# Support Ticket Triage & Escalation Workflow

Design an AI-assisted support workflow that classifies tickets, routes ownership, drafts replies, protects sensitive data, and escalates uncertainty.

## Prompt

You are a senior workflow automation designer. Your task is to design an AI-assisted support ticket triage and escalation workflow that keeps risky decisions and customer replies under appropriate human control.

Design the workflow so it can be implemented in Zapier, Make, n8n, a native integration, or custom code. Do not pretend that you have connected to, configured, tested, or deployed any system. Do not invent field names, API capabilities, permissions, prices, limits, credentials, business rules, or approval authority.

USER INPUT
Ticket sources: {{ticket_sources}}
Help desk, CRM, knowledge base, and messaging tools: {{tools}}
Ticket fields and available metadata: {{ticket_fields}}
Categories and priority definitions: {{categories_priorities}}
Routing and ownership rules: {{routing_rules}}
Service-level targets: {{sla}}
Knowledge sources allowed for reply drafting: {{approved_sources}}
Sensitive-data rules: {{data_rules}}
Auto-action boundaries: {{automation_boundaries}}
Escalation rules: {{escalation_rules}}

DESIGN METHOD
1. Define ticket intake and the events that create, update, or reopen a case.
2. Validate sender, subject, message, account, language, and attachments.
3. Design classification for category, urgency, sentiment, product, and required expertise.
4. Assign confidence thresholds for automatic routing versus human triage.
5. Define priority using supplied business rules rather than model sentiment alone.
6. Route security, legal, payment, safety, account-access, and high-impact issues to specialist review.
7. Allow the model to draft replies only from approved knowledge sources.
8. Require human approval before sending replies unless the user defines a narrow, low-risk approved template.
9. Track SLA timers, ownership, escalation, reassignment, and closure evidence.
10. Add monitoring for misclassification, unsafe drafts, repeated contacts, and backlog growth.

REQUIRED OUTPUT
A. Intake and ticket state model
B. Validation and sensitive-data controls
C. Classification taxonomy and confidence rules
D. Priority and routing decision table
E. AI reply-drafting boundaries
F. Human-review and specialist escalation gates
G. SLA timers and notifications
H. Failure and fallback behavior
I. Monitoring and quality metrics
J. Test set with edge cases
K. Implementation checklist

CORE RELIABILITY RULES
1. Separate confirmed requirements from assumptions and unresolved questions.
2. Define one precise trigger and the conditions that should block false or duplicate runs.
3. Identify the system of record for every important field.
4. Map data explicitly between source and destination fields.
5. Validate required fields before taking downstream actions.
6. Use a stable unique identifier or idempotency key to prevent duplicate records and repeated messages.
7. Add human approval before external messages, publishing, payments, deletion, permission changes, or other difficult-to-reverse actions.
8. Define retries, timeouts, error logging, alert ownership, and a manual fallback.
9. Minimize movement and storage of personal or sensitive data.
10. Keep secrets in the automation platform's secure credential manager. Never request real API keys, passwords, access tokens, or webhook secrets.
11. Include test cases for the happy path, missing data, duplicates, invalid data, unavailable services, permission failures, and partial completion.
12. End with a clear list of items that require human confirmation before implementation.
13. Never allow an AI classification alone to close a case, issue a refund, change account access, make a legal commitment, or resolve a safety concern.
14. Do not draft answers from unapproved or unverifiable knowledge sources.

## Best for

Customer support teams designing ticket routing, SLA alerts, knowledge-grounded reply drafts, and human escalation in help-desk platforms.

## Compatible tools

- Claude
- ChatGPT

## How to use

- Provide ticket categories, priority definitions, teams, and service levels.
- List approved knowledge sources and actions the automation may never take.
- Set confidence thresholds and specialist escalation rules.
- Evaluate the workflow on historical and synthetic edge-case tickets before launch.

## Customization tips

- Add language detection and route unsupported languages to translation review.
- Define different automation limits for authenticated and unauthenticated users.
- Measure routing accuracy and reply-approval rates by category.
- Exclude passwords, payment credentials, and unnecessary personal data from logs.

## Example input

Tickets arrive through Intercom. Categories are billing, bug, how-to, account access, security, and cancellation. Security and account-access tickets always require specialist review. AI may draft answers from the approved help center but cannot send them. Low-confidence classification goes to the triage queue. Critical tickets alert Slack and page the on-call owner.

## Example output

Routing rules:
Security → Security Review.
Account access → Identity and Access Review.
Billing → Billing Support.
Bug → Technical Support.
How-to → General Support.
Cancellation → Retention queue without automatic action.

Confidence:
Route automatically only above the approved confidence threshold and when no high-risk keyword or attachment condition is present.
All uncertain classifications enter Triage Review.

Reply handling:
Retrieve only from the approved help center.
Create a draft with cited source articles.
Require an agent to verify the account context and send.

Escalation:
Critical severity starts the emergency SLA, posts to the restricted Slack channel, and alerts the on-call owner.
The workflow never closes, refunds, or changes access automatically.