Observability & Evaluation · Software component
Metrics Dashboard
Software componentObservability & EvaluationObservability & EvaluationVariation point (abstract)arc:MetricsDashboard
A visualization component that queries stored metric time series and renders graphs, heatmaps and gauges organised around operational questions.
Responsibility. Presents combined metrics as operational views of system health, bottlenecks and capacity.
Also known as: Observability dashboard, Monitoring dashboard, Grafana dashboard, Operational dashboard, GPU dashboard, Data quality dashboard, Live dashboard, Drift detection dashboard, Impact dashboard, Aggregate performance report, Fairness dashboard
Variants
| Variant | When to choose |
|---|---|
| Adoption Dashboard | — |
| Agent Performance Dashboard | Choose when managers must review multi-agent collaboration effectiveness and escalation patterns across thousands of conversations. |
| Compliance Dashboard | — |
| Cost Reporting Dashboard | Choose when the audience is budget owners or executives who need trend, model-mix, unit-economics and budget-versus-actual views rather than operational health panels. |
| Risk Dashboard | — |
Relationships
deployed on structural
invokes dependency
is invoked by dependency
reads dependency
receives data from dynamic
receives telemetry from dynamic
Design guidance
- SHOULD organise dashboards around operational questions (SLA attainment, error concentration, capacity, cost trend) rather than technical components.
- SHOULD include LLM-specific views: latency, token efficiency, cost, vector query time, GPU utilization and MCP health.
- SHOULD correlate replica count with request rate, utilization with targets, and P95 latency with SLOs to validate autoscaling.
- SHOULD show request rate, P50/P95/P99 latency, per-GPU utilization, error rate vs SLA line, tokens/s, CPU/memory/network and cost panels.
- SHOULD provide audience-specific executive, operations and optimization dashboards.
- SHOULD correlate GPU hardware metrics with application-level latency on the same view to separate GPU saturation from other causes.
- SHOULD place safety and infrastructure panels separately, each with thresholds matching its own SLO.
Quantitative guidance
As stated by the sources; verify before use.
- Typical production NIM dashboard has 7 panels (Ch7.2).
- Infrastructure panel thresholds 0.1% green / 0.2% yellow (2x burn) / 0.5% red (5x burn); safety panel 1% green / 2% yellow / 3% red (Ch8.2B).
- Klarna tracks 40+ performance metrics in real-time dashboards (Ch10.1).
Classification
- Patterns
- Question-oriented dashboardsTask lifecycle dashboardAgent dependency graphPre/post-deployment comparisonCorrelated replica-count / request-rate / utilization / P95 panels
- Technologies
- GrafanaDatadogNVIDIA GPU Dashboard (Grafana ID 12239)LangSmithDataDogNew Relic
- Quality attributes
- Maintainability (ISO/IEC 25010)
- Risks mitigated
- Slow incident root-cause analysisUndetected scaling oscillationMis-tuned scaling thresholds
Sources
- 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.
- 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.
- 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.
- Ch4.7: T. Nguyen, "Scaling Strategies," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.7. ISBN: 9798244538229.
- 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.
- 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.
- 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.
- 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.
- Ch7.3: T. Nguyen, "NeMo Agent Toolkit Profiling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.3. ISBN: 9798244538229.
- Ch8.1: T. Nguyen, "Latency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.1. ISBN: 9798244538229.
- 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.
- 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.
- Ch8.3: T. Nguyen, "Token Economics and Architecture," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.3. ISBN: 9798244538229.
- Ch9.2: T. Nguyen, "Action Constraints and Permission Models," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.2. ISBN: 9798244538229.
- 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.
- Ch10.1: T. Nguyen, "Conversational UI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.1. ISBN: 9798244538229.
- Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
- Ref4.05: NVIDIA, "Setting up Prometheus," NVIDIA GPU Telemetry Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/datacenter/cloud-native/gpu-telemetry/latest/kube-prometheus.html
- Ref7.15: "Advanced Agentic AI Optimization Techniques," unpublished reference note (15-Advanced-Agentic-Optimization.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- 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
- 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
- Ref8.01: LangChain, "LangSmith observability: AI agent observability platform," LangChain. Accessed: Sep. 27, 2026. [Online]. Available: https://www.langchain.com/langsmith/observability
- Ref8.04: "Data Quality and Drift Detection for Agent Systems," unpublished reference note (04-Data-Quality-Drift-Detection.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref8.07: "Agent Health Checks and Diagnostics," unpublished reference note (07-Agent-Health-Checks-Diagnostics.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref8.09: "Chapter 8 Summary: Run, Monitor, and Maintain," unpublished reference note (09-Chapter-8-Summary.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref9.06: "Auditing and Compliance Monitoring for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/06-Auditing-Compliance-Monitoring.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- Ref9.07: "Risk Assessment and Management for AI Systems," unpublished reference note (references/Chapter 9 - Safety, Ethics, and Compliance/07-Risk-Assessment-Management.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
- 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
- Ref9.10: "Chapter 9 Summary: Safety, Ethics, and Compliance," unpublished reference note (10-Chapter-9-Summary.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note