Automate support categorization with a fallback queue

Ticket categorization should reduce sorting work without making uncertain cases disappear. The fallback queue is a normal workflow state, not an implementation error.

Route the fixture

The automation lab accepts one authored result and sends two to review.

from automation_cases import support_queue_case

result = support_queue_case()
assert result["auto_ids"] == ["T1"]
assert result["fallback_ids"] == ["T2", "T3"]
assert result["rows"][1]["confidence"] == 0.61
assert result["model_executed"] is False

No classifier ran; categories and confidence are fixtures.

Define acceptance beyond confidence

Use a closed category schema, validate the output and require relevant permissions. Add rules for high-risk content, VIP/service obligations, unsupported language and new intents. Confidence is model-specific and needs calibration; it cannot override a critical-route policy.

The exception record needs ticket ID, proposed category, reason, model/release and deadline. Assign an owner and alert when queue age exceeds the service objective. Human corrections become labelled candidates only after review, privacy checks and split protection.

Evaluate per-category precision/recall, fallback rate, misroute severity, queue time and downstream resolution. Compare with the same manual baseline. Do not count fallback as a model failure if abstention is the required safe behaviour.

The Generative & Agentic AI course links classification evals with business exception handling.

Exercise

Create fifty labelled tickets with an unknown class. Tune an acceptance threshold on development data and report holdout routing, fallback workload and high-impact errors.

Continue learning

This article is part of the Business automation with AI sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Build an approval-first invoice extraction workflow.
• Continue with AI email drafting with a mandatory human send step.

Reference: NIST AI RMF Generative AI Profile.