Executive Summary
AI operations starts before launch. Teams need service telemetry and AI behavior telemetry: latency, errors, traces, token usage, tool-call success, groundedness, relevance, safety, user corrections, and cost per completed task. Microsoft Foundry guidance describes evaluation, monitoring, and tracing as core observability capabilities for generative AI. OpenTelemetry defines the broader instrumentation model through traces, metrics, and logs.
Measure Behavior, Not Just Uptime
Traditional health checks can say the API is up while the product is quietly giving weak answers. AI systems need a scorecard that joins infrastructure signals with quality signals. A useful production dashboard should show request volume, latency, error rate, model/version, retrieval source, tool path, refusal rate, quality evaluator scores, safety events, and spend.
- Use service-level objectives for availability and response time.
- Use evaluation scorecards for answer quality, groundedness, and safety.
- Connect traces to prompt version, model version, retrieval index, tool call, and deployment build.
- Sample production traffic for continuous evaluation where privacy and policy allow it.
Release Gates for AI Change
Prompts, retrieval indexes, model settings, guardrails, and tool schemas are production artifacts. They need review, testing, rollout, rollback, and ownership. Azure Well-Architected operational excellence guidance emphasizes standardized practices, automated pipelines, quality gates, monitoring, incident management, and safe deployment. Those same practices apply directly to AI assets.
Runbooks and Incident Loops
Runbooks should identify the symptom, business impact, dashboard, likely causes, first safe mitigation, rollback, owner, escalation path, and post-incident capture. For AI systems, add instructions for disabling a tool, lowering autonomy, switching model versions, rolling back retrieval data, or forcing human review for high-risk tasks.
Confidence
Confidence score: 95/100. The operating model is grounded in Microsoft Foundry observability guidance, Azure Well-Architected operational excellence, OpenTelemetry observability concepts, and established SRE practices around monitoring, incidents, and postmortems.