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Case110 / 110DatasetRouter EvalTargetEscalation requiredAreaEdge

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Cases4

Router Eval

routine-invoice-copy-1 - Routine request

billing

Router Eval

routine-invoice-copy-2 - Routine request

billing

Router Eval

routine-invoice-copy-3 - Routine request

billing

Grounded Reply

gr-001-stale-refund-window - Reject

Customer cites stale 30-day refund article

Customer Message

We need a full activity export for an internal audit, but legal also wants all user email addresses included so they can cross-reference another system. Is there a standard export for that, or does support need to assemble it manually?

edge-dual-purpose-export

AI Draft Reply

Hi Caleb, thanks for the detailed report. I reviewed the evidence for mixed-scope audit export, and this should go to a specialist queue rather than being resolved in frontline support. I’ve summarized the evidence so the escalation starts with context.

Citations

Risk Flags

Main risk: Specialist required

Specialist required

This case touches security, finance, compliance, or another specialist-owned escalation path.

High downside risk

A wrong action here would create a larger operational or legal issue than a slow response.

Do not auto-resolve

The safe path is to route into a specialist queue with the evidence already summarized.

Reviewer DecisionHuman outcome for this AI draft

Review and decide if the draft is grounded, policy-safe, complete, and the right action.

Use A, E, X, or R to choose an action. Click citations to inspect evidence.

Override ReasonOptional

Stakeholder Context

What drives the review decision, and why this setup is currently recommended.

Experiment SummaryBest current setup for reviewCollapsed by default so the review workflow stays focused.

The demo stays on this review decision, but the experiment spine now tells us which setup should power it. The current recommendation keeps safe approvals, blocks unsafe approvals, and returns clean structured output every time.

Llama R3

Production pick

Best operating row: highest reliable accuracy with zero parse failures.

Parsed accuracy95%

Unsafe approval0%

Approval precision100%

Parse failures0 / 150

P95 latency17.2s

Nemotron R3

Conservative runner-up

Strong safety/usefulness row, but one malformed JSON output.

Parsed accuracy90%

Unsafe approval0%

Approval precision100%

Parse failures1 / 150

P95 latency27.8s

Qwen R3

Research signal

Perfect on parsed rows, but parse reliability blocks production use.

Parsed accuracy100%

Unsafe approval0%

Approval precision100%

Parse failures7 / 150

P95 latency34.4s

Parsed accuracy means accuracy after excluding malformed model outputs. Parse failures still count as product failures, which is why Llama beats Qwen for production despite Qwen's perfect parsed rows.