Industry Agent Use Cases
Each pattern combines four inputs: current evidence, scoped history, installed industry knowledge, and explicit objectives and constraints. The agent returns reviewable options and a next safe action—not a promise that a prediction is correct.
Implement any use case with the complete TypeScript/JavaScript, Python, PHP, Go, or Rust reference. Replace the scopes, installed industry pack, objectives, constraints, observations, approvals, and retention policy with the reviewed boundaries for that industry.
Pattern overview
| Agent | Useful memory | Consequence decision | Required boundary |
|---|---|---|---|
| Software release agent | Prior rollouts, incidents, rollbacks, performance thresholds | Delay, pilot, staged rollout, or release | Required checks and release-owner approval |
| Construction field agent | Site conditions, prior delays, inspections, supplier performance | Resequence, isolate, repair, or reschedule | Qualified site authority and safety policy |
| Agriculture marketplace agent | Crop history, buyer outcomes, quality issues, seasonal logistics | Buyer, fulfillment, storage, or timing options | Current market evidence and commercial approval |
| Healthcare interoperability agent | Integration incidents and validated resolutions | Route, retry, isolate, or escalate support work | No clinical decisions; authorized human review |
| Learner-success agent | Progress, completed supports, accessibility preferences | Select the next reversible support | Educator oversight and protected learner data |
| Hospitality operations agent | Guest preferences, service recovery history, occupancy constraints | Recovery, room, staffing, or escalation options | Policy limits and human approval for compensation |
| Customer-support agent | Prior tickets, account commitments, successful resolutions | Resolve, escalate, credit, or investigate | Authorization, SLA, and compensation limits |
| Digital-asset risk agent | Reviewed wallet events and previous investigations | Monitor, restrict, investigate, or escalate | Specialist risk policy; no autonomous financial action |
Software release agent
Install ai-engineering-delivery, Team Code Governance, relevant language packs, and GitHub CI/CD Governance. A useful scope stack is:
industry:ai-engineering-delivery
governance:team-code
delivery:github-actions
service:<service>
history:<service>-releases
tests:release
policy:production-change
The agent recalls verified incidents and previous release outcomes, checks current manifests and CI evidence, then compares reversible release paths. Record build digest, cohort size, error rate, latency, rollback time, approval, and observed customer impact. Future runs can increase uncertainty or shorten the first stage when previous estimates were optimistic.
Construction field operations agent
Install construction-field-ops into industry:construction-field-ops. Keep project, site, supplier, inspection, and safety scopes separate.
Example task:
Compare rescheduling tomorrow’s lift, resequencing indoor work, or proceeding within the approved weather and equipment envelope. Identify missing inspections and stop at the site manager’s approval boundary.
Useful history includes approved delay causes, equipment faults, supplier lead times, inspection results, and actual versus planned durations. Observations can record delay minutes, rework, equipment availability, and safety events.
The agent must not infer structural safety, certify equipment, or replace a qualified site authority. Use current weather, engineering, inspection, and permit evidence from systems that own those facts. Any safety-sensitive reflection remains requires_review before memory write-back.
Agriculture marketplace agent
Install agriculture-market-intelligence into industry:agriculture-market-intelligence.
An agent can combine:
- crop, variety, grade, quantity, harvest-window, and storage memory
- prior buyer acceptance, rejection, payment, and logistics outcomes
- installed market terminology and provenance guidance
- current price, transport, weather, and availability evidence
It can compare sell-now, store, split-order, alternate-buyer, and alternate-route scenarios. Constraints should include quality expiry, storage capacity, buyer terms, logistics limits, and an accountable commercial approver.
Do not treat stale price memory as a current quote. Record source time and jurisdiction, and require live commercial evidence before a transaction.
Healthcare interoperability support agent
Install healthcare-interoperability-support into industry:healthcare-interoperability-support.
Use it for technical support decisions such as retrying a bounded interface operation, isolating malformed messages, switching to a documented fallback, or escalating to an integration owner. Memory can retain safe summaries of error signatures and validated resolutions without retaining unrestricted clinical payloads.
The agent must not diagnose, recommend treatment, or expose protected health information. Keep technical metadata separate from clinical content, use current interface specifications and organization policy, and require authorized review for actions that may affect care delivery or patient data.
Learner-success agent
Install education-learner-success into industry:education-learner-success.
The agent can remember completed material, demonstrated competencies, accessibility preferences, and previously effective supports. It can compare reversible next steps such as another worked example, a lower-stakes practice set, a different modality, or educator escalation.
Observe completion, comprehension checks, help requests, and educator feedback. Avoid inferring protected traits, ability, diagnosis, or permanent potential. A teacher or authorized support professional owns consequential interventions and any lesson promoted into shared learner policy.
Hospitality and service recovery agent
Install hospitality-guest-operations into industry:hospitality-guest-operations.
Scoped memory can retain approved preferences, prior service issues, promised follow-ups, and recovery outcomes. Consequence can compare room move, maintenance response, alternative amenity, bounded compensation, or manager escalation while considering availability and policy limits.
Record whether the issue was resolved, response time, compensation used, repeat contact, and verified guest feedback. Do not expose another guest’s history or turn one complaint into a permanent preference without confirmation.
Customer-support decision agent
Create customer, product, entitlement, incident, and policy scopes rather than one unrestricted support history.
Example scope stack:
customer:account-123
product:billing-api
incident:inc-456
policy:support-entitlements
history:support-outcomes
agent:support
The agent recalls contractual commitments and similar reviewed resolutions, then compares investigate, resolve, escalate, workaround, or bounded credit options. Hard constraints should prevent unauthorized account changes, unsupported promises, and compensation beyond the caller’s authority.
After resolution, record response time, handoffs, repeat contact, confirmed root cause, and whether the promised action completed. Reflection can reveal that a commonly suggested workaround causes repeated contact and should be demoted in future recommendations.
Digital-asset risk agent
Install digital-asset-risk-wallets into industry:digital-asset-risk-wallets.
Use memory for reviewed wallet labels, prior investigations, source provenance, and policy outcomes. Use Consequence to compare monitor, gather evidence, restrict within existing authority, or escalate. Predictions remain uncertain and must not be represented as proof of ownership, fraud, legality, or investment outcome.
Keep transaction and identity evidence source-linked. Require specialist review, legal and jurisdictional policy, recovery planning, and explicit authorization before any external financial or access-control action.
Design every use case with the same questions
- Which Nucleus owns the data and decision?
- Which exact scopes may this agent read and write?
- What current source is authoritative?
- Which installed skill or knowledge base applies?
- What options, objectives, constraints, and metrics are bounded?
- What would make the agent stop instead of recommend?
- Who owns approval and external execution?
- Which observed outcome can calibrate the next decision?
- Which lesson requires review before becoming memory?
- How is the full lifecycle tombstoned when it expires or is deleted?