AI, RAG & ML · Audit
Model Serving & Monitoring Audit
Audit model serving across artifact identity, preprocessing, routing, batching, concurrency, fallback, observability, and safe rollout.
Vibe Coding 1.3.0 · vibe-ai · English technical instructions
Use this workflow
Use this workflow in Codex or Claude Code with the free Vibe Coding plugin. Choose your assistant and prompt language, then add your task details after the prompt.
Operation
Inspect the named boundary and report supported findings. Do not edit product code. Include concrete evidence, impact, the owning source, one remediation direction and a meaningful validation route. Severity follows actual impact, not a category example.
Goal and scope
Audit model serving across artifact identity, preprocessing, routing, batching, concurrency, fallback, observability, and safe rollout.
Domain invariants
- Serving loads the intended model/preprocessing/schema/threshold versions atomically and reports their identity.
- Admission, batching, pools, concurrency, timeouts, cancellation, retry, and memory/GPU limits are bounded.
- Fallback or shadow routing preserves semantics, tenant/privacy controls, and truthful status; it never silently changes product policy.
- Latency, errors, saturation, input/output quality, drift, and cost are observable with privacy-safe dimensions and rollback controls.
Audit method
- Trace request routing through preprocessing, model selection, batch/queue, inference, postprocessing, persistence, and response.
- Check artifact/config skew, cold start, overload, partial batch, timeout, cancellation, provider outage, fallback, and mixed-version rollout.
- Inspect health/readiness semantics, autoscaling signals, cache identity, canary/shadow evaluation, alerts, and operator disable.
- Separate performance evidence from quality/drift evidence.
Priority model
- P0: unsafe action, privacy or tenant leak, materially wrong irreversible decision, corrupted model/data lineage, or critical service failure.
- P1: a reachable quality, grounding, evaluation, serving, cost, or governance defect with clear product impact.
- P2: a lower-risk but concrete robustness, observability, dataset, or maintainability issue.
Workflow ID: model-serving-monitoring-audit · View the versioned source · Shared workflow and authority rules