable-package selection, unsafe false positives, abstention, completion time, and review effort with the form and expert-system baseline.

#Approved waste disposition

An ambiguous waste description is matched to an approved handling procedure rather than improvised safety advice.

Research idea · One Agent · Approved route selection

• What the user gets: A laboratory coordinator receives an EHS-approved calculation and handling checklist—or abstention—from an ambiguous waste description.
• Why Jig: The model chooses among admitted methods rather than composing plausible but unauthorized safety advice.
• Simplest version: Deterministic compatibility filtering, one closed choice, exact method execution, and mandatory human confirmation.
• What it needs: EHS-owned package inventory, material attributes, jurisdiction and facility compatibility, abstention, and no device or disposal authority.
• Use something else when: An EHS form or specialist product wins when waste attributes can be captured structurally.
• What would prove it: Use expert-labelled edge cases and adversarial unknowns; compare unsafe selection, unnecessary abstention, operator effort, and task completion with the incumbent form.

#Approved release transform

A records steward selects the approved transformation for a specific release without allowing the model to weaken policy.

Research idea · One Agent · Approved route selection

• What the user gets: A records steward applies one admitted redaction or de-identification procedure appropriate to the dataset and release purpose, while retaining release authority.
• Why Jig: Semantic interpretation may select policy implementation but cannot synthesize, weaken, or execute an unapproved transform.
• Simplest version: Deterministic purpose and jurisdiction filtering, closed choice or abstention, exact transform, and human review.
• What it needs: Artifact projection, maintained transform packages, compatibility metadata, result inspection, and disclosure policy.
• Use something else when: DLP or records-management software wins for stable document classes and centrally administered policy.
• What would prove it: Test known classes, mixed-purpose records, unknown requests, and adversarial attempts to select weaker transforms; measure unsafe releases, over-redaction, abstention, and review time.

#Supervise ongoing Agent work

#Time-travel handoff

A busy worker writes a handoff, then continues from an earlier conversation checkpoint with the current implementation and newly learned lessons. In the automatic case, other workers keep progressing during the transition.

Research idea · Manual practice reported · Automatic supervision unproved

• What the user gets: Earlier decisions stay available without the entire recent execution transcript, plus an opportunity to review current work from a fresh context without manually supervising every worker's handoff.
• Why Jig: A reusable supervision method could act on only its authorized workers while the host enforces lifetime, data access, and cleanup.
• Simplest version: One completed work episode, a handoff, and an earlier conversation fork against the unchanged current files. This does not yet prove automatic intervention while multiple Agents are busy.
• What it needs: Retained conversation checkpoints and demonstrated client control; the automatic case also needs observable progress, safe stopping boundaries, bounded concurrency, and defined handling of competing requests. None implies a selected public session or event API.
• Use something else when: Client-native compaction or a small application controller provides the same context and authority guarantees with less work.
• What would prove it: Preserve decisions and newer instructions without replaying effects; then automatically transition one of two active workers while the other progresses. Evaluate review quality and operator effort separately from lifecycle correctness, under comparable budgets.

The full use-case brief preserves the manual prompts, automatic scenario, coordination questions, failure boundaries, and falsifiers.

#React reliably to outside events

#Auditable allocation

A cooperative or grant program uses a frozen participant list and a seed chosen in advance to produce one reproducible allocation of places or funds.

Research idea · No Agent · Event-triggered work

• What the user gets: A cooperative or grant program obtains one reproducible allocation tied to a frozen roster, committed seed, and exact algorithm.
• Why Jig: One authenticated close fact can be durably associated with the exact admitted computation without speculative redispatch after uncertainty.
• Simplest version: One fact derives one deterministic Run and immutable result; no Agent.
• What it needs: Trustworthy fact identity, frozen input, durable derivation, correction procedure, and public or participant review. Jig does not authenticate the roster's social legitimacy.
• Use something else when: Specialist lottery software or a transactional job service wins unless local inspectability and independently distributed algorithms matter.
• What would prove it: Exercise duplicate facts and crashes before, during, and after dispatch; verify one durable Jig derivation, reproducibility, uncertainty reporting, corrections, and participant comprehension. Publishing an official allocation is a separate idempotent external effect and must be tested as such.

#Cold-chain exception packet

A real temperature excursion produces one calculation and review packet tied to the right procedure revision.

Research idea · No Agent · Event-triggered work

• What the user gets: A biobank or distributor receives one exposure calculation and operator packet for each real temperature excursion.
• Why Jig: The fact can bind to the exact SOP and package revision active at derivation, while duplicate sensor delivery cannot silently create a competing assessment.
• Simplest version: Deterministic excursion grouping and exposure calculation; an optional Agent drafts only the human-readable brief.
• What it needs: Authenticated sensor facts, excursion identity, durable derivation, SOP revision, correction policy, and human disposition authority. Holds and notifications need separately idempotent integrations.
• Use something else when: IoT monitoring or cold-chain SaaS wins for connector-rich ordinary alerting.
• What would prove it: Replay realistic telemetry with duplicates, reorderings, gaps, coordinator loss, and corrections; compare missed and duplicate excursions, SOP binding, operator time, and recovery with the incumbent platform.

#Share protected services

#Privacy-budgeted analysis

Repeated statistical answers can gradually expose individuals. One shared service answers useful aggregate questions while tracking and limiting that total disclosure.

