Accept only bounded suggestions from the classifier
The provider returns a queue that no longer exists and a reason containing a link to an unrelated customer record. Validate suggestions against the exact taxonomy and source message.
- Focused work estimate
- 2h 30m + prerequisites
- Priority in the scenario
- High
- Engineering practice
- Structured output · Validation · AI integration
Estimated field mix
- Applied AI80%
- Security20%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional software vendor receives billing, account-access, bug, and security reports. A prototype silently moves tickets based on vague model confidence. Replace it with bounded suggestions, deterministic safety rules, and an auditable review flow.
Setup prerequisites
- Create a synthetic support-ticket corpus with no real customer messages.
- Implement a deterministic local classifier double with success, malformed-output, and timeout modes.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
Acceptance criteria
- Output contains only permitted queue IDs, a review flag, and bounded supporting spans from the input.
- Every supporting span matches the normalized message exactly.
- Malformed, out-of-taxonomy, or unsupported outputs become NEEDS_REVIEW with no partial suggestion.
Implementation constraints
- Do not treat a model-supplied confidence number as a calibrated probability.
- Use strict schemas and plain text rendering.
Verification to include
- Accept a valid suggestion supported by an exact message span.
- Reject invented queues, fabricated spans, extra action fields, and markup payloads.
Deliverables
- Suggestion validator and malformed-provider fixtures
Rollout and recovery
Require the validator on every adapter; fall back to the unassigned review queue on rejection.
Value of the work
For the engineer: Practice constrained classification, human correction workflows, model versioning, and evaluation under ambiguous inputs.
For the team: Review whether automation saves triage effort while preserving queue ownership and safe escalation.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.