AKS Structured Input Modes
Use this reference when the troubleshooting request already contains structured inputs.
Detector-backed Mode
Use when AKS-aware detectors or AppLens-style insights are available.
Decision rules:
- Ignore findings where the detector is
emergingIssues. - Prefer critical findings over warnings.
- Prefer findings with more concrete remediation detail when choosing the likely root problem.
- Preserve per-insight output: problem summary, root-problem flag, affected resources, suggested commands.
Warning Events Mode
Use when the request includes Kubernetes warning events.
Expected output:
- summary of the events and their impact
- likely cause or causes
- next kubectl checks
- monitoring follow-up
Metrics Scan Mode
Use when the request includes CPU or memory time-series data.
Expected output:
- healthy or unhealthy status
- anomaly timestamps and explanations
- suggestion tied to the observed metric pressure
Generic Symptoms Mode
Use when the request includes resource symptoms but not detector results, warning events, or time-series metrics.
Expected output:
- symptom summary by resource
- likely failure domain
- next evidence-collection steps
Learn Grounding Fallback
If the first troubleshooting pass is incomplete, search Microsoft Learn using:
- the user prompt
- the parsed problem names
- the AKS troubleshooting context
Use Learn grounding to refine or validate the root-cause hypothesis, not to replace observed evidence.