Research idea · No Agent · Shared protected service

• What the user gets: A data steward lets independently authored analyses return approved aggregates while one provider atomically enforces cumulative privacy expenditure.
• Why Jig: Packages cannot obtain raw rows, database credentials, or a new budget simply by starting another Run.
• Simplest version: Exact analysis packages call a typed long-lived provider that owns dataset access, caller identity, accounting, and results.
• What it needs: A real privacy mechanism, transactions, recovery, revocation, governance, and side-channel review. A toy counter proves only plumbing.
• Use something else when: A data clean room or centralized analytics service wins unless independently distributed local packages genuinely need the shared authority.
• What would prove it: Use at least two independent consumers; test concurrent calls, budget exhaustion, restart, caller substitution, provider loss, raw-data escape, and analytical utility against an established privacy platform.

#Field evidence sealing

Field evidence is stored, linked, timestamped, and signed without giving collection workflows the signing keys.

Research idea · No Agent · Shared protected service

• What the user gets: A journalist or field inspector creates a tamper-evident bundle linking originals, derivations, trusted time, and signatures.
• Why Jig: Independently authored collection procedures can use one local vault without receiving signing keys or mutable evidence storage.
• Simplest version: Exact collection packages call a narrow append-and-seal provider; the provider owns key and ledger lifetime.
• What it needs: Secure key storage, trusted time, canonical media handling, append-only persistence, recovery, and evidence policy.
• Use something else when: A forensic evidence application wins when one organization and workflow own the entire lifecycle.
• What would prove it: Use independent producer packages and test tampering, rollback, concurrent append, key access, crash recovery, verification portability, and operator error against the incumbent tool.

#Instrument protocol commons

Independently authored laboratory procedures use equipment through a safe local service, without receiving direct device control. The service enforces calibration and safety checks.

Research idea · No Agent · Shared protected service

• What the user gets: A community laboratory runs independently reviewed methods against vendor-neutral instruments while calibration and interlocks remain under local control.
• Why Jig: Protocol packages can remain replaceable while a typed provider owns units, sessions, exclusive access, device credentials, and safety.
• Simplest version: One long-lived instrument provider and exact protocol packages; Agents are unnecessary for physical control.
• What it needs: Device-specific implementation, calibration, units, cancellation, exclusive sessions, recovery, and physical safety review.
• Use something else when: Vendor instrument software wins in a single-vendor laboratory or whenever certified integrations already cover the methods.
• What would prove it: With safe simulated hardware first, test units, concurrency, cancellation, provider loss, interlocks, and cross-package portability; physical deployment requires independent safety evidence.

#Consent-gated archive

Archive tools can ask whether an item may be used without receiving the underlying rights database.

Research idea · No Agent · Shared protected service

• What the user gets: Transcription, analysis, and publication packages can ask whether an item may be used without receiving the raw rights database.
• Why Jig: Independently maintained tools share one least-authority local consent boundary whose state and revocation outlive one Flow call.
• Simplest version: A typed provider owns consent history and exposes narrow query and update operations to authenticated consumers.
• What it needs: Caller identity, concurrency rules, authenticated changes, durable history, recovery, revocation, and institutional policy.
• Use something else when: A monolithic archive-management system wins when one application owns all workflows and integrations.
• What would prove it: Use at least two independent consumer packages; test concurrent updates, withdrawal, stale decisions, unauthorized queries, restart, and audit comprehension against the existing archive system.

#Safe repair bus

Repair packages receive only a short list of safe diagnostic operations instead of unrestricted low-level control of a vehicle or appliance.

Research idea · No Agent · Shared protected service

• What the user gets: Independent repair procedures query a vehicle or appliance through typed diagnostics without obtaining arbitrary serial or bus access.
• Why Jig: A provider can enforce model compatibility, sessions, and safe operations while procedures remain separately admitted and replaceable.
• Simplest version: Exact repair package calling a narrow device provider; semantic dispatch may choose the package but never the raw operation.
• What it needs: Device-specific interlocks, exclusive sessions, cancellation, operator confirmation, version compatibility, and physical safety assurance.
• Use something else when: OEM service tools or a specialist diagnostic application wins unless independent cross-vendor procedures are the central requirement.
• What would prove it: Start with a simulator; test unauthorized commands, session collision, cancellation, model mismatch, provider loss, and useful diagnosis before any supervised physical trial.

#Build complete applications

#Persistent job-search campaign

A job search continues across openings, deadlines, applications, and follow-ups while the user approves every external action.

Research idea · One Agent · Complete application

• What the user gets: A job seeker discovers suitable openings, verifies fit, prepares truthful applications, retains status, and responds to deadlines while approving every submission.
• Why Jig: Independently admitted research, evidence, drafting, and review procedures can evolve while personal data, external accounts, and submission authority remain separately scoped.
• Simplest version: Begin with the truthful application Flow and explicit user-selected openings. Add durable facts, discovery, and specialized roles only after each improves outcomes independently.
• What it needs: Personal-data policy, source integrations, durable application state, deadlines, human approvals, and idempotent external actions. Job boards and messaging are application integrations.
• Use something else when: An applicant tracker plus a strong career Agent wins unless procedure reuse and least-authority account boundaries improve response quality or substantially reduce user work.
• What would prove it: Run a consented longitudinal study; measure suitable opportunities, unsupported claims, completed applications, interviews, user time, privacy incidents, notification errors, cost, and abandonment against the participant's incumbent process.