AI Observability

Overview

Most AI observability stops at the model layer: token consumption, latency, content filters. That tells you the infrastructure is working. It doesn’t tell you whether the AI is helping anyone.

This five-part series builds observability across four layers, from model infrastructure through governance, then makes it operational with alerts and workbooks. The goal isn’t dashboards. The goal is defensible decisions about AI capabilities, grounded in evidence, with audit trails that prove you’re governing responsibly.

The Core Problem

When someone says “the AI is hallucinating,” the problem usually lives in the grounding layer, not the model. The retrieval pipeline returned irrelevant chunks, or no chunks at all, and the model did its best with garbage input. That failure mode is invisible in model-layer metrics.

Similarly, a conversation that completed without errors might have left the user frustrated. A tool call that succeeded might have been unnecessary. The gap between “it worked” and “it helped” requires different instrumentation.

What You’ll Learn

Part 1: The Model Layer Azure OpenAI monitoring: token consumption, latency baselines, content filter analytics, budget enforcement. The foundation that tells you whether the infrastructure is healthy.

Part 2: The Grounding Layer Retrieval quality and RAG pipeline monitoring. Search latency, semantic scores, corpus freshness. Where hallucinations actually originate and how to see them.

Part 3: The Orchestration Layer Application-level quality measurement. Conversation completion rates, semantic similarity between queries and responses, tool execution patterns. Where value is measured.

Part 4: The Governance Layer Authority tracking, confidence thresholds, compliance evidence. The observability that proves you’re governing responsibly and provides audit trails for decisions.

Part 5: Making It Operational Alert rules that trigger on meaningful conditions. Workbooks for different audiences. Deployment guidance for standing up the infrastructure. The framework becomes operational.

Why This Matters

Confidence Engineering argues that confidence is empirical, built through evidence, and requires observable criteria. This series is the observability that makes confidence measurable.

Technical observability tells you what happened. Governance observability tells you whether what happened was acceptable, and whether you can prove it.

Who This Is For

Platform architects housing AI capabilities in their platform layer. Operations teams responsible for AI reliability. Governance professionals ensuring responsible AI use. Anyone building AI-enabled systems who needs to demonstrate that those systems are working as intended.

The Throughline

This series implements the instrumentation that Confidence Engineering requires. It builds on the monitoring patterns in Beyond Azure Monitor. It operationalizes the platform layer concepts from Platform Resiliency.

The goal was never dashboards. The goal was defensible decisions about AI capabilities, grounded in evidence.