observability
7 free lessons tagged observability across AI, Programming. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Memory, Identity, and Seeing What the Agent Did
Three services decide whether an agent survives contact with production: what it remembers between sessions, whose authority it acts with when it calls your systems, and whether you can reconstruct what it did after the fact. This lesson covers AgentCore Memory, Identity and Observability, and the delegation problem that makes agent authentication genuinely different.
SLOs and Error Budgets: Turning Reliability Into a Number
How reliable should the service be? Wrong question: the right one is how much unreliability you can afford, spent deliberately. This lesson builds the SLI, SLO and error budget machinery from Google's SRE practice, does the arithmetic of nines, explains burn-rate alerting, and shows why 100 percent is the wrong target.
Distributed Tracing and the Art of Throwing Data Away
Tracing every request through every service produces the most useful telemetry you have and more of it than anyone can afford. This lesson covers how context propagation actually stitches a trace together, the head versus tail sampling decision and why it determines which incidents you can debug, and the collector pipeline where all of it is enforced.
Percentiles and Cardinality: The Two Numbers That Run Your Bill
Two pieces of arithmetic decide whether your telemetry is useful and affordable. Percentiles, because averages hide exactly the users who are suffering, and you cannot average a p99. Cardinality, because metric cost is not per event but per label combination, and one careless label can multiply your bill by the size of your user base. This lesson does both mechanisms by hand.
Metrics, Logs, Traces: Three Signals, Three Cost Models
Observability is not a product you buy but a property your system has: can you explain a behaviour you did not predict? This lesson defines the three telemetry signals, what question each answers, why their costs grow along completely different axes, and why the difference between monitoring and observability is the difference between known and unknown failure modes.
Monitoring, Drift, and When to Retrain
A model that has stopped working returns answers with the same confidence as one that still does. This lesson covers the difference between data drift and concept drift, what to monitor when labels arrive months late, the triggers that should start a retraining run, and the cases where retraining is the wrong response.
LLM Observability with OpenTelemetry: GenAI Semantic Conventions
Master the OTel GenAI semantic conventions — gen_ai.* attributes, span structure for prompts/completions/tools, sampling strategies, and cost attribution — and understand why standardizing across LangSmith, Phoenix, Datadog, and Grafana matters for production AI systems.