Observability & Evaluation · Software component

Incident Manager

Software componentObservability & EvaluationObservability & Evaluationarc:IncidentManager

An operations component that opens an incident from an automatically detected alert, notifies the on-call engineer and escalates through a timed chain of responders until resolution.

Responsibility. Drives alert-to-incident creation, paging and timed escalation.

Also known as: Incident response workflow, On-call paging, SLO breach response, Fairness incident response, Incident response process, Incident triage

Direct neighbourhood (hover for relationship types)

Relationships

is configured by structural

invokes dependency

Feature Flag Service Ch9.8

reads dependency

writes dependency

Post-Incident Report Ref8.06 Ref9.08

escalates to dynamic

is triggered by dynamic

sends data to dynamic

Transparency Report Ch9.8

triggers dynamic

Personal Data Breach Notifier Ch9.7

Design guidance

SHOULD escalate unresolved incidents at fixed time intervals to progressively senior owners.
SHOULD produce a post-incident review covering what happened, why, detection, fix and prevention.
SHOULD combine tracing (which component is slow) with GPU-level metrics (why) to reduce mean time to recovery (MTTR).
SHOULD run an incident retrospective after an SLO breach.
SHOULD follow phased response: detection and triage (0-5 min), investigation (5-30 min), resolution (30 min-2 h+), monitoring and recovery (2 h+), post-incident review.
SHOULD prefer quick mitigations (scale up, shed traffic, disable feature, route to fallback or smaller model, revert) when a permanent fix is not immediately available.
MUST classify each incident as CRITICAL, HIGH, MEDIUM or LOW at triage by assessing harm, scope, regulatory, legal and reputational impact.
SHOULD prioritise containment (disable component, reduce access, revert changes, add safeguards) before remediation.
SHOULD preserve evidence (logs, system state, artifacts) and document every response action.
SHOULD release incident fixes through expert review, testing, staging validation and monitored production deployment, then verify resolution.
MUST notify regulators and affected individuals when required by law.

Quantitative guidance

As stated by the sources; verify before use.

Escalate if unresolved after 30 min, to team lead at 60 min, to service owner at 90 min (Ref7.16).
Detection and triage phase: 0-15 minutes; investigation: hours; remediation: days (Ref9.08).
Remediation timeline: design and review Day 1, testing and staging Day 2, production Day 3, verification Days 3-4, monitoring Days 4-14 (Ref9.08).
Incident response time target <1 hour (Ref9.10).

Classification

Patterns: Timed escalation ladderPost-incident reviewSeverity-based incident triageDetect-investigate-contain-remediate-report-review lifecycle
Technologies: PagerDutyOpsGenie
Quality attributes: Reliability (ISO/IEC 25010 | NIST AI RMF: valid and reliable)
Risks mitigated: Unresolved production incidentsHarmful outputPrivacy breachRegulatory violationBias or discrimination incident
Frameworks & regulations: NIST AI RMF: MANAGEISO/IEC 42001 Annex A: incident management controlsGDPR: 72-hour breach notification

Sources

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.
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.
Ch9.7: T. Nguyen, "GDPR and Data Protection Regulations," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.7. ISBN: 9798244538229.
Ch9.8: T. Nguyen, "Standards and Frameworks for AI Governance," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.8. ISBN: 9798244538229.
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.
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.
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
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
Ref8.06: "Error Troubleshooting and Incident Response for Agent Systems," unpublished reference note (06-Error-Troubleshooting-Incident-Response.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
Ref9.02: "Responsible AI and Ethical Principles," unpublished reference note (02-Responsible-AI-Ethical-Principles.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
Ref9.08: "Safety Incident Response for AI Systems," unpublished reference note (08-Safety-Incident-Response.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