Observability & Evaluation · Data artifact

Alert Rule Set

Data artifactObservability & EvaluationObservability & Evaluationarc:AlertRuleSet

Relationships

Design guidance

• SHOULD alert on sustained threshold violations rather than transient spikes and include current value, threshold and duration in the message.
• SHOULD align thresholds with SLAs and require sustained conditions (2-10 minutes) to avoid false positives from transients.
• SHOULD annotate alerts with current value, threshold and affected components for rapid triage.
• MAY add informational alerts for model reloads, scaling, deployment and config-change events.
• SHOULD define per-partition-profile memory thresholds when mixed partition layouts are used.
• SHOULD define severity tiers by burn rate with longer sustain windows for lower severities.
• SHOULD NOT alert on every minor deviation from baseline; calibrate thresholds to separate actionable alerts from informational notices.

Quantitative guidance

As stated by the sources; verify before use.

• Example rule: agent p95 latency > 500 ms for 5 minutes (Ch4.1).
• Example rules: agent error rate > 5%, memory approaching limits, rising pod restart counts (Ch4.3).
• Example alerts: P95 latency >500ms for 2 min, error rate >1% for 5 min, GPU utilisation <30% (Ch4.5).
• Edge example: P95 latency >150ms alert; GPU utilisation warning 85% / critical 95%; failed health check restart up to 3 times; cloud disconnection >10 min alert (Ch4.6).
• Vector store alert thresholds: p95 latency >200 ms, ingestion failures >1%, disk >80%, memory >90% (Ch6.2B).
• ETL alert thresholds: zero documents extracted for 3 consecutive runs; rejection rate above 50%; duration above 2x historical average; any phase failing 3 times in 24 hours (Ch6.3B).
• Example alert rules: P95 > 2 s, error rate > 0.1%, retrieval recall < 90%, daily cost over budget (Ch6.5).
• High latency: P95 >2s for 5m (warning); error rate >5% for 2m (critical); GPU >95% for 10m (warning); pod restarts over 15m sustained 5m (critical) (Ch7.2).
• Warning examples: GPU >80% for 5 minutes, memory fragmentation >30%, cost per request doubling (Ref7.16).
• Alert if latency increases by more than 10% as the system scales; target scaling efficiency above 85% (Ref7.17).
• Example rules: error rate >0.05 over a 5-minute window at high severity; anomaly detection on cost per request at medium severity (Ref8.01).
• Critical: burn >= 10.0 sustained 5 min -> page on-call, escalate, halt deployments; Warning: 2.0-9.9 sustained 1 h -> ticket, fix within 24 h; Info: 1.5-1.9 sustained 6 h -> log for weekly review; <1.5 no alert (Ch8.2A).
• Infrastructure: error ratio > 0.002 (2x burn of 0.1% budget) for 10 m -> critical, operations team; Safety: violation ratio > 0.03 (3x 1% baseline) for 15 m -> warning, security team (Ch8.2B).
• Example rules: safety_violations > 5 in last 24h -> CRITICAL; demographic_parity < 0.75 for any group -> HIGH (Ref9.09).

Classification

Patterns

Sustained-threshold alertingMulti-window, multi-burn-rate alerting

Technologies

Grafana AlertingPrometheus alert rulesGrafana alertingPrometheus alerting rules

Quality attributes

Maintainability (ISO/IEC 25010)

Risks mitigated

Alert fatigue from transient spikes

Sources

1. Ch4.1: T. Nguyen, "Introduction to AI Agent Deployment and Scaling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.1. ISBN: 9798244538229.
2. Ch4.3: T. Nguyen, "Container Orchestration and Edge Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.3. ISBN: 9798244538229.
3. Ch4.5: T. Nguyen, "NVIDIA NIM and Triton Inference Server," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.5. ISBN: 9798244538229.
4. Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
5. Ch6.2B: T. Nguyen, "Production Vector Database Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.2B. ISBN: 9798244538229.
6. Ch6.3B: T. Nguyen, "ETL Worked Example - Load Phase & Pipeline Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.3B. ISBN: 9798244538229.
7. Ch6.4: T. Nguyen, "Data Quality Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.4. ISBN: 9798244538229.
8. Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
9. Ch7.2: T. Nguyen, "Performance Optimization and Production Monitoring," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.2. ISBN: 9798244538229.
10. Ch7.6: T. Nguyen, "Multi-Instance GPU," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.6. ISBN: 9798244538229.
11. Ch8.2A: T. Nguyen, "Error Rates and Reliability," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.2A. ISBN: 9798244538229.
12. Ch8.2B: T. Nguyen, "NeMo Guardrails Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.2B. ISBN: 9798244538229.
13. Ch9.4: T. Nguyen, "Fairness and Bias Mitigation," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.4. ISBN: 9798244538229.
14. Ch10.2: T. Nguyen, "Proactive Agents," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.2. ISBN: 9798244538229.
15. Ch10.4: T. Nguyen, "Human-in-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.4. ISBN: 9798244538229.
16. Ref7.16: "Production Monitoring and Operations for Agentic AI," unpublished reference note (16-Production-Monitoring-Operations.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
17. Ref7.17: "Scaling Agentic AI Systems: Patterns and Strategies," unpublished reference note (17-Scalability-Patterns.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
18. Ref7.18: "Chapter 7 Summary: NVIDIA Platform Implementation," unpublished reference note (18-Chapter-7-Summary.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
19. Ref8.01: LangChain, "LangSmith observability: AI agent observability platform," LangChain. Accessed: Sep. 27, 2026. [Online]. Available: https://www.langchain.com/langsmith/observability
20. Ref8.02: "Machine Learning Monitoring in Production," unpublished reference note (02-ML-Monitoring-Production.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
21. Ref8.03: "Agent Evaluation Frameworks and Metrics," unpublished reference note (03-Agent-Evaluation-Frameworks.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
22. Ref9.09: "Compliance Automation and Tools," unpublished reference note (09-Compliance-Automation-Tools.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note