> For AI agents: the complete documentation index is available at https://jig.md/llms.txt, the full documentation bundle is available at https://jig.md/llms-full.txt. # 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](https://jig.md/guide/index.md) shows what people can use now. ## Find an example The examples grow with Jig, but no application has to use every capability. | What Jig adds | A good first example | | -------------------------------- | ----------------------------------------------------------------------------- | | Run reviewed code safely | [Confidential counterparty evaluation](#confidential-counterparty-evaluation) | | Give one Agent a narrow job | [AI response release gate](#ai-response-release-gate) | | Connect known steps | [Grant proposal workshop](#grant-proposal-workshop) | | Choose among approved routes | [Repair diagnostic](#repair-diagnostic) | | Supervise ongoing Agent work | [Time-travel handoff](#time-travel-handoff) | | React reliably to outside events | [Cold-chain exception packet](#cold-chain-exception-packet) | | Share a protected service | [Privacy-budgeted analysis](#privacy-budgeted-analysis) | The [software factory](#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](https://jig.md/orchestration-patterns.md). ## 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](https://jig.md/time-travel-handoff.md) 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.