Architecture Lens

AI-enabled operations need bounded automation

AI can reduce operational toil by summarizing incidents, enriching alerts, finding runbook steps, and drafting communications. The architecture still needs to separate recommendations, human-approved actions, and fully automated remediation.

  • Classify use cases by recommendation, assisted action, or automated action.
  • Ground AI responses in approved runbooks, service catalogs, incident history, and ownership data.
  • Measure triage time, escalation quality, runbook coverage, avoided manual work, and repeat incidents.
AI-assisted operations loop
Signal

Alert, log, incident, ticket, or customer report.

Context

Runbooks, service map, ownership, and recent changes.

Assist

Summary, recommendation, draft response, or next step.

Control

Approval, action, audit trail, and learning update.

Original InSkyto diagram informed by Azure Well-Architected operational excellence concepts.

References

Microsoft Azure Well-Architected Framework

Delivery Pattern

Improve knowledge before adding automation

AI assistants perform poorly when runbooks, ownership records, service catalogs, and incident notes are stale. Knowledge quality is part of the platform, not documentation cleanup.

Operating Model

Keep accountability human-readable

For production changes, teams need to know who approved the action, what context was used, what the AI recommended, what was executed, and how the result was verified.