Structured Input Modes

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.