Support Ticket Triage & Escalation Workflow

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.