Normalize inbound messages without discarding the original record
Quoted email chains dominate routing input and duplicate attachments inflate request size. Create a bounded classification view while retaining the synthetic original message separately.
- Focused work estimate
- 1h 30m + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Text processing · Data provenance · Input limits
Estimated field mix
- Data engineering70%
- Applied AI30%
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
- The derived view records normalization version, source message revision, and truncation indicators.
- Input size is bounded and attachments contribute only permitted metadata.
- The original record is immutable and can be inspected by an authorized support reviewer.
Implementation constraints
- Normalization is deterministic; do not use a model to silently rewrite the complaint.
Verification to include
- Normalize a long quoted chain twice and compare identical derived views.
- Provide oversized text and an attachment filename containing markup; verify bounded plain-text input.
Deliverables
- Normalization pipeline and input-limit fixtures
Rollout and recovery
Compare derived views in local review before enabling suggestions; use the original source reference for disputed cases.
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.