# Observability
All Observability notes →5 — Continuous Profiling
What makes always-on, sampling-based profiling cheap enough to run in production continuously, why it earns that cost mainly for hot or expensive services, and how a profile correlates back to the one trace that was running during the sample.
4 — Observability-Driven Development
The TDD analogy taken seriously: SLOs and instrumentation defined at design time as acceptance criteria, not retrofitted after an incident — and why this only sticks as a launch gate, not a guideline.
1 — AIOps / Agentic RCA
What's actually new versus a static runbook — an investigation loop, not a fixed trigger-action mapping — why it depends on everything earlier in this book already being solid, and the read-vs-write safety line most real deployments draw.
2 — Root Cause Analysis
Covers automated root-cause analysis as an investigation loop over existing telemetry, not a fixed trigger-action mapping.
3 — Anomaly Detection
Covers statistical and ML-based anomaly detection on time series, and its false-positive tradeoff against static thresholds.
4 — Event Correlation
Covers correlating alerts, deploys, and changes across systems to collapse a flood of related signals into one incident.
5 — Predictive Alerting
Covers forecasting-based alerting that pages before a threshold breach, and the calibration risk that comes with prediction.
6 — LLM Assisted Troubleshooting
Covers using an LLM over existing telemetry for incident triage, and the hard boundary between read-only investigation and write-capable remediation.
7 — Autonomous Remediation
Covers safely scoping autonomous remediation actions, and why the read/write safety line matters more here than anywhere else in the stack.
# System Design
All System Design notes →Chapter 13 — Runbook Automation / AIOps Engine
LLM-powered diagnosis, trigger-action mappings, safety guardrails.
Chapter 14 — Observability Data Lake
Cold/warm/hot tiers, Parquet storage, query federation (Thanos/Cortex/Mimir).
Chapter 15 — Cost Optimization Pipeline
Adaptive sampling, metric drop rules, cardinality-aware ingestion.
Chapter 16 — Incident Management Platform
Alert correlation, incident lifecycle, escalation, runbook automation.