AI Infrastructure
What Is OpenTelemetry, and Why It Matters for Agent Observability
OpenTelemetry in plain terms: the data model, the GenAI semantic conventions, and why building agent tracing on OTel avoids lock-in.
6 min read
The infrastructure under AI systems: vector stores, serving, scaling, and cost.
9 articles
OpenTelemetry in plain terms: the data model, the GenAI semantic conventions, and why building agent tracing on OTel avoids lock-in.
What traces and spans actually are, how they map onto an agent run, and how to walk one to find the exact step that broke.
Git worktrees let you check out several branches into separate folders at once. They went from obscure to default because running coding agents in parallel needs isolated workspaces.
Why model deprecation quietly breaks production apps, and a concrete workflow: one models constant file, a scheduled GitHub Action checking deprecations.info plus a liveness probe, and an alert.
A practical guide to LLM observability: the traces and metrics to log, the handful of alerts worth waking up for, and why traditional monitoring misses what matters.
Prompt caching is solved. The bigger win for agents is caching tool-call results, and that is harder because tools have side effects. Here is how to do it safely.
If you can already ship software, the way into AI engineering is not another tutorial. It is building five systems that force you to handle failure, measure quality, and run things in production.
The field moves faster than any course can. So I learn production AI by gutting and rebuilding the open-source projects that production teams actually run. Here is the path.
A practical, opinionated comparison of the four vector databases people actually argue about, with a clear default and the signals that mean you have outgrown it